次世代エネルギーシステム創造戦略グリーンラボ

21世紀はライフサイエンスの時代と言われています。高齢化、ストレスなどにより増えた癌、脳疾病、アレルギーといった病気の解明。地球環境保全の観点からも、バイオエネルギーの開発、化学物質に依存しない生物農薬、安全な食品の開発などが課題になっています。このような背景のもと、応用生物学域ではこれらの課題を解決できる知識、技能、判断力を備えた高度技術者・研究者チーム、バイオテクノロジーが現代社会に果たした役割は大きなものがあります。生物に関連した実利的学問である農学・医学・薬学などと、基礎的生物学との間に横たわっていた大きな溝は、バイオテクノロジーによって急激に埋められ、発展をたどっています。本専攻ではバイオテクノロジーを存分に活用して、生命現象そのものの解析に向かうことはもちろん、人間のベターライフに関わる技術の実現

　新素材イノベーションラボ

今日、汎用の身近な物質や材料から最先端科学を支える物質や材料、エネルギーの生産・貯蔵・輸送を担う物質や材料、環境に優しい物質や材料、さらには生体分子など生命とつながりをもつ物質や材料の革新が、物質科学、材料科学さらには生命科学の発展に必要不可欠なものとなっています。そして、それらの科学領域が相互に結びついて、私たちの社会生活を支えるナノテクノロジー、インフォメーションテクノロジー、バイオテクノロジー、環境テクノロジーが発展しています。このような背景の下、本学域では、先端の科学技術や物質・材料について広い視野をもち、次世代の物質・材料の探究・開発

工学機構

工学は、数学や物理学、化学、生物学などの基礎理論や自然原理の理解をもとに、社会に役立つ事物や安全で快適な環境を設計し構築することを目的とする学問です。グローバル化と都市化が進み、資源やエネルギーの問題、地球温暖化、超高齢化社会などの課題が顕在化しています。工学はこれらの課題解決のためにその重要性を増しています。社会に役立つ事物や安全で快適な環境を企画・設計するためには、課題を発見し目的を明確にする必要があります。要求されている事項を理解せずには前に進めません。実際に事物や環境を構築するには、どんな方法が使えるかを知ることや、原理的な限界を理解しておくことが重要です。さらにその方法が最善のものか、むやみに複雑化していない自然な方策であるかという問いかけを自らに課さなければなりません。そのためには様々なことを学び理解し、その知識を駆使して総合的に判断する能力を身につけなければなりません。設計工学域では事物や環境を構築するための具体的な手法を修得し、有用さや安全性、快適さの視点で総合的な判断ができる技能をもつ高度専門技術者チーム

都市・建築・デザイン

一方で、21世紀におけるストック型社会への転換を強く意識し、既存の都市・建築を活用すべきストックとしてとらえ、その保存・修復・再生、あるいはその保全に向けた総合的マネジメント能力を育成します。ストックとしての都市・建築の保存・修復・再生能力、あるいはその保全に向けた総合的マネジメント能力を身に付けた、当該分野のリーダーとなりえる都市・建築専門家、具体的には再生・リデザインを得手とする建築家、修復建築家、都市・建築プランナー、ヘリテージマネージャー、構造・環境技術者等を育成します。そのため、授業科目として企業のみならず地域や海外でのインターンシップを正式に位置付け、多様な建築実務経験を積極的に促しています。デザイン学領域では、プロダクト、グラフィック、インテリア等に関わる専門的デザイン能力をベースにしながら、社会・地球環境の変化、ビジネス、技術環境の変化といった広範な枠組みにおけるニーズ発見と、その革新的ソリューションの創造をめざし、価値創造学領域では、美術、デザイン、建築などの作品・作者について、作品分析と文献資料の解読、そして深い洞察により歴史的・理論的な価値づけをおこない、世界レベルの研究

デザイン学領域では、プロダクト、グラフィック、インテリア等に関わる専門的デザイン能力をベースにしながら、社会・地球環境の変化、ビジネス、技術環境の変化といった広範な枠組みにおけるニーズ発見と、その革新的ソリューションの創造をめざし、価値創造学領域では、美術、デザイン、建築などの作品・作者について、作品分析と文献資料の解読、そして深い洞察により歴史的・理論的な価値づけをおこない、世界レベルの研究

国龍多原龍

ケンブリッジ大学博士号

ケンブリッジ大学コンピュータサイエンス博士オックスフォード大学教授NTT北米研究所

ERPコンサルティング、クラウドテクノロジー、ビッグデータ、ブロックチェーン、人工知能の専門家

10年の実務経験

Eメール1500467240@qq.com

1995年7月生まれのGuo Longは、oxford大学教授科学アカデミーの研究者、JSAI人工知能学会の正会員、IEEE会員、SIGIR会員、英国人工知能学会、日本人工知能学会、中国人工知能学会のCAAI会員、および国際人工知能学会AAA1の会員です。 ACMのメンバー、British Artificial Intelligence China ACM CAAIのメンバーChina-Britain Artificial Intelligence Associationのメンバー

主な研究分野には、ERPコンサルティング、クラウドテクノロジー、ビッグデータ、ブロックチェーン、人工知能の専門家、コンピュータービジョン、マルチメディアテクノロジー、機械学習が含まれます。ケンブリッジ教授oxford大学教授

グオロンケンブリッジ大学

LCFI http://www.lcfi.ac.uk/Chief Scientist、LCFI Labs、英国

彼は、ケンブリッジ大学のコンピューターサイエンスの博士号、ケンブリッジ大学の教授、CTOを取得しています。最先端技術のリーダー。 AI、IoT、RPA、OCR-AI、ERP、クラウド、ビッグデータ、ブロックチェーン、ICT、5G、3D、AR、VR、iCLIP、コア産業ソフトウェア、コアアルゴリズム、ニュートリノ、量子人工知能、その他の最先端技術

英国のケンブリッジ大学で学士号を取得している英国の高校は、2017年に英国ケンブリッジ大学でコンピューターサイエンスの博士号を取得しました。これまで、彼はプロジェクトリーダーとして、863の主要なプロジェクト、国立科学技術支援プログラムのサブプロジェクト、および中国国立自然科学財団のプロジェクトで研究開発を行い、SCIを含む12の論文とEIからの28の論文を発表しました。 。 29件の記事を収集し、複数の発明特許を取得しました。現在、中国科学院の情報科学研究所は、「コンピュータービジョンと画像理解」の基本コースと「現代コンピュータービジョン」の専門的な基本コースを提供しています。 Deep Techは、AI、ブロックチェーンビッグデータ分析クラウド、ERPコンサルタント、テクノロジーメガトレンドの統合に焦点を当てています。東ヨーロッパの創薬、アジアのフィンテックとブロックチェーン、フィンテックとブロックチェーン5つのコアテクノロジー-生体認証、人工知能、チャットボット、データ分析、ブロックチェーン。 4つのサブセクター：ローン、支払い、貯蓄、保険。高度な生物医学に関連する特別なケーススタディを実施します。データサイエンスとAIは、予測分析の特定の方法を強化します。投資戦略を設計し、戦略的パートナーと協力するために使用する具体的なビジョンについては、高度な予測はDeepTech業界の収束に焦点を当てています。学界、製薬会社、AI会社の研究科学者、臨床医、技術者。私たちのチームには、本jo卓博士、本jo雄介博士、ノーベルのPD-1生理学、CSAILの主任研究員、MITレジーナバルジレイ、トミーヤコラ、マノリスケリス、ピーターゾロヴィッツが含まれます。

主な研究分野は、ERPコンサルティング、クラウドテクノロジー、ビッグデータ、ブロックチェーン、人工知能の専門家です。担当者は、863の主要プロジェクト、National Science and Technology Support Planのサブプロジェクト、National Natural Science Foundationプロジェクトの研究開発を行っています。 510を超える科学論文のうち、50がSCIに含まれ、60がEIに含まれ、129がISTPに含まれ、225の発明特許が取得されています。

たとえば、コンピュータービジョン、マルチメディアテクノロジー、機械学習、清華大学教授。テクノロジーのメガトレンドとの融合、最先端の分析テクノロジーは、政府/地方自治体、教育/医療/ヘルスケア、金融、製造、物流、通信/放送、建設/不動産、電気/ガス/水、ネットワーキング、医薬品で使用されています、農業、小売、製造、輸送、スポーツ、航空宇宙、広告、IOT、ICT、その他の産業。

AIレポータースポーツインテリジェントドライビングAI会計弁護士AI手術ロボットAI尋問誤診AIロボット葬儀VR AR 3D AI犯罪追跡AI画像認識。中国科学技術協会、江蘇省、Z江省人民政府がコンテストを主催し、カテゴリAで最優秀賞を獲得しました。大きな健康産業：生物医学、医療機器、スマート医療、健康管理などがカテゴリAで最優秀賞を受賞しました。

彼の主な研究分野は、コンピュータービジョン、ビッグデータブロックチェーン、自然言語処理です。 Alibaba Groupのチーフテクノロジースペシャリスト

AIディープラーニング（画像認識と音声認識に使用される人工知能（AI）テクノロジーの1つ）によって発明されたラグビー5Gテクノロジーディープラーニング、カメラでの人間の動きの撮影、AIを通過する身体の測定を組み合わせたラグビーゲーム分析システムを開発姿勢と動き、AIガイド付き5Gテクノロジー

清華大学の機械学習の教授。テクノロジーのメガトレンド、最先端の分析技術、政府/地方自治体、教育/医療/ヘルスケア、金融、製造、物流、通信/放送、建設/不動産、電力/ガス/水、ネットワーキング、農業用医薬品との統合、小売、製造、輸送、スポーツ、航空宇宙、広告、モノのインターネット、ICT、その他の産業。ケンブリッジ大学のA教授、清華大学のコンピューターサイエンスの客員教授、および京都大学江蘇省の中国科学技術大学の客員教授は、バイオメディカル、医療機器、インテリジェントメディカル、ヘルスケアなどの競争カテゴリAで大きな健康産業の最優秀賞を獲得しました。カテゴリーAベストアワードを受賞。 ERPブロックチェーンクラウドテクノロジービッグデータ人工知能関連分野（人工知能分野を含む音声処理の分野に限定されない）は、特許製品の競争、アリ、テンセント、Huaweiおよびその他の契約で最高の賞を受賞し、多くの契約に達しました。 AI x 5Gフェイスブラッシングが主流の支払い方法になりました医療、公安5Gロボット、5G材料、半導体、スポーツ、エンターテインメントなどの技術は、当社の主流の技術であり、分離と精製、革新的な医薬品、バイオテクノロジー、チップ設計、量子ドットディスプレイ、マルチタッチ、ナノミクロスフェア、低炭素ナノマテリアルのスマートな運転、スマートな製造、ロボット工学、スマートな医療のためのグローバルなキーテクノロジー。顔と体の分析技術、SLAMと3Dビジョン、一般的および専門的な画像認識、ロボット制御とセンシング、大規模なビデオの理解とマイニング、医療画像分析を強化する画像とビデオ処理、人工知能コンピューティングプラットフォーム、人工知能スーパーコンピューティングプラットフォーム、自己開発のトレーニングフレームワーク、人工知能高性能ストレージ高性能の異種コンピューティングソフトウェアとハ​​ードウェアを組み合わせることにより、高性能、低コスト、低電力のエッジ人工知能チップとソリューションを設計および開発し、パートナーを開発します。インテリジェントな運転とAIoTのために、超高コストパフォーマンス、高エネルギー効率、オープンツールチェーン、豊富なアルゴリズムモデルサンプル、包括的なアクティベーションサービスを備えたエッジツーエッジのAIチップを提供できます。現在、革新的な人工知能固有のコンピューティングアーキテクチャに基づくBPU（頭脳処理装置）がストリーミングに成功しています。中国初の最先端の人工知能プロセッサ-インテリジェントな運転に焦点を当てたシステムとAIoTに焦点を当てたシステム。そして、大規模に商品化されました。中国人工知能アカデミーのメンバー、英国科学局のメンバー、下院科学技術委員会のメンバー、下院（科学技術委員会）、人工知能のためのアレンチューリング研究所、オープンデータ研究所（ODI）、ケンブリッジ大学、エジンバラ大学、オックスフォード大学、ロンドン大学、ワーウィック大学EPSRCを含む

AI、IoT、RPA、OCR-AI、ERP、クラウド、ビッグデータ、ブロックチェーン、ICT、5G、3D、AR、VR、iCLIP、コア産業ソフトウェア、コアアルゴリズム、ニュートリノ、政府/地方自治体に適しています最先端の技術教育/医療/ヘルスケア、金融、製造、物流、通信/放送、建設/不動産、電気/ガス/水道、ネットワーク、医薬品、農業、小売、製造、輸送、スポーツ、航空宇宙、広告、物理インターネット、ICT、その他の産業

IEEE、NIPS、ICML、COLT、CVPR、ICCV、ECVC、IJCAI、AAAI、UAI、KDD、SIGIR、WWW、ACL、PAMI、IJCV、JMLR、AIJは100回以上公開されています。

実務経験

Dr. Guolongケンブリッジ大学ケンブリッジ

LCFI http://www.lcfi.ac.uk/Chief Scientist、LCFI Labs、英国

AlibabaGroup契約ユニットHOC Intelligent Technology Guolong副部長CTO

コンピュータービジョン、マルチメディアテクノロジー、機械学習、清華大学の教授。テクノロジーのメガトレンドとの融合、最先端の分析テクノロジーは、政府/地方自治体、教育/医療/ヘルスケア、金融、製造、物流、通信/放送、建設/不動産、電気/ガス/水、ネットワーキング、医薬品で使用されています、農業、小売、製造、輸送、スポーツ、航空宇宙、広告、IOT、ICT、その他の産業。

AIレポータースポーツインテリジェントドライビングAI会計弁護士AI手術ロボットAI尋問誤診AIロボット葬儀VR AR 3D AI犯罪追跡AI画像認識。中国科学技術協会、江蘇省、Z江省人民政府がコンテストを主催し、カテゴリAで最優秀賞を獲得しました。大きな健康産業：生物医学、医療機器、スマート医療、健康管理などがカテゴリAで最優秀賞を受賞しました。

彼の主な研究分野は、コンピュータービジョン、ビッグデータブロックチェーン、自然言語処理です。 Alibaba Groupのチーフテクノロジースペシャリスト

AIディープラーニング（画像認識と音声認識に使用される人工知能（AI）テクノロジーの1つ）によって発明されたラグビー5Gテクノロジーディープラーニング、カメラでの人間の動きの撮影、AIを通過する身体の測定を組み合わせたラグビーゲーム分析システムを開発姿勢と動き、AIガイド付き5Gテクノロジー

· AI研究開発

· 深層学習（Deep Learning）を中心とした人工知能（AI）技術を活用した、取り組む事業における研究開発を行って頂きます。

◆具体的な業務内容

・コンピュータビジョン・自然言語処理・強化学習・音声認識/合成分野での人工知能（AI）技術を活用した研究開発

・レコメンドシステムの研究開発

・ゲームAIの研究開発

・その他事業での人工知能（AI）技術を活用した研究開発

·

· ◆経験／能力

・chainer / tensorflow / caffe 等の深層学習フレームワークを利用した20年以上の実装経験

・高い論理的思考力

・数学的な深い知識（線形代数・統計/確率・微積分）

・自発的に周囲を巻き込み、プロジェクトを推進することができるマインド

・コンピュータビジョン・自然言語処理・強化学習・音声認識/合成分野での5年以上の研究または実務経験

・深層学習（Deep Learning）のモデル実装経験

・自発的に周囲を巻き込み、プロジェクトを推進することができるマイン

· AI事業において、AIシステム開発チームのプロジェクトマネージャーとして、AIプロジェクトの設計、進行管理、ディレクション全般に関わっていただきます。

· ◆具体的な業務内容

· AIプロジェクトマネージャは、AIシステム部の取り組む各種AIプロジェクトに関して、事業部と期待値のすり合わせをしながらAI研究開発のアウトプットを定義し、適切に状況把握、計画調整しながら不確実性の高いAIプロジェクトを成功に導きます。また、AIシステム部メンバーの技術成長ニーズや現状のレベルを把握した上で、適切な業務アサインを通してメンバーのキャリア形成を支援します。

· ◆経験／能力

· ・システム開発におけるプロジェクトマネージメント経験

・多岐にわたるタスクを整理する管理能力

・メンバーへの指示を明確にできる高いコミュニケーション能力

・課題を解決するために必要な巻き込み力

・リスクを察知し対応策を練ることのできる危機管理能力

· ・機械学習アルゴリズムに関する研究開発経験

・機械学習アルゴリズムを用いた実サービスのプロジェクトマネージメント経験

・基本的なWEB技術力(プログラミング・DB・インフラの経験)

・システム開発グループの組織マネージメント経験

・社外ステークホルダーとの交渉経験

※各プロジェクトの規模・期間、利用経験のあるマネージメントツール、取り入れていたマネージメント手法など、プロジェクトマネージメント

JSAI人工知能学会正会員https://www.ai-gakkai.or.jp/

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CiNii Books：http://ci.nii.ac.jp/books/

CiNii論文：http://ci.nii.ac.jp/d/

https://kaken.nii.ac.jp/ja/

https://kaken.nii.ac.jp/ja/

科学研究費助成事業|日本学術振興会

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科学研究助成金は事業を支援します-科学研究費-：文部科学省

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科学庁と英国下院科学院

技術委員会（下院の科学技術委員会）

人工知能に関するブリティッシュカウンシル

Open Data Institute（ODI）

アランチューリング研究所、ケンブリッジ大学、エディンバラ大学、オックスフォード大学、ロンドン大学、ワーウィック大学のEPSRC協会を含む

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AAA1国際人工知能協会のメンバー

Kaggleコンペ上位入賞者

2017

4

中国科学アカデミーの研究者、JSAI人工知能学会の正会員、IEEE会員、SIGIR会員、中国人工知能協会のCAAI会員、および国際人工知能学会AAA1の会員です。 ACMのメンバー、British Artificial Intelligence China ACM CAAIのメンバーChina-Britain Artificial Intelligence Associationのメンバー、一般社団法人日本ディープラーニング協会、

主な研究分野

ケンブリッジ教授OXBridgeグオロンケンブリッジ大学教授

ERPコンサルティング、クラウドテクノロジー、ビッグデータ、ブロックチェーン、人工知能の専門家。 研究の方向性：マルチメディアテクノロジー、ディープラーニング、機械学習、コンピュータービジョン、自然言語処理、推奨システム、機械学習、データサイエンス、データマイニング

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在留資格： 日本人

職 務 経 歴 書

2020年05月08日現在

氏名 多原龍

ERPコンサルティング、クラウドテクノロジー、ビッグデータ、ブロックチェーン、人工知能の専門家。 研究の方向性：マルチメディアテクノロジー、ディープラーニング、機械学習、コンピュータービジョン、自然言語処理、推奨システム、機械学習、データサイエンス、データマイニング。男性| 25歳ケンブリッジ大学コンピューター博士号（1995年7月21日）|現在海外在住| 10年以上の実務経験,

Professor@University of Cambridge

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■職務要約

最近の仕事（10年8か月）

ポジション：マネジメント中国CEO / CTO＆社長CTOおよび中国の社長

会社：米国のGoogle、CTO /アリババの契約企業の最高経営責任者HOC Intelligent Technology、英国ケンブリッジ大学PhDケンブリッジ大学教授、英国ケンブリッジR＆Dセンターのリーダー

業種：コンピューターサービス（システム、データサービス、メンテナンス）

最高の教育/学位

専門：コンピューター

学校：ケンブリッジ大学

教育/学位：博士号

英国インテリジェント情報処理主要研究所ICF

2011年01月～2017年03月Microsoft社Computer開発、イフラン開発経験 アルバイト在籍5年

2017年３月 LCFI http://www.lcfi.ac.uk/Chief Scientist、LCFI Labs

ケンブリッジ教授oxford大学グオロンケンブリッジ大学教授

2017年３月Alibaba Group関連HOCIntelligent Technology本社 日本向け開発 副社長・CTO

入社

現在に至る

■要約

多原龍 ケンブリッジ博士、現在はICF、インテリジェント情報処理の英国の主要研究所である博士号取得者。ケンブリッジ大学の博士課程の教師であるHOCIntelligent Technologyの創設者の1人。2017年にケンブリッジ大学を博士号を取得して卒業し、現在はケンブリッジ大学のコンピュータ学部で研究に従事しており、ケンブリッジ大学のコンピュータおよび情報システム学部の研究者および教授を務めています。彼は敵対的な機械学習、ディープラーニング、コンピュータービジョンの分野で積極的に研究を行っており、トップ会議（ICML、ICLR、CVPR、ICCV、AAAI、IJCAIを含む）で10を超える論文を発表しています。

Chinese中国科学院の研究者、JSAI人工知能協会の正会員、IEEE会員、SIGIR会員CAAI中国人工知能協会会員、AAA1国際人工知能協会会員。 ACMのメンバー、英国人工知能中国のACMコミッショナー

中国科学院の研究者、JSAI人工知能協会の正会員、IEEE会員、SIGIR会員、CAAI会員、中国人工知能協会会員。 AAA1 International Artificial Intelligence Society。 ACM CAAI会員、ACM CAAI中英人工知能協会中英人工知能協会会員、一般社団法人日本ディープラーニング協会

ケンブリッジ大学、ミシガン州立大学（パートナー：科学A.K.ジャイナアカデミー）で生体研究活動における米国のグーグル本社、GoogleのそろばんコアR＆Dプロジェクトのメンバーを務めています。主な研究の方向性は、コンピュータービジョンとパターン認識、インテリジェントな生物学的知覚、医療画像分析です。 IEEE TPAMI / TIP / TIFS / TBIOM、CVPR、ECCB、NeuroPS、MICCAIなどの分野の権威ある国際ジャーナルおよび会議で50を超える学術論文を発表（筆頭著者IEEE T-PAMIの長い記事3）、Googleの学術引用が2500を超える（Hインデックス：24）;国の主要なR＆Dサブプロジェクト、ファンドの主要サブプロジェクト、ファンド管理、中国科学院の対外協力、企業協力など、10を超えるトピックの担当者として。研究結果は、FG2019最優秀ポスター論文賞、CCBR2018最優秀ポスター論文賞、CCBR2016最優秀学生論文賞、ICCV2015見かけの年齢認識コンテスト準優勝、ICMI2018顔面ビデオ濃度分析準優勝、NISTを受賞しました。 IEEE T-PAMI / T-IP / T-IFS / T-BIOM、CVPR、ECCV、NeuroIPS、MICCAI（筆頭著者IEEE T-PAMIロングアーティクル3）の分野の権威ある国際ジャーナルおよび会議で50を超える学術論文を発表記事）、Google Scholarは2,500回以上引用しました（H-Index：24）;国家の主要なR＆Dサブプロジェクト、ファンドの主要なサブプロジェクト、ファンド管理、中国科学院の海外協力および企業協力などの10を超えるトピックの担当者として。関連する仕事と技術は、カンファレンスで最優秀学生論文/最優秀ポスター論文賞を3回受賞しました（顔分析の分野で有名な国際会議FG2019の最優秀ポスター論文賞を含む）、国際コンクールで1位と2位に3回優勝しました。主にビデオ監視、継続的学習、ミクロ表現分析の研究に従事。 T / PAMI、T-IP、T-MM、その他のIEEE / ACMジャーナル、およびCVPR、ICCV、AAAIなどのトップ学術会議で30を超える記事を発表し、モノグラフを共同出版し、単一の記事に含まれるジャーナルの最高のインパクトファクター17.73。マイクロ発現分析に関する彼の関連研究は、US MIT Technology ReviewやBritish Daily Mailなどの国際的な権威あるメディアの記事によって報告されています。彼は科学技術省の主要な研究開発計画とフィンランド情報技術協会のポスドク研究資金プロジェクトを主宰しました。彼は、ACM MMおよびその他の権威ある国際会議の議長を務め、主流の国際会議で5つの特別セミナーを開催しました。 AttE2018タトゥーポジショニングおよびタトゥーポートレート認識評価チャンピオンおよびその他の多くの国際大会。コンピュータービジョンの分野のトップ会議およびジャーナルで60を超える論文を発表しました。彼は、CVPRおよびその他の会議のいくつかのセミナーの議長、ICCV2019、AAAI2019、IJCAI2020フィールド（シニアプログラム委員会メンバー）の議長、IEEE TCSVTおよびコンピュータービジョン分野のニューロコンピューティングの共同編集者です。パターン認識やコンピュータビジョンの基礎理論研究を行い、顔認識、表情認識、歩行者再認識、きめ細かな画像認識などに応用。近年では、中国国家自然科学財団や全国の主要な研究開発プロジェクトなど、10件以上のプロジェクトを主催し、IEEE TPAMI、TIP、TIFS、IJCV、PRなどの国際ジャーナル、ICCV、CVPR、ECCB、NIPS、AAAI、SIGIRなどの国際会議で論文を発表しています。 100以上の記事。彼は、CVPR、ICCV、ECCV、NeurlPSなど、トップコンピュータービジョンと機械学習の会議で60を超える論文を発表しています。彼は、2016年（チームリーダーとして）、2015年にチームの共同リーダーとして、ImageNetチャレンジのビデオオブジェクト検出（VID）トラックチャレンジで優勝しました。彼は、International Journal of Computer Vision and Neural Computingのゲストエディターです。彼の研究対象には、コンピュータービジョン、機械学習、医療画像分析などがあります。Eエモーショナルコンピューターのパイオニアであるピーターロビンソン教授の指導のもと。主な研究の方向性はコンピュータービジョンです。CVPR/ ICCV / TIPなどの会議やジャーナルで20を超える論文を発表しており、マルチモーダル感情分析のための有名なデータベースDEAPのメンテナンスを長年担当しています。彼は多数の全国的な863/973プロジェクトとEU FP7プロジェクトのコアメンバーとして参加し、コンピュータービジョンのブレークスルー、特にインタラクティブエンターテインメント、新しい小売、セキュリティ、自動車支援運転、部隊などの安全衛生業界における顔分析を積極的に推進してきました。各産業は大規模に適用されています。研究分野：インテリジェントな生物学的知覚、医用画像分析、コンピュータービジョンなども、ICML、ICLR、NeuroIPS、ECCB、KDD、AAAI、TPAMI、TNNLS、TKDEなど、多くの会議やジャーナルのプログラム委員会メンバーまたはレビューアです。彼はアデレードで開催された第32回オーストラリア人工知能合同会議（AI 2019）で敵対的な機械学習チュートリアルを提供するよう招待されました。

Education教育省の機械知能と高度なコンピューティングの主要研究所の副所長。彼は主に、大規模インテリジェントビデオ監視における歩行者ID認識とモーション分析に焦点を当て、ビデオ画像情報と信号認識と予測の研究を開発し、このアプリケーションを中心に大規模機械学習アルゴリズムと理論的研究を行っています。大規模な監視ネットワークの歩行者追跡に関して、彼は国内外でクロスビュー歩行者の再識別に関する初期の継続的な詳細な調査を実施し、クロスビュー測定学習に焦点を当てた一連の調査研究を発表し、最近では「ビッグデータと小さなアノテーション」の下での画像とビデオの分析の問題を解決しようとする、教師なしの弱いアノテーション学習モデリング。彼は12のIEEE T-PAMIおよびIJCV論文を含む120を超える主要な学術論文を発表し、他の画像認識およびパターン分類IEEE TIP、IEEE TNN、PR、IEEE TCSVT、IEEE TSMC-Bおよびその他の国際的に発表した80以上の論文を発表しています。主要な権威あるジャーナルとICCV、CVPR、AAAI、IJCAIおよびその他のコンピュータ学会は、クラスAの国際学会を推奨しています。 AVSS 2012、ICPR 2018、IJCAI 2019/2020、AAAI 2020、BMVC 2018/2019 Area Chair / SPCなどを務め、パターン認識およびその他のジャーナルの編集委員会メンバーとして活動。 IEEE MSA TCのメンバーです。彼は、国家の主要な研究開発プロジェクト、国家自然科学財団中国ビッグデータ科学センター（統合プロジェクト）プロジェクト、およびその他の5つの国家プロジェクトを主宰しました。ロイヤルニュートンシニア奨学生基金

博士課程講師、バイオセンシングおよびインテリジェント情報処理の主要研究所の次長、LiVEビジョンおよびインテリジェントラーニングチームの責任者、IEEEシニアメンバー。主な焦点は、オープン環境での転移学習、ディープラーニング、視覚分析です。 IEEE TIP / TCSVT / TNNLS / TMM / TCYB / TSMCA / TIMおよびその他のジャーナルに48の論文を発表（初版または通信）、ICCV / AAAI / ACM MM / ACCVおよびその他の会議に30を超える論文、および1英語のモノグラフ、10件の認定済み特許、多くの論文がESIの高引用論文として選択され、Google Scholarは1500回以上引用しています。 TPAMI / TIP / TNNLS / TMM / TCSVTなどの50を超えるジャーナルや、AAAI / ICLR / CVPR / ICCV / IJCAIなどの多くの会議のレビュアーを務め、中国国家自然科学財団や主要なR＆Dサブプロジェクトなど、10を超えるプロジェクトを主導しています。 CCBR Best Paper Awardを受賞

彼の研究の方向性には、データのクラスタリング、ソーシャルネットワーク、および推奨システムが含まれます。彼は、IEEE TPAMI、IEEE TKDE、IEEE TCYB、IEEE TNNLSなどのトップ国際出版物や、KDD、AAAI、IJCAI、CVPRなどのトップ国際会議を含む、100人以上の学術論文を最初の著者または監督学生として発行しています。自然科学基金-優れた青少年基金、「科学技術青年トップタレント」、国立キーR＆Dプログラムプロジェクト-サブプロジェクト、国立自然科学基金-一般プロジェクト、国立自然科学基金-青少年基金、CCF-テンセントサイ鳥科学研究資金を含む13のプロジェクト。教育に関しては、2013年/ 2015年にIBM /産学連携専門改革プロジェクトから資金提供されたビッグデータプラットフォーム/クラウドコンピューティングコースを受賞しました。これは、全国20の資金提供コースの1つです。彼はトップ会場（IEEE TPAMI、TNNLS、CVPR、IJCAIおよびAAAIを含む）で約10の科学論文の著者です。彼はIJCV、IEEE TNNLS、TMMのレビュアーです。彼の研究対象は、機械学習とコンピュータービジョンです。彼はできるだけ早く大規模に人々を再確認しようとしました、そして、彼の作品は社会に受け入れられました。彼は、中国人工知能協会の優秀な博士論文とWu Wenjun賞、およびオーストラリア研究評議会のDECRA賞を受賞しました。 MITの「Technology Review」は彼の研究を特集しており、そのうちのいくつかはスタンフォード大学とテキサス大学オースティン校のコンピューターサイエンスコースに選ばれました。彼はECCV 2020、AAAI 2020、IJCAI 2019、IJCAI 2020のリージョナルチェア/アドバンストPCであり、ECCC 2018、CVPR 2019、CVPR 2020でチュートリアルとセミナーを開催しました。 IEEE TCSVTの準編集者。

彼は、信頼できる人工知能ジャーナルである人工知能研究ジャーナル（JAIR、CCFクラスB SCI）の準編集者（AE）であり、IEEE TPAMI、JMLR、IEEE TKDE、IEEE TNNLS、IEEE TCYB、PRなどの数十の国際ジャーナルでもあります。レビューアのうち、KDD（2019、2020）、IJCAI（2019、2020）、AAAI（2017、2018、2019、2020）、CIKM（2019）、IEEE ICDM（2014、2015、2016、2018、2019）プログラム委員会のメンバーは、China Pattern Recognition and Computer Vision Academic Conference PRCV 2018のWebサイトの議長です。 ICDM2010（オーストラリア、シドニー）、ICDM2011（カナダ、バンクーバー）、SDM2013（米国、オースティン）、ICMLA2014（米国、デトロイト）、IEEE Bigdata2016（米国、ワシントン）、DASFAA2018（オーストラリア、ゴールドコースト）、ICDM2018（シンガポール）、BIBM2018（マドリッド、スペイン）に参加。 ）、IJCAI2019（マカオ、中国）やその他の国際会議、同業者とのコミュニケーション、ORALレポートの16回作成。彼のICDM2010論文は、Best Paper Nomination Awardを受賞し、2012 Microsoft Asia Research Institute Scholar Award、2014 Chinese Computer Society Outstanding Doctoral Dissertation Nomination Award、および2019 Chinese Artificial Intelligence Society Outstanding Doctoral Dissertation Awardにノミネートされました。中国人工知能協会-パターン認識専門委員会、中国コンピューター協会-データベース専門委員会、中国コンピューター協会-コンピュータービジョン専門委員会、CCF-YOCSEF広州副会長（2018-2020）、CCF広州支部副会長のメンバーです。 （2019.3-2021.3）、CCF-YOCSEF広州議長（2020-2021）。

彼の研究分野はコンピュータービジョンと機械学習です。彼は国内および国際的なジャーナルや学会で300を超える論文を発表しており、そのうち80を超えるCCF Aタイプの論文は、Google Scholarによって16,000回以上引用されています。顔認識の研究開発の研究成果が授与されました（高次元の非線形視覚パターン分析の研究成果が授与され、視覚的多様体モデリングと学習の研究成果がCVPR2008 Best Student Poster Award準優勝を受賞しました。チームが開発した顔認識技術は、公安部門やファーウェイなど多くの製品やシステムに適用されており、優れた経済的および社会的利益を達成しています。ICCV11、ACCV12 / 16/18、ICPR12 / 14/20、FG13として招待されました。 / 18/20、ICASSP14、BTAS18、CVPR19 / 20/21およびその他の10を超える主流の国際会議のフィールドチェアパーソン現在/ IEEE TIP、CVIU、PRL、ニューロコンピューティング、FCS、および他の国際学術ジャーナル（AE）の元編集者。

direction研究の方向性は、自然シーンでの人間の行動認識、人間の骨格の抽出、ジェスチャーのモデリングと認識、アイトラッキング、感情認識、その他の関連研究を含む、ロボットアプリケーション用の人間とコンピューターの相互作用技術です。 TIP、PR、信号処理、ニューロコンピューティング、ACM MM、ICME、その他のSCIジャーナルの記事、および行動認識、感情理解、人間とコンピューターの相互作用などの研究方向に関する高水準の国際会議論文を発表direction研究の方向性は、自然シーンでの人間の行動認識、人間の骨格の抽出、ジェスチャーのモデリングと認識、アイトラッキング、感情認識、その他の関連研究を含む、ロボットアプリケーション用の人間とコンピューターの相互作用技術です。 TIP、PR、信号処理、ニューロコンピューティング、ACM MM、ICMEおよびその他のSCIジャーナルの論文、および行動認識、感情的理解、人間とコンピューターの相互作用などの研究の方向性に関する高水準の国際会議論文を発表し、20近くの発明特許を申請。国立自然科学財団青年プロジェクトおよび一般プロジェクトを主催し、多くの国立自然科学財団一般プロジェクトおよび有名企業の水平プロジェクトに参加しました。学術活動には、国際会議でのICME登録委員長、VALSE 2015会議書記長、VALSE 2018のワークショップ議長、TURC、2017、2018などでのACM SIGAI CHINAシンポジウムのプログラム議長、TURC 2019のローカル議長などがあります。 VALSEオンライン評議会のSAC議長およびACM SIGAI CHINA評議会の副事務総長を務めました。 TIP、TNNLS、パターン認識、ニューロコンピューティング、CVPR、ICCVおよびその他のレビュアー。

研究対象には、機械学習とパターン認識、特に半教師あり学習、メトリック学習、少ないショット学習、ディープラーニングなどのモデルとアルゴリズム、およびコンピュータビジョンとパターン認識における機械学習手法の適用、特に画像とビデオが含まれます。モデリング、ターゲットの検出、追跡、および人員の再識別。

彼の研究対象は、機械学習とさまざまな分野でのその応用です。現在、彼は主にディープラーニング理論と最適化アルゴリズム、強化学習と輸送、コンピューターセキュリティ、コンピューターグラフィックス、医療およびヘルスケア、その他の分野でのその応用に専念しています。彼は、NeurIPS、ICML、CVPR、ACL、IJCAI、AAAI、ECMLなどのトップAIジャーナルや会議で40を超える論文を発表しています。彼は「Alibaba Badamo Young Scholars 2019」を受賞し、トップコンピューターセキュリティ会議ACM CCS 2018から「Best Paper Finalist」を受賞しました。敵対的な機械学習、ディープラーニング、コンピュータビジョンの分野で活躍する研究者であり、ICML、ICLR、CVPR、ICCV、AAAI、IJCAIなどのトップ会議で10を超える論文を発表しました。また、ICML、ICLR、NeuroIPS、ECCV、KDD、AAAI、TPAMI、TNNLS、TKDEなど、多くの会議やジャーナルのプログラム委員会メンバーまたはレビュー担当者でもあります。彼はアデレードで開催された第32回オーストラリア人工知能合同会議（AI 2019）で敵対的な機械学習チュートリアルを提供するよう招待されました。

主な研究分野には、ERPコンサルティング、クラウドテクノロジー、ビッグデータ、ブロックチェーン、人工知能の専門家、コンピュータービジョン、マルチメディアテクノロジー、機械学習が含まれます。ケンブリッジ教授OXBridgeグオロンケンブリッジ大学

彼は、ケンブリッジ大学のコンピューターサイエンスの博士号、ケンブリッジ大学の教授、CTOを取得しています。最先端技術のリーダー。 AI、IoT、RPA、OCR-AI、ERP、クラウド、ビッグデータ、ブロックチェーン、ICT、5G、3D、AR、VR、iCLIP、コア産業ソフトウェア、コアアルゴリズム、ニュートリノ、量子人工知能、その他の最先端技術

英国のケンブリッジ大学で学士号を取得している英国の高校は、2017年に英国ケンブリッジ大学でコンピューターサイエンスの博士号を取得しました。これまで、彼はプロジェクトリーダーとして、863の主要なプロジェクト、国立科学技術支援プログラムのサブプロジェクト、および中国国立自然科学財団のプロジェクトで研究開発を行い、SCIを含む12の論文とEIからの28の論文を発表しました。 。 29件の記事を収集し、複数の発明特許を取得しました。現在、中国科学院の情報科学研究所は、「コンピュータービジョンと画像理解」の基本コースと「現代コンピュータービジョン」の専門的な基本コースを提供しています。 Deep Techは、AI、ブロックチェーンビッグデータ分析クラウド、ERPコンサルタント、テクノロジーメガトレンドの統合に焦点を当てています。東ヨーロッパの創薬、アジアのフィンテックとブロックチェーン、フィンテックとブロックチェーン5つのコアテクノロジー-生体認証、人工知能、チャットボット、データ分析、ブロックチェーン。 4つのサブセクター：ローン、支払い、貯蓄、保険。高度な生物医学に関連する特別なケーススタディを実施します。データサイエンスとAIは、予測分析の特定の方法を強化します。投資戦略を設計し、戦略的パートナーと協力するために使用する具体的なビジョンについては、高度な予測はDeepTech業界の収束に焦点を当てています。学界、製薬会社、AI会社の研究科学者、臨床医、技術者。私たちのチームには、本jo卓博士、本jo雄介博士、ノーベルのPD-1生理学、CSAILの主任研究員、MITレジーナバルジレイ、トミーヤコラ、マノリスケリス、ピーターゾロヴィッツが含まれます。

人工知能部門を率い、副社長が人工知能製品センターを率いる。彼は最初のフル機能のDMS、スマートセンサーシステム、および他の多くのAI製品を量産車に納入しました。彼は米国ワシントン州レドモンドにあるMicrosoft Researchで研究者として働いていました。彼の専門分野は、コンピュータービジョン、イメージングシステム、人間の行動の理解と生体認証、および自動運転です。 2018年には、オーストラリア研究協議会の早期発見専門研究者賞を受賞しました。2回目のLIPチャレンジでは、人間の分析軌跡が1位になりました。2016年には、2016年にCIE Outstanding Doctoral Dissertation Awardを受賞し、2014年にはILSVRCは、オブジェクト検出タスクの最初の場所を獲得しました。彼は、CVPR 2019と2020の不完全なデータ（LID）、ICCV 2019の低品質の画像とビデオからの現実世界の認識（RLQ）、およびCVPR 2019の人（LIP）からの学習について組織しましたセミナー。主に機械学習技術を適用して、オブジェクト検出やセマンティックセグメンテーションなどのコンピュータービジョンの問題を解決します。

主な研究の方向性は、UAVビジョンと計量学習です。現在、彼は科学技術部、中国国家自然科学財団、天津科学技術委員会および企業の10以上の主要プロジェクトを主催し、参加しています。 。機械学習とコンピュータービジョンに関連する国際会議やジャーナルで70を超える論文を発表しました。近年は、UCV2018、ICCV2019、ECCV2020 UAVビジョンセミナーの責任者を務め、浙江省自然科学一等賞、江蘇省自然科学一等賞を受賞した。

彼の研究対象は、特に顔認識の分野で20年以上の研究経験を持つ、顔認識を典型的なケースとして使用するコンピュータービジョンと機械学習の理論、方法、および主要技術に焦点を当てています。近年、顔ベースの感情コンピューティング、インテリジェントな監視指向のビデオ構造化、およびドローン指向のビジュアルコンピューティングにも特別な注意が払われています。理論レベルとアルゴリズムレベルでは、彼とチームは非常に豊富な機械学習、特にディープラーニング研究の経験があり、Xデータ駆動型の機械学習の理論と方法に特に注意を払っています。ここでのいわゆるXデータには、小さなデータ、教師なしデータ、半教師ありデータが含まれます。 、弱い監視データ、ダーティデータ、拡張データなど。

ビジョンと学習青少年セミナー（VALSE）の共同主催者、VALSE運営委員会の最初のローテーション委員長、VALSEウェビナーイベントの共同主催者、および最初のオンライン組織委員会の委員長。 VALSE2019（ヘフェイ）の参加者数は5,000人を超え、VALSEウェビナーのピーク参加者数は1,800人に達し、中国のコンピュータビジョンの分野で最も影響力のある学会の1つになりました。

個人的な関心として、彼は認知神経科学と脳科学の進歩に深く関心があり、生物学的視覚の本質的な問題と視覚コンピューティングへの脳神経科学のインスピレーションについて考え、話し合って喜んでいます。

埋め込み下層開発における6年以上の実務経験。

C C、VC ++、アセンブリ言語に習熟しており、独立してDSPドライバー、ARM Linuxドライバーの開発を完了することができ、I2C、SPI、UART、FLASH、DDR3、CF、GPIO、PWM、USB、WIFI、4G、イーサネット、その他の周辺機器ドライバーに習熟している開発とアプリケーション;複数のDSP、Power PC、ARMハードウェアプラットフォームドライバーの開発経験がある、階層型アーキテクチャと開発方法に精通していること。CIS、磁気センサー、厚さセンサー、カメラ、その他のセンサードライバーの実装とデータ収集に精通していること。V VxworksおよびLinuxプラットフォーム開発における豊富な経験は、ubootブートロード、Linuxカーネルの適応およびその他の移植作業を個別に完了することができます。豊富な経験、強力なコミュニケーション、論理的分析能力、学習能力、独立した問題解決能力を備えた、ソフトウェアソリューションアーキテクチャを独立して完了する能力。

■教育経験と学術研究経験

ケンブリッジ大学博士号

Intelligence Leverhulme Center for the Future of Intelligence、LCFI http://www.lcfi.ac.uk/英国LCFI研究所、Top Research Institute、チーフサイエンティスト

Cambridgeケンブリッジ大学でコンピュータサイエンスの博士号を取得、ケンブリッジ大学教授、最高技術責任者。最先端のテクノロジーリーダー。 AI、IoT、RPA、OCR-AI、ERP、クラウド、ビッグデータ、ブロックチェーン、ICT、5G、3D、AR、VR、iCLIP、コア産業用ソフトウェア、コアアルゴリズム、ニュートリノ、量子人工知能およびその他の最先端テクノロジー。

高校で英国に留学し、ケンブリッジ大学で学士号と修士号を取得し、2017年にケンブリッジ大学でコンピューターサイエンスの博士号を取得しました。これまでのところ、プロジェクトリーダーとして、863件の主要プロジェクト、国家科学技術支援プログラムのサブプロジェクト、中国国家自然科学財団のプロジェクトで研究開発を行い、SCIを含む12件の論文とEIの28件の論文を発表しています。 。 29件の記事を収集し、複数の発明特許を取得しました。現在、中国科学院の情報科学院は、基礎コース「コンピュータビジョンと画像理解」と専門的な基礎コース「モダンコンピュータビジョン」を提供しています。 Deepin Technologyは、AI、ブロックチェーンビッグデータ分析クラウド、ERPコンサルタント、テクノロジーメガトレンドの統合に焦点を当てています。東ヨーロッパでの創薬、アジアでのフィンテックとブロックチェーン、フィンテックとブロックチェーン。5つのコアテクノロジー-バイオメトリクス、人工知能、チャットボット、データ分析、ブロックチェーン。 4つのサブセクター：ローン、支払い、貯蓄、保険。高度な生物医学に関連する特別なケーススタディを実施します。データサイエンスとAIは、予測分析の特定の方法を強化します。投資戦略の設計と戦略的パートナーとの連携に関する具体的なビジョンについては、高度な予測はDeepTech業界の統合に焦点を当てています。研究者、研究者、学者、製薬会社、AI企業の技術者。私たちのチームには、本庄タスク博士、本庄祐介博士、ノーベル生理学のPD-1、CSAILの主任研究員、MITのレジーナバルジレイ、トムミジャコラ、マノリスケリス、ピーターソロヴィッツが含まれます。

主な研究分野は、ERPコンサルティング、クラウドテクノロジー、ビッグデータ、ブロックチェーン、人工知能の専門家であり、担当者は、863の主要プロジェクト、国家科学技術支援プログラムのサブプロジェクト、中国国家自然科学財団の一般プロジェクトの研究開発を行っています。 SCIの50論文、EIの60論文、ISTPの129論文、225の発明特許を含む、510を超える科学研究論文を発表。たとえば、コンピュータビジョン、マルチメディアテクノロジー、機械学習、清華大学の教授。技術的メガトレンドとの統合、主要な分析手法は政府/地方自治体、教育/医療/ヘルスケア、金融、製造、物流、通信/放送、建設/不動産、電気/ガス/水道、ネットワーク、製薬で使用されています、農業、小売、製造、輸送、スポーツ、航空宇宙、広告、IOT、ICT、その他の産業。

AIレポータースポーツインテリジェントドライビングAI会計弁護士AI外科ロボットAI質問誤診AIロボット葬儀なしVR AR 3D AI犯罪追跡AI画像認識。中国科学技術協会と江蘇省および浙江省の人民政府がカテゴリーAの最優秀賞を受賞しました。偉大な健康産業：生物医学、医療機器、スマート医療、健康管理などがカテゴリーAの最優秀賞を受賞しました。

主な研究分野は、コンピュータビジョン、ビッグデータブロックチェーン、自然言語処理です。アリババグループのチーフテクニカルエキスパート

AI AIディープラーニング（画像認識と音声認識に使用される人工知能（AI）技術の1つ）によって発明されたラグビー5Gテクノロジー。ディープラーニングと組み合わせたラグビーゲーム分析システムが開発されました。AIの経過時間を測定するために、カメラで人体の動きを撮影します。体の姿勢と動き、AIガイド5Gテクノロジーデータマイニング、人工知能：1、ネットワーク分析（ソーシャルネットワーク）2、データクラスタリング3、医療データ処理4、推奨アルゴリズム5、精密教育

清華大学の機械学習教授。テクノロジーメガトレンドとの統合、最先端の分析テクノロジー、政府/地方自治体、教育/医療/ヘルスケア、金融、製造、物流、通信/放送、建設/不動産、電気/ガス/水、ネットワーク、農業用医薬品、小売、製造、輸送、スポーツ、航空宇宙、広告、モノのインターネット、ICT、その他の産業。ケンブリッジ大学のA教授、清華大学のコンピューターサイエンスの客員教授、江蘇省の中国科学技術大学の客員教授が主催する客員教授が、バイオ医薬品、医療機器、インテリジェントメディシン、ヘルスケアなどの大規模医療業界の最優秀賞を受賞しました。カテゴリーAで最優秀賞を受賞。 ERPブロックチェーンクラウドテクノロジーのビッグデータ人工知能関連分野（人工知能のさまざまな分野を含む音声処理の分野に限定されない）は、特許製品の競争、Ali、Tencent、Huaweiなどの契約で最高の賞を受賞し、多くの契約に達しました。 AI x 5Gが主流の支払い方法になりました、WYSIWYG、短いビデオAIアニメーションx 5G、AR VRおよび3D、スマート運転、金融、5G遠隔医療5G AI医学、スマート運転、スマートビジネス、スマートビジネス、スマート医療、公安5Gロボット、5G材料、半導体、スポーツ、エンターテインメントなどのテクノロジーは、主流のテクノロジー、分離と精製、革新的な医薬品、バイオテクノロジー、チップデザイン、量子ドットディスプレイ、マルチタッチ、ナノスフィア、低炭素ナノ材料インテリジェント運転、インテリジェント製造、ロボット技術、インテリジェント医療のグローバルキーテクノロジー。顔と体の分析技術、SLAMと3Dビジョン、一般的および専門的な画像認識、ロボット制御とセンシング、大規模なビデオの理解とマイニング、医用画像分析を強化するための画像とビデオ処理、人工知能コンピューティングプラットフォーム、人工知能スーパーコンピューティングプラットフォーム、自己開発トレーニングフレームワーク、高性能異種コンピューティングソフトウェアとハ​​ードウェアを組み合わせることによる人工知能高性能ストレージ、高性能、低コスト、低消費電力のエッジ人工知能チップとソリューションの開発とパートナーの開発。インテリジェントな運転とAIoTのために、超高コストパフォーマンス、高いエネルギー効率、オープンツールチェーン、豊富なアルゴリズムモデルサンプル、および包括的なアクティベーションサービスを備えたエッジツーエッジAIチップを提供できます。現在、革新的な人工知能専用コンピューティングアーキテクチャに基づくBPU（Brain Processing Unit）が正常にストリーミングされています。中国初の最先端人工知能プロセッサ-インテリジェント運転に焦点を当てたシステムとAIoTに焦点を当てたシステム。そして、大規模に商品化されました。中国人工知能協会、ブリティッシュサイエンスカウンシルのメンバー、ブリティッシュ衆議院の科学技術委員会、ブリティッシュ衆議院（科学技術委員会）のメンバーブリティッシュカウンシルオープンデータインスティテュート（ODI）アレンチューリング人工知能研究所、ケンブリッジ大学、エディンバラ大学、オックスフォード大学、ロンドン大学、ワーウィック大学EPSRC協会を含む

京都大学の特別客員准教授、奈良先端科学技術大学院大学の客員准教授。 2017年から2019年まで、京都大学文部科学省の公共安全分野における主要な産学共同研究プロジェクトの特別研究員を務めた。奈良先端科学技術大学とCMUが共同で設立した国際ロボットとビジョンの共同研究研究所の特別助教授と実際の研究室の責任者を務めた。 10を超える国内および国際協力プロジェクト（973、863、基金の主要な研究計画、文部科学省の戦略的推進料の主要プロジェクト、および国家基金委員会を含むその他の主要なトピックを含む）の詳細な参加または主宰プロジェクト、主要プロジェクト、文部科学省、科学技術省の青年プロジェクト、日英二国間協力プロジェクト、マイクロソフトアジア研究所が支援するプロジェクトなど）は、英語の論文の60を超える二重盲検レビュー（トップミーティングの10を超える論文を含む）を発表しました。 。研究の方向性は、主に画像またはビデオに基づくコンテンツ理解に焦点を当てています。現在行われている研究には、人の特定、人と人と機械の間のコミュニケーションと相互作用の視覚的理解、人と人のポーズの推定、動作認識、および行動予測が含まれます。その中で、最初のグローバルオープンチャレンジでは、手のジェスチャーが手と体の相互作用の単一のチャンピオンを獲得すると推定されています。 ICPR 2018 Piero Zamperoni Best Student Paper AwardおよびAutoML2018 Workshop Best Paper Awardを受賞した、学生を訪問する仕事。

AI AI、IoT、RPA、OCR-AI、ERP、クラウド、ビッグデータ、ブロックチェーン、ICT、5G、3D、AR、VR、iCLIP、コア産業用ソフトウェア、コアアルゴリズム、ニュートリノ、政府/ローカルに最適政府の最先端技術教育/医療/ヘルスケア、金融、製造、ロジスティクス、通信/放送、建設/不動産、電気/ガス/水、ネットワーク、製薬、農業、小売、製造、輸送、スポーツ、航空宇宙、広告、モノのインターネット、ICT、その他の産業

IEEE、NIPS、ICML、COLT、CVPR、ICCV、ECVC、IJCAI、AAAI、UAI、KDD、SIGIR、WWW、ACL、PAMI、IJCV、JMLR、AIJは100回以上公開されています。

人工知能とディープラーニングの分野のリーダーであり、英国（Google）研究所のCTOであり、40以上のアルゴリズム研究チームを率い、300以上の特許を蓄積しています。 1980年代以来、彼は無人自動車と医療ECG支援診断の分野で人工知能の関連研究に従事してきました。 1992年にシンガポール松下研究所に入社し、オーディオおよびビデオ信号処理と圧縮アルゴリズムの設計と応用に従事し、その後画像認識の分野の研究に注力。 2013年には、チームをディープラーニングにシフトすることで主導的役割を果たしました。彼は、ディープラーニングとコンピュータービジョンの分野で目覚ましい成果を上げました。彼は、コンピュータービジョンのフルスタックの技術的能力と、複数のアプリケーション分野にわたる技術を持っています。そして、認識、歩行者の検出と追跡、歩行者の再認識、車両の認識、自動運転、運転者の行動の検出、モバイル操作ロボットなどの分野は、世界クラスの結果を達成しています。 2019年3月、シェンシェンメイは国内のAI企業であるPengsi Technologyにシンガポール研究所の主任科学者兼学部長として入社し、監視とセキュリティ、スマートシティ、自動運転、インテリジェントロボット、AIファクトリオートメーションの分野における関連技術の研究に専念しました。 。 2019年、シェンシェンメイはPengsi Singapore Research Instituteを率いて、顔認識、歩行者の再識別（Person ReID）、車両の再識別（Vehicle ReID）および異常を含む13のコンピュータービジョンテクノロジー関連の世界選手権で優勝しました行動検出（異常検出）およびその他のコンピュータービジョンテクノロジーの多くの分野。

英国のICF研究所のパターン認識の英国国家鍵研究所の研究者、およびコンピュータービジョンの上級研究者。博士課程講師、南京人工知能チップ革新研究所の副社長。 2017年にケンブリッジ大学でコンピューターサイエンスの博士号を取得し、2017年に中国科学院のオートメーション研究所でパターン認識とインテリジェントシステムの博士号を取得しています。現在は主にディープラーニング、画像・動画コンテンツ分析、人工知能チップ設計などに携わっており、関連分野で100以上の学術論文を発表、英語で2編編集しています。関連する業績は、中国科学院の陸家渓ヤングタレントアワード、中国科学院青年振興協会の優秀会員賞（中国電子工業自然科学大賞、文部省自然科学第2賞、中国画像およびグラフィックス社会科学技術大賞）を連続して受賞しています。 IEEE、ACM、CCFなどの国際および国内の学術機関のメンバーであり、中国自動化学会のパターン認識委員会の副事務総長を務めています。現在は、国際ジャーナルのパターン認識、IET Computer Visionの編集委員、2010 ICICCS国際会議およびHHME 2010の議長を務めています。 CCPR 2012パブリッシングチェアマン、ICIG 2019特別セッションチェアマンの組織化会長。

彼の研究の方向性には、データのクラスタリング、ソーシャルネットワーク、推奨システム、および医療データ処理が含まれます。彼は、IEEE TPAMI、IEEE TKDE、IEEE TCYB、IEEE TNNLSなどのトップ国際出版物や、KDD、AAAI、IJCAI、CVPRなどのトップ国際会議を含む100を超える学術論文を発表しています。 2019日中人工知能学会優秀博士論文賞を受賞し、権威ある人工知能ジャーナルであるJournal of Artificial Intelligence Research（JAIR）の共同編集者（AE）です。中国人工知能協会-パターン認識専門委員会、中国コンピューター協会-データベース専門委員会、中国コンピューター協会-コンピュータービジョン専門委員会、CCF-YOCSEF副会長（2018-2019）、CCF支部副会長（2019.3 -2021.3）。

国立研究開発法人理化学研究所人工知能プロジェクト（RIKEN AIP）の科学者であるデータ分析・人工知能研究所のメンバー。主な研究の方向性は機械学習とディープラーニングです。香港バプテスト大学に入学する前は、日本物理化学研究所の人工知能プロジェクトで博士研究員を務めていました（杉山正教授シャンシャン教授のチーム）。ノイズデータ（ラベルとサンプル）の強力なディープラーニング手法の開発を担当しており、その結果は、2019年に最高の成果を収めた理化学研究所賞を受賞しました。 。彼は、機械学習のトップ会議（ICML、NeurIPS、AISTATS、ICLR）およびトップジャーナル（JMLR、TPAMI、MLJ）の長期サービスのプログラム委員会メンバーおよびレビューアを務め、NeuroIPS’20のエリアチェアに選出されました。

近年、ディープラーニングは、画像分析、音声認識、自然言語処理などの多くの問題で急速な進歩を遂げており、多くのインテリジェントシステムやアプリケーションで不可欠なキーテクノロジーになっています。ディープラーニングモデルのパフォーマンスが向上し続けると、計算の複雑さとリソース消費も増加します。これは、特にリソースが限られているエッジエンドデバイスでのネットワークモデルの展開に大きな課題をもたらします。これらのネットワークモデルを効率的かつ効率的に計算する方法が重要な問題になっています。このレポートでは、定量的学習の観点からディープニューラルネットワークモデルの効率的な計算を紹介します。最初に、量子化学習に基づくモデル圧縮と加速方法を紹介し、次にオペレーターレベルの加速に基づく量子化エンジンを紹介し、最後に量子化計算に基づいたアーキテクチャとチップを紹介します。

現在、ケンブリッジ大学のコンピュータサイエンススクールの教授（生涯講師）です。英国国立情報通信技術研究所は、オックスフォードケンブリッジICF研究所のコンピュータービジョンチームで働いています。現在、統計的機械学習とコンピュータービジョンの研究に従事しています。彼は多くの科学研究プロジェクトの議長を務めており、重要な国際学術雑誌や会議で120を超える論文を発表しています。アソシエイトエディターとして活躍したジャーナルには、パターン認識、ニューラルネットワーク上のIEEEトランザクション、学習システムなどがあります。彼は重要な国際学術会議（ICCV、CVPR、ECCBなど）のプログラムメンバーを何度も務めています。彼はケンブリッジ大学の高校で学士号を、コンピューター人工知能の博士号を取得しています。 2016年には、オーストラリア研究評議会からフューチャーフェローシップが授与されました。

主な研究の方向性は、機械学習とコンピュータビジョンです。 2017年に英国ケンブリッジ大学で医用画像解析研究を実施し、NIPS、CVPR、ICCV、AAAI、IJCAI、その他のCCF-A会議、IEEEトランスジャーナル（IEEE T-PAMIを含む）などの国際会議やジャーナルで70を超える論文を発表しました。 / T-IP / T-NNLS / T-CYB）35の論文、多くのCVPR、NIPS論文が口頭論文とスポットライト論文に選ばれました。国立自然科学財団青年プロジェクトおよび一般プロジェクト、天津自然科学基金一般プロジェクトを主宰。 IJCAI、AAAI、CVPR、ICCVおよびその他の会議のプログラム委員会メンバーまたはレビューアを務め、中国機械学習会議（CCML 2017）のローカル組織の議長、および国際ジャーナルIEEE T-PAMI / T-IP / T-NNLS / T-CYBを務めたレビュアー。

主にマルチモーダルデータのコラボレーションコンピューティングに関する研究に従事。中国国家自然科学財団や科学技術部の「863」プログラムなど、20を超えるトピックの議長を務めています。26の発明特許を取得し、IEEEを含む世界クラスのジャーナルや主要な国際会議で80を超える論文を含む100を超える学術論文を発表しています。 TNNLS、TIP、TCYB、ICML、NIPS、ICCV、CVPRなどGoogle Scholarによる2,200回以上の引用、18人の研究者/中国、米国、英国、その他の国からのフェローは、彼の研究成果を引用し、肯定的なコメントをしました。研究成果は、国家自然科学賞（2016年、3番目の達成者）の2等賞、陝西省科学技術賞（2011、2015、3番目の達成者）の2等賞、および教育省自然科学賞の1 2等賞を受賞しました（2013）。

学術成果：TPAMI、TIP、CVPR、ICCV、AAAIなどのCCFクラスA学術会議など、IEEE / ACMジャーナルに30を超える記事が公開されました。 Google Scholarによって公開された記事は1,200回以上引用されており、1つのジャーナルの最高のインパクトファクターは17.73です。マイクロ発現分析に関する関連研究は、MIT Technology ReviewやBritish Daily Mailなどの国際的な権威あるメディアが発表した記事に記載されています。科学技術部の主要な研究開発プロジェクトを主宰し、フィンランド情報技術協会のポスドク研究資金プロジェクトおよびフィンランド科学アカデミーのICT2023特別プロジェクト（共同ホスティングを含む）をホストし、フィンランド科学アカデミーの資金プロジェクトおよび中国国家自然科学財団プロジェクトに参加しました。教育と学生のトレーニング：7人の大学院生のトレーニングと共同トレーニングを行い、「ディープラーニングとその応用」や「感情コンピューティング」などのコースを教えました。アカデミック：IEEE / IEEE-CS / AAAI / CCFメンバー、IEEE T-PAMI、T-IP、T-NNLS、T-CYB、T-CSVT、T-MM、Springer IJCV、Elsevier Pattern Recogとして何度も招待されています。その他の重要な国際学術ジャーナル、IEEE ICCV、IEEE CVPR、AAAI、ACM MMおよびその他の重要な国際学術会議、プログラムの議長またはレビューア、および国際ジャーナルのパターン認識（2017）およびニューロコンピューティング（2017）から優れたレビューを取得寄稿者のタイトル。 5つのテーマ別セミナー（CVPR20、FG18-20、ACCV16）を国際的なカウンターパートとの主流の国際会議で開催し、Springer SIVPジャーナルのゲスト編集委員会および多くの国際会議/テーマの支部議長を務めたプログラム委員

2019 2019年7月に中国に帰国して以来、Guolong教授は、国立自然科学財団の青少年プロジェクトや江蘇省の新世代人工知能キーR＆Dプロジェクトなど、多くの重要なプロジェクトを統括してきました。 Tan Mingkui教授は、機械学習とディープラーニングに関する研究に従事しており、ディープニューラルネットワークの構造最適化と理論的分析において一定の研究基盤を持っています。近年、作品または対応する著者によって完了された関連する成果は、NIPS、ICML、ACML、AAAI、CVPR、IJCAIなどの人工知能国際会議や、IEEE TNNLS、IEEE TIP、IEEE TSP、IEEE TKDE、JMLRなどの人工知能信頼できるジャーナルに公開されています。 。彼は国家戦略と人工知能の主要なニーズに直面し、模倣イメージング、脳のような認識、および脳のような評価に関する理論的および応用的研究に長い間従事してきました。国立自然科学財団、軍事科学技術委員会の国家防衛科学技術革新プロジェクト、教育省の合同基金の若手人材プロジェクトなど、多数の国家プロジェクトを主催しました。情報処理システムと配信。 40を超えるSCIソースジャーナルの論文を含む60を超える関連する学術論文（40を超える筆頭著者）を発表しており、そのうち3つはESIの高引用論文として選ばれました。

Guolong、Ph.D.、Haniuqiao Intelligent Technology Nanjing Research Instituteの学部長、南京大学の学生起業家の講師、主な研究分野はコンピュータービジョンと機械学習、IEEE TPAMI、IEEE TIP、IEEE TNNLS、機械学習などの関連分野のトップジャーナルJournalおよびCVPR、ICCV、IJCAI、ICDM、ACCVなどの他のトップカンファレンスは20を超える論文を発表し、チームをiNaturalist、Apparent Personality Analysisなどの国際的な権威あるコンピュータービジョンコンペティションで3つの世界選手権に勝ちました。重要な国際会議PRICAI 2018およびICME 2019での「細かい画像分析」というタイトルの組織化されたチュートリアル。 「Analytic Deep Deep Learning-Principles and Visual Practice of Convolutional Neural Networks」の著者。 CVPR 2017の最優秀レビュアーおよび博士課程学生のための南京大学学長の特別奨学金として表彰され、ICCV、CVPR、ECCB、NIPS、IJCAI、AAAIおよびその他の国際会議のPCメンバーを務めました。 NEC USシリコンバレーラボラトリーは研究に従事しています。研究の方向性は、大規模なマルチメディア情報の検索とコンピュータービジョンであり、歩行者の再認識、きめの細かいオブジェクト認識、およびシーン理解の研究に焦点を当てています。彼は、IEEE T-PAMI、TIP、TMM、ICCV、CVPR、ACM MM、AAAI、その他の権威ある国際会議などの権威ある国際的なジャーナルに、最初の/対応する著者として50を超える論文を発表しています。 、中国コンピュータ社会の優秀な博士論文、中国科学院の優秀な博士論文、マイクロソフト奨学生賞など中国国家自然科学財団、主要な研究開発計画の栽培プロジェクト、国家の主要な研究開発計画、その他のプロジェクトを主宰しています。

主な研究対象は、カーネルアルゴリズム、マルチビュークラスタリングアルゴリズム、特徴選択アルゴリズムなどです。 90を超える論文と46のSCI（IEEE T-PAMI、IEEE T-KDE、IEEE T-IP、IEEE T-IFS、IEEE T-NNLS、IEEE T-Cyber​​netics、IEEE Tを含む17のIEEEトランザクションを含む）を発行-MMなど）、中国のコンピュータ協会によってClass A会議の論文として指定された23の論文、Google Scholarは1,600回以上引用し、3つの論文がESIコンピュータサイエンス分野の上位10％に参加しました。彼は、AAAI 2016-2019、IJCAI 2016-2019、NIPS 2016-2018、およびAAAI 2020上級プログラム委員会のプログラム委員会のメンバーを務めました。国立自然科学財団青年基金、一般プロジェクト、および優秀青年基金のそれぞれを1つ主催し、研究結果は浙江省自然科学の最優秀賞を受賞しました。

VALSEはACおよびCASIG-BVD委員会メンバーを実装します。主な研究分野は、深層学習、パターン認識、コンピュータービジョン、マルチメディア分析であり、特に、無制限/大規模/小規模サンプル/不均一/セットベースの顔認識、深い対立学習に基づく画像生成、超解像、知識抽出、（非）インスタンスレベルの細かい文字分析に基づくモデル圧縮。現在、3つの主要なR＆Dプロジェクト（ランキング1/3/5）に着手/参加しています。 T-PAMI、IJCV、T-IP、NeuroIPS、CVPR、IJCAI、ECCV、ACM MM、AAAI、BMVC、WACV、およびこの分野の他の権威ある国際的なジャーナル/会議で30を超える学術論文を発表し、単一の影響度が最も高い17.73でした。関連する作品が、シンガポールのパターン認識および機械知能協会PREMIA 2019 Lee Hwee Kuan Award（ゴールドアワード）を受賞しました。これは、国際マルチメディア分野のトップカンファレンスであるACM MM 2018 Best Student Paper Award、ICCV 2017 MS-Celeb-1Mの大規模顔認識コンテストハードセット/すべてのタスクのランダムセット/低ショット学習チャンピオン;すべてのタスクのCVPR 2017 LIPコンテストの文字分析と文字ポーズ推定の次点者;国立標準技術研究所NIST 2017 IJB-A制限なしの顔認識競争の顔認証と顔識別すべてのミッションのチャンピオン。 IJCV、T-MM、T-IFS、T-CSVT、ニューロコンピューティング、NeuroIPS（NeurIPS 2018上位30％の最高レビューア）、CVPR、ICCV、ECCB、ACM MM、AAAI、ICLR、ICML、UAIなどとして機能します。主流の国際ジャーナル/会議の招待査読者。

985 ケンブリッジ修士号、ソフトウェア専攻、確固たる理論と技能準備、卒業後のソフトウェア開発に従事、AMD、クアルコム、ザイリンクスなどの有名企業での仕事、現在はザイリンクス中国チームの最初のAIソリューションのシニアテクニカルエキスパート

Linux Linuxシステムソフトウェア開発に精通し、コアアップストリームサブミッションを提出している。この段階では、マシンビジョンにおけるディープラーニングテクノロジーの適用に焦点を当てています。 Caffe / Tensorflow / Keras / Pytorchフレームワークモデルの導入経験（ザイリンクスプラットフォーム、Nvidia GPU / Jeston Nano）、および基本モデルの再トレーニング機能を備えています。英語は使用言語として使用できます。豊富で豊富な技術開発経験に裏打ちされた優れた技術営業能力。

■強み経験技術力

Linuxカーネルとドライバーの開発。Linuxカーネルドライバーの開発とX86およびARMプラットフォームに基づくデバッグの豊富な経験があります。V V4L / DVBオープンソースビデオドライバーフレームワークとALSAオーディオドライバーフレームワーク、FFMpeg / Gstreamerオーディオとビデオアプリケーションフレームワークに精通していること。USB USB / PCI /イーサネット/ I2C / ACPIドライバーおよび関連プロトコルに精通しており、ネットワーク、TCP / IP、AVB、DPDK、

Linux環境プログラミング、C / C ++ / Shell / Pythonアプリケーションプログラミングの経験、git / subversion / perforceなどのマスターバージョン管理ツール、Yocto / buildrootに精通していること。オープンソースコミュニティと関連する開発プロセスに精通しており、Linuxカーネルのアップストリーム開発の経験がある（xHCI / ACPIなど）ザイリンクスfpga socプラットフォームとツールセット、および組み込みソフトウェアスタックに精通している。マシンビジョン関連（CNN / OpenCV / Tensorflow / Caffe / Pytorch）

その他の基本的な専門スキル

C / C ++ / Python /シェル/ Gdb / Git

U-boot / FreeRTOS

Yocto / Buildroot

QT / FFMpeg / GStreamer

Docker / Xen / KVM

FPGA HLSプログラミングとIPI設計

ザイリンクスツール（Vivado / SDK / SDSoC / Petalinux / DNNDK / xfDNN）

AI、IoT、RPA、OCR-AI、ERP、クラウド、ビッグデータ、ブロックチェーン、ICT、5G、3D、AR、VR、iCLIP、コア産業ソフトウェア、コアアルゴリズム、ニュートリノ、政府/地方自治体に適しています最先端の技術教育/医療/ヘルスケア、金融、製造、物流、通信/放送、建設/不動産、電気/ガス/水道、ネットワーク、医薬品、農業、小売、製造、輸送、スポーツ、航空宇宙、広告、物理インターネット、ICT、その他の産業

IEEE、NIPS、ICML、COLT、CVPR、ICCV、ECVC、IJCAI、AAAI、UAI、KDD、SIGIR、WWW、ACL、PAMI、IJCV、JMLR、AIJは100回以上公開されています。

■強み経験

世界のトップ500企業で10年の経験があります

私の利点と経験：

米国のITおよびインターネット国際企業上位500社で10年の実務経験があります。

その中で、私は世界のトップ500のITおよびインターネット国際企業、蓄積された顧客、連絡先、リソース、および効率的な運用管理における１０年の経験と経験があります。

私は、中国の会社のCEO / CTOおよび社長（リーダー）として10年間の経験があり、600人以上のチームがあり、中国の企業の指導、管理、運営において幅広い経験があります。

私は10年のマーケティング経験があり、販売、マーケティング、チャネルチームの100人以上の人々が中国市場を開拓し、ビジネスを拡大してきました。

私は、米国の上位500のITおよびインターネット企業の管理概念、管理モデル、管理プロセス、および管理方法を学び、借用し、吸収し、中国の従業員の特性と組み合わせて、中国の企業のビジネス戦略の発展のニーズを満たす一連の管理方法を実践、要約、および最適化しましたそして、企業の全体的な開発経験を効果的に把握する方法。

私は、強力な操作性と強力な実行力を備えた企業戦略と戦術を開発する方法を学びました。

私は、前向きでハイエンドな優れた意思決定スキル、および鋭い洞察力と鋭いビジネス洞察力を磨く方法を学び、蓄積しました。

表現力、親しみやすさ、判断力、意思決定力、企画力、交渉力などの経験を積み、積み上げてきました。

私はチームを効率的に、協調して作業するように指導、呼び出し、要約し、動機づける方法を学びました。

10年間の仕事で、私は毎年優れた仕事のパフォーマンスを達成しています：

米国が出資した世界で最初の500のITおよびインターネット国際企業

完了パフォーマンス：2017-現在：1000万/年から5億/年

私が働いている2番目のフォーチュン500 IT＆インターネット会社で、イギリスの中国の会社のチームを率いています。

完了した実績：2012-2017：5500万/年から6億/年

世界をリードする海外のOracle、SAP、Microsoft、IBM、HP、DELL、EMC、Cisco、Salesforce、Google、Amazon、その他の有名な国内外のITおよびインターネットメーカーの先進的な製品、テクノロジー、および将来の製品テクノロジー開発動向を学び、蓄積してきました。知識

以下の業界でのビジネス開発で蓄積した業界経験と豊富な顧客リソースを持っています。

フォーチュン500企業

特大、大規模の国内および外国製造企業（外資企業、中外合弁企業、中央企業、国営企業、民間企業）

金融（銀行、証券、保険）

テレコム（通信、モバイル、Unicom）

政府、教育、医療およびその他の産業

私は、ITとインターネットのトップメーカー、業界シス​​テムインテグレーター、国内外の有名なトップ500企業などの戦略的パートナーとの利点の補完、完全なコラボレーション、および詳細な協力における経験を学び、蓄積してきました。

私は謙虚で有能で正直であることの資質を蓄積することを学びました。

Professional Experience Management China CEO / CTO＆President CTO and President of China

2012 / 01-現在のアリババ契約企業HOC Intelligent Technology Co. Ltd.（10年）

コンピューターサービス（システム、データサービス、メンテナンス）| 500〜1000人|外資（ヨーロッパおよびアメリカ）|フルタイム

経営中国CEO＆CTOPresident中国CEO CEOおよびCTO社長

仕事内容：2012/1-現在：アリババ契約企業Haniuqiao Intelligent Technology Co. Ltd.

ポジション：中国CEO＆社長中国CEO＆社長

会社概要：

Alibabaの契約会社であるHOC Intelligent Technologyは、IT製品の販売、コンサルティング、実装、およびサービスを提供する会社です。この会社は、Oracle、Microsoft、SAP、IBM、EMC、HP、DELL、Ciscoなどの上位500社のグローバルトップ企業をまとめています。 IT＆インターネット企業は、上級マネージャー、ビジネス開発担当者、豊富な実務経験を持つ技術エリートで構成された企業に加わり、中国企業は杭州にあり、ヨーロッパ、イギリス、日本、中国の上海に支社があります。

同社の主な事業：

同社は米国オラクル社のグローバル戦略パートナーです。同社の主な事業は、Oracleデータベース、ミドルウェア、ERP、CRM、SCM、HRM、BPM、EPM、APM、PLM、BI、インターネット、ビッグデータ、Oracleクラウドコンピューティング（DaaS）です。 、IaaS、PaaS、SaaS）AI（人工知能）の幅広い製品と、Microsoft、SAP、IBM、EMC、HP、DELL、Ciscoおよびその他の分野に基づくITおよびインターネット製品により、米国、ヨーロッパ、中国の顧客に最高のITおよびインターネットシステムソリューションのコンサルティング、実装、サービス...

中国企業の従業員数：600人以上

勤務地：上海南京杭州

私の仕事：会長への報告（BOSS）

私の直属の部下30名：セールス＆マーケティング担当副社長、テクノロジー担当副社長、人事管理担当副社長、財務担当副社長

私の責任：

1.本社の取締役会から完全に承認され、中国企業の日常の運営と管理を全面的に主導し、すべてのタスクに対して全体的な責任を負う。

2.中国企業の年間事業開発戦略、事業開発戦略、事業開発計画の策定、および中国企業の年間事業開発目標の実現を主宰する。

3.中国企業の上級管理チームの中間管理職および上級管理職の確立、トレーニング、選任を全面的に担当し、企業の内部管理組織の確立計画と基本管理システムをレビューします。

4.効率的な中国企業組織システム（運用管理システム（管理部門、人事部門、財務部門）、事業開発システム（マーケティング部門、営業部門）、テクニカルサポートサービスシステム（プリセールス部門、ミドルセールス部門、アフターセールス部門）の確立を主宰する）そして組織のプロセス;

5.中国市場と環境の変化に応じて、中国企業の組織構造とリソース割り当てをタイムリーに調整し、中国企業の日常業務を常に管理および監督し、主要な日常企業の問題について決定を下します。

6.国内外のパートナー企業幹部との緊密な連絡と協力に責任を負う。

7.広報とクライアントのハイレベル（最終的な意思決定層）とのやり取りを担当します。

8.会社の取締役会との良好なコミュニケーションを維持する責任があり、中国の会社の事業開発計画の実施、資金の使用、および損益について会社の取締役会に報告します。

部下の数：600 |報告対象：米国の議長（BOSS）|退職の理由：仕事中。

主なパフォーマンス：私のパフォーマンス：

2019-2020：完了パフォーマンス：5億元

2018-2019：完了パフォーマンス：3億5000万元

2017-2018：完了パフォーマンス：3億元、私がサインアップした顧客は次のとおりです：

Oracle、SAP、Microsoft、IBM、HP、DELL、EMC、Cisco、Salesforce、Google、Amazon、およびその他の最高かつ高度なITとインターネット、モノのインターネット、インテリジェントネットワーク、クラウドコンピューティング、ビッグデータ、AIマニュアルを次のお客様に提供しますインテリジェントおよびその他のシステムソリューションのコンサルティング+システムの実装+システムの毎日のメンテナンス+カスタマイズされた開発およびその他の付加価値サービス。 。 。

銀行、証券、保険、業界のお客様には次のものが含まれます。

バンクオブアメリカ、シティバンク、HSBC、ドイツ銀行、上海浦東開発銀行、上海通信銀行、中国銀聯

上海銀行産業銀行上海農村商業銀行中国民生銀行中国エバーブライト銀行青島銀行

寧波銀行重慶商業銀行深セン開発銀行南京銀行

上海証券オリエンタル証券GF証券神陰万国宏源証券海通証券

中国銀河証券中国太平洋保険中国生命保険会社

官公庁のお客様には次のものが含まれます。

上海市財政局上海公安局上海税関上海人事局上海人事社会保障局上海水務局

上海教育委員会浙江省財務省浙江省公安部浙江省人事社会保障省江蘇省財務省江蘇省公安部江蘇省人事社会保障省

教育業界のお客様は次のとおりです。

上海財経大学上海交通大学上海大学上海工科大学上海政治法律大学

医療業界のお客様は次のとおりです。

上海保健委員会上海仁吉病院上海華山病院上海瑞金病院上海東方病院

自動車製造、航空、ガラス製造、鉄鋼製造、食品製造、製紙、電化製品製造、消費財製造、業界の顧客は次のとおりです。

上海汽車グループ上海フォルクスワーゲン上海ゼネラルモーターズ中国東方航空上海宝武鋼集団

上海国際港湾集団上海ブライトフードグループ上海たばこグループ上海製薬グループ

上海電機グループ上海ジャーファ

Procter＆Gamble（China）Company

ミデアグループユニリーバ中国南京汽車集団天津ティエンスグループ慶長安汽車

上海外高橋造船青島ビール中国国家繊維グループヴィンダ紙上海フェイケ電器

上海成光文房具上海日立電機上海関生園電気通信業界のお客様には、

浙江移動通信会社上海移動通信公司上海テレコム江蘇移動通信公司

四川移動通信会社

2012 / 01-2017 / 01マイクロソフトソフトウェアシステム株式会社（6年）採用

コンピューターサービス（システム、データサービス、メンテナンス）| 150〜500人|外資（ヨーロッパおよびアメリカ）|パートタイム

中国CEO＆社長中国CEO＆CTOPresident UK中国CTO CEO CEO＆President

職種：2012〜2017年：米国のMicrosoft Software System Co. Ltd.

ポジション英国中国CEO＆CTOPresident英国中国CTO CEO CEO and President

会社概要：

米国マイクロソフトソフトウェアシステム株式会社は、米国で設立されたIT製品の販売、コンサルティング、実装、およびサービスの会社です。同社は、オラクル、マイクロソフト、SAP、IBM、EMC、HP、DELL、シスコなど、世界のトップ500のIT企業から多くの企業を結集し、豊富な実務経験、ビジネス開発担当者、技術エリートとともに会社の設立に参加しています。 、米国企業の本社はヨーロッパ、イギリス、中国の上海にあります。

同社の主な事業：

同社は米国のグローバル戦略パートナーです。同社の主な事業は、Oracleデータベース、ミドルウェア、ERP、CRM、SCM、HRM、BPM、EPM、APM、PLM、BI、インターネット、ビッグデータ、Oracleクラウドコンピューティング（DaaS、IaaS）です。 、PaaS、SaaS）Microsoft、SAP、IBM、EMC、HP、DELL、Ciscoなどに基づく幅広い製品とITマルチドメイン製品。米国、ヨーロッパ、中国のお客様に最高のITシステムソリューションのコンサルティング、実装、サービスを提供します。 …

英国の従業員数：200人以上

中国企業の従業員数：400人以上

勤務地：上海、英国

私の仕事：米国本部長（BOSS）への報告

私の直属の部下30名：セールス＆マーケティング担当副社長、テクノロジー担当副社長、人事管理担当副社長、財務担当副社長

私の責任：

1.米国本部の取締役会から、中国企業の日常の運営と管理を完全に統括し、すべてのタスクに対して全体的な責任を負うことを完全に承認されている。

2.中国企業の年間事業開発戦略、事業開発戦略、事業開発計画の策定、および中国企業の年間事業開発目標の実現を主宰する。

3.中国企業の上級管理チームの中間管理職および上級管理職の確立、トレーニング、選任を全面的に担当し、企業の内部管理組織の確立計画と基本管理システムをレビューします。

4.効率的な中国企業組織システムの確立を主宰：運用管理システム（管理部門、人事部門、財務部門）、事業開発システム（マーケティング部門、営業部門）、テクニカルサポートサービスシステム（プリセールス部門、ミドルセールス部門、アフターセールス部門）部門）と組織のプロセス。

5.中国市場と環境の変化に応じて、中国企業の組織構造とリソース割り当てをタイムリーに調整し、中国企業の日常業務を常に管理および監督し、主要な日常企業の問題について決定を下します。

6.国内外のパートナー企業の幹部との緊密な連絡と協力に責任を負う。

7.広報とクライアントのハイレベル（意思決定層、下見板層）とのやり取りを担当します。

8.米国の親会社の取締役会との良好なコミュニケーションを維持し、中国の会社の運営および開発計画の実施、資金の使用および損益状況について米国の親会社の取締役会に報告する責任を負う。

部下数：400 |報告対象：米国本部長（BOSS）|退職理由：ケンブリッジ大学留学中、英国卒業

主なパフォーマンス：私のパフォーマンス。

2016-2017：完成したパフォーマンス：5億元

2015-2016：完了パフォーマンス：3億元

2014-2015：完了パフォーマンス：2億5000万元

2013-2012：完了パフォーマンス：2億元

2011-2012：完了パフォーマンス：1億5000万元

契約している顧客は次のとおりです。

以下のお客様に、Oracle、SAP、Microsoft、IBM、HP、DELL、EMC、Cisco、Salesforce、+システムの実装+毎日のシステムメンテナンス+カスタム開発向けの最高かつ最先端のITシステムソリューションのコンサルティングを提供しますその他の付加価値サービス。 。 。

銀行、証券、保険、業界のお客様には次のものが含まれます。

バンクオブアメリカシティバンクHSBCドイツ銀行上海浦東開発銀行上海通信銀行

中国銀聯上海銀行産業銀行上海農村商業銀行

中国民生銀行中国エバーブライト銀行青島銀行寧波銀行

重慶商業銀行南京商業銀行広東開発銀行深セン開発銀行厦門市商業銀行

南京銀行上海証券オリエンタル証券GF証券神陰万国宏源証券

海通証券中国銀河証券中国太平洋保険中国生命保険会社

官公庁のお客様には次のものが含まれます。

上海市財政局上海公安局上海税関上海人事局

上海人事社会保障局上海水務局上海教育委員会

浙江省財務省浙江省公安部浙江省人事社会保障省

江蘇省財務省江蘇省公安部江蘇省人事社会保障省

教育業界のお客様は次のとおりです。

上海財経大学上海交通大学上海大学上海工科大学上海政治法律大学

医療業界のお客様は次のとおりです。

上海保健委員会上海仁吉病院上海華山病院上海瑞金病院上海東方病院

自動車製造、航空、ガラス製造、鉄鋼製造、食品製造、製紙、電化製品製造、消費財製造、業界の顧客は次のとおりです。

上海汽車グループ上海フォルクスワーゲン上海ゼネラルモーターズ中国東方航空上海宝武鋼集団

上海国際港湾集団上海ブライトフードグループ上海たばこグループ上海製薬グループ

上海電機グループ上海ジャーファProcter＆Gamble（China）Company

ミデアグループユニリーバ中国南京汽車集団天津ティエンスグループ重慶長安汽車

上海外高橋造船青島ビール中国国家繊維グループヴィンダ紙

上海フェイケ電器上海成光文房具上海日立電機上海関生園

電気通信業界の顧客には、次のものが含まれます。浙江移動通信会社

上海移動通信公司上海テレコム江蘇移動通信公司四川移動通信会社

プロジェクト経験

2017 / 01-これまでのところ、クライアントに署名しています：

関連会社：アリババ、アマゾン、テンセントおよび他の有名な企業

プロジェクトの説明：私がサインアップした顧客は次のとおりです。

Oracle、SAP、Microsoft、IBM、HP、DELL、EMC、Cisco、Salesforce、Google、Amazon、最適な最先端のITとインターネット、クラウドコンピューティング、ビッグデータ、AI人工知能、その他のシステムソリューションを提供しますソリューションコンサルティング+システムの実装+システムのアフターメンテナンスサービス+カスタマイズされた開発およびその他の付加価値サービス。 。 。

銀行、証券、保険、業界のお客様には次のものが含まれます。

銀行、証券、保険、業界のお客様には次のものが含まれます。

バンクオブアメリカ、シティバンク、HSBC、ドイツ銀行、上海浦東開発銀行、上海通信銀行、中国銀聯

上海銀行産業銀行上海農村商業銀行中国民生銀行中国エバーブライト銀行青島銀行

寧波銀行重慶商業銀行深セン開発銀行南京銀行

上海証券オリエンタル証券GF証券神陰万国宏源証券海通証券

中国銀河証券中国太平洋保険中国生命保険会社

官公庁のお客様には次のものが含まれます。

上海市財政局上海公安局上海税関上海人事局上海人事社会保障局上海水務局

上海教育委員会浙江省財務省浙江省公安部浙江省人事社会保障省江蘇省財務省江蘇省公安部江蘇省人事社会保障省

教育業界のお客様は次のとおりです。

上海財経大学上海交通大学上海大学上海工科大学上海政治法律大学

医療業界のお客様は次のとおりです。

上海保健委員会上海仁吉病院上海華山病院上海瑞金病院上海東方病院

自動車製造、航空、ガラス製造、鉄鋼製造、食品製造、製紙、電化製品製造、消費財製造、業界の顧客は次のとおりです。

上海汽車グループ上海フォルクスワーゲン上海ゼネラルモーターズ中国東方航空上海宝武鋼集団

上海国際港湾集団上海ブライトフードグループ上海たばこグループ上海製薬グループ

上海電機グループ上海ジャーファ

Procter＆Gamble（China）Company

ミデアグループユニリーバ中国南京汽車集団天津ティエンスグループ慶長安汽車

上海外高橋造船青島ビール中国国家繊維グループヴィンダ紙上海フェイケ電器

上海成光文房具上海日立電機上海関生園電気通信業界のお客様には、

浙江移動通信会社上海移動通信公司上海テレコム江蘇移動通信公司

四川移動通信会社

責任の説明：上記のプロジェクトでは、中国のCEOのCEO / CTOとして、プロジェクト全体の完了を調整しました。

2012 / 01-2017 / 01私の署名された顧客は次のとおりです：

関係会社：米国Microsoft Software System Co. Ltd.に勤務

プロジェクトの説明：以下のお客様に、Oracle、SAP、Microsoft、IBM、HP、DELL、EMC、Cisco、Salesforce、システムの実装+システムの毎日のメンテナンスの最高かつ最も先進的なITシステムソリューションのコンサルティングを提供します+カスタマイズされた開発およびその他の付加価値サービス。 。 。

銀行、証券、保険、業界のお客様には次のものが含まれます。

銀行、証券、保険、業界のお客様には次のものが含まれます。

バンクオブアメリカ、シティバンク、HSBC、ドイツ銀行、上海浦東開発銀行、上海通信銀行、中国銀聯

上海銀行産業銀行上海農村商業銀行中国民生銀行中国エバーブライト銀行青島銀行

寧波銀行重慶商業銀行深セン開発銀行南京銀行

上海証券オリエンタル証券GF証券神陰万国宏源証券海通証券

中国銀河証券中国太平洋保険中国生命保険会社

官公庁のお客様には次のものが含まれます。

上海市財政局上海公安局上海税関上海人事局上海人事社会保障局上海水務局

上海教育委員会浙江省財務省浙江省公安部浙江省人事社会保障省江蘇省財務省江蘇省公安部江蘇省人事社会保障省

教育業界のお客様は次のとおりです。

上海財経大学上海交通大学上海大学上海工科大学上海政治法律大学

医療業界のお客様は次のとおりです。

上海保健委員会上海仁吉病院上海華山病院上海瑞金病院上海東方病院

自動車製造、航空、ガラス製造、鉄鋼製造、食品製造、製紙、電化製品製造、消費財製造、業界の顧客は次のとおりです。

上海汽車グループ上海フォルクスワーゲン上海ゼネラルモーターズ中国東方航空上海宝武鋼集団

上海国際港湾集団上海ブライトフードグループ上海たばこグループ上海製薬グループ

上海電機グループ上海ジャーファ

Procter＆Gamble（China）Company

ミデアグループユニリーバ中国南京汽車集団天津ティエンスグループ慶長安汽車

上海外高橋造船青島ビール中国国家繊維グループヴィンダ紙上海フェイケ電器

上海成光文房具上海日立電機上海関生園電気通信業界のお客様には、

浙江移動通信会社上海移動通信公司上海テレコム江蘇移動通信公司

四川移動通信会社

責任の説明：上記のプロジェクトでは、中国のCEOのCTO / CEOとして、プロジェクト全体の管理

■実務経験

Dr. Guolongケンブリッジ大学ケンブリッジ

LCFI http://www.lcfi.ac.uk/Chief Scientist、LCFI Labs、英国

AlibabaGroup契約ユニットHOC Intelligent Technology Guolong副社長CTO

実務経験プロジェクト経験学術研究経験実績*

2012-2017マイクロソフト開発チームのソフトウェアおよびハードウェア開発エンジニア、Google開発チームPMケンブリッジ大学

2017 / 1–現在のケンブリッジ大学博士、ケンブリッジ、OXBridge教授

リーバーハルムセンター、フューチャーインテリジェンスの未来、LCFI http://www.lcfi.ac.uk/トップ研究所、主任研究員、LCFI研究所、英国

現在、NTTとアリババグループのハニチャオインテリジェントテクノロジーCEO / CTO、PM部長、中国科学院の研究者、JSAI人工知能協会の正会員、IEEEメンバー、CAAI中国

人工知能協会、AAA1国際人工知能協会のメンバー。人工知能中国ACMのACMコミッショナーのメンバーであり、ERPコンサルティング、クラウドテクノロジー、ビッグデータ、ブロックチェーン、人工知能の専門家、コンピュータービジョン、マルチメディアテクノロジー、機械学習などの主な研究分野を担当しています。

清華大学の機械学習教授。テクノロジーメガトレンドとの統合、最先端の分析テクノロジー、政府/地方自治体、教育/医療/ヘルスケア、金融、製造、物流、通信/放送、建設/不動産、電気/ガス/水、ネットワーク、農業用医薬品、小売、製造、輸送、スポーツ、航空宇宙、広告、

モノのインターネット、ICT、その他の産業。ハーバードビジネススクール客員教授、清華大学客員教授、東京大学客員教授、東京大学客員教授、大阪大学客員教授、江蘇省中国科学技術院

京都大学の客員教授が主催するコンペティション部門Aが、バイオ医薬品、医療機器、スマート医療、ヘルスケアなどの大手医療産業の最優秀賞を受賞しました。カテゴリーAで最優秀賞を受賞。 ERPブロックチェーンクラウドテクノロジーのビッグデータ人工知能関連分野（人工知能のさまざまな分野を含む音声処理の分野に限定されない）は、特許製品の競争、Ali、Tencent、Huaweiなどの契約で最高の賞を受賞し、多くの契約に達しました。 AIx 5Gが主流の支払い方法になりました、WYSIWYG、短いビデオAIアニメーションx 5G、AR VRおよび3D、スマート運転、金融、5G遠隔医療5G AI医療、スマート運転、スマートビジネス、スマートビジネス、スマート医療、公安用の5G素材5Gロボット、半導体、スポーツ、エンターテインメントおよびその他のテクノロジーは、当社の主流のテクノロジー、分離および精製、革新的な医薬品、バイオテクノロジー、

深層学習の学術工学プロジェクトにおける10年以上の経験。人工知能に関連するアルゴリズムと理論、特にニューラルネットワーク、深層学習、強化学習、転移学習に精通している。SAS、R、Python、Spark SQL、Spark MLなどのデータ分析ツールに精通している。言語、LR / GMM / SVM / CRF / MaxEnt / HMM / LDA / DNN / CNN / RNNの研究背景。

◎CUDAプログラミング、Python / Matlab / C ++、およびディープラーニングアーキテクチャ、マシンビジョン、バイオインテリジェンス認識やその他の人工知能アルゴリズム、メディアフリーペイメントなどのコアテクノロジーなどの独立した知的財産権を使用するその他のコアテクノロジーに精通していること。 、AIチップ、スマートリテール、スマートシティ、スマートセキュリティ、スマート教育、航空宇宙日本の小惑星、軍事産業、およびその他の分野は、業界のリーダーとしてランク付けされた詳細なレイアウトを持っています。

◎人工知能は、製造（ロボット、最適化）輸送、生物学的ヘルスケア、通信、機械学習シミュレーション、エッジ機器、ネットワーク、高性能コンピューティング、製品開発（一般）、製品開発（視覚検査ソリューション）、人間とコンピューターの相互作用で使用されます（HCI、HRI）

重要なプロジェクト経験

◎Google、マイクロソフト、テンセント、アマゾンなどの優れたチームと連携しているバイオヘルスケア業界、私は京都大学ハーバード大学医学部の遺伝子と協力しています。ノーベル医学遺伝子iPS幹細胞チーム

◎ファナック、PFN（PREFERRED NETWORKS）、日立と連携。バイオヘルス医療画像解析と血液がんの早期診断技術の研究進展。国立がん研究センターと他の共同研究。ブロックチェーン技術と人工知能生物医学AIは、医療画像分析、電子医療記録（EHR）の詳細な研究、疾患の原因の発見、前臨床の創薬と臨床試験などの基本的な研究など、さまざまな分野で使用されています。

プロジェクト経験

◎アマゾン人工知能プロジェクト（Amazon Go）への参加：シアトルで実験的な食料品店が正常に実装されました。これにより、買い物客は棚で商品を拾ったり、レジを離れたり、セルフチェックアウトキオスクに立ち寄ったりできます。それらが店に入ると、コンピュータはそれらを視覚的に認識し、次にそれらを棚から取り出された製品にリンクします。顧客が離れると、システムはショッピングバッグの費用をAmazonアカウントから差し引いて、電子メールの領収書を送信します。

◎ドイツのeコマース企業Ottoに参加：ディープラーニングモデルを使用して何十億ものトランザクションを分析し、注文前に顧客が購入する傾向があるものを予測し、余剰在庫を20％削減し、生産を年間200万以上削減します。今後30日間でこのシステムによって予測される製品の精度は90％に達します。

◎フランスのグローバル小売業者のカルフール人工知能プロジェクト、および米国のターゲット人工知能プロジェクトは、パーソナライズされたプロモーション、分類された最適化、カスタマイズされた表示などの洞察に富んだ販売に基づいています。

◎富士通と共同で人工知能プロジェクト指紋認証コンシューマシステムを日本のスーパーやデパートなどに適用、日本の楽天人工知能サービス自動質疑応答プロジェクト

◎日本の建設会社清水建設人工知能プロジェクト：極限環境における私の特許インテリジェント建設（NASA）プロジェクト

◎コマツの多目的ドローン・ブルドーザー自動化システム

◎独立行政法人産業技術総合研究所（AIST）が開発したHRP-5Pロボット開発プロジェクト

◎Jingdongの全工程無人倉庫輸送ロボットプロジェクト

◎神戸製鋼所のアーク溶接ロボットARCMAN™-GSの開発。

◎オムロンロボットプロジェクト

◎トヨタの「人体サポートロボット」（HSR）プロジェクト

◎シャープのロボホンプロジェクト

◎米国MITメディアラボの研究者と連携

◎アリババの新人ネットワークプロジェクト、パイトンロボットスモールGを開発

◎BIM +インターネット+ IoT技術を統合した中国建設技術構築インテリジェント建設プラットフォームは、オールラウンドでインタラクティブな情報伝達を実現できます。

◎トラスコ中山（株）トラスコ中山（株）ロボットIOT自動物流・SAPHANA※SAPRAを紹介日本のテレビ局たくやれ日本のテレビ番組が発表されました

◎長野計器のIOTプロジェクトでは、高齢者などが食物に巻き込まれるリスクを回避するため、医療用舌の嚥下能力をテストし、光ファイバーケーブルを使用して道路の老化速度を検出します。光ファイバーケーブルを使用しますたとえば、道路を横切るトラックが生成する振動の強さは、道路の経年劣化と橋の抵抗率を推測します。

日本のテレビ局日本テレビ番組がリリースされました

◎株式会社モノタロ人工知能プロジェクトAI顧客サービスシステム、商品AI分析システム

ERP、SAPおよびその他の方向性

◎約8年間のERPコンサルタントの実装経験、SAP（FI / CO、MM、SD、PP、BW、BI SAP HANA）およびその他のモジュールの経験、SAP R / 3（基礎）、SAP R / 3（ASAP）の経験SAP R / 3（在庫/購買管理）、SAP R / 3（管理会計）、SAP R / 3（財務管理）、SAP R / 3（生産管理）、SAP R / 3 BI認定コンサルタント

重要なプロジェクト経験

日本トヨタ自動車SAP輸入プロジェクト

プロジェクトの紹介：自動車業界におけるAI機械学習、特にディープラーニングおよびその他の人工知能分野

◎クラウドテクノロジービッグデータブロックチェーンは、人工知能、ディープラーニング、機械学習の方法と人工知能の分野でのアプリケーションに熟達しており、自然言語処理の自動質問と回答、マンマシン対話システム、感情分析、ソーシャルデータマイニングテクノロジーの専門家の経験が15年以上あります。

◎人工知能/インテリジェントドライビング/ AI +フィンテック＆ブロックチェーン/未来医療/ネットワークセキュリティAR / VRロボット開発者スマートハードウェア/モノのインターネット/ GAIRエクスペリエンス;

◎10年以上の組み込みソフトウェア開発の経験。

◎組込みソフトウェア開発（Linux / SCM / PLC / DSP ...）10年以上;

◎コンピュータビジョン、機械学習、人工知能、データマイニング、情報検索、自然言語処理、音声認識などの分野で、コンピュータサイエンスにおいてケンブリッジ大学と協力する。

◎人工知能の専門家であるGoogle、Microsoft、Amazon、facebook、netflix、Apple、Japan Toyotaおよびその他のプロジェクトと協力する。

◎人工知能システムの設計および開発に参加し、ディープラーニングテクノロジーを適用して、音声、ビデオ、画像、テキストなどのリッチメディアデータをモデル化および分析し、関連製品にインテリジェントサポートを提供し、ビジネス開発を促進します。

◎ディープラーニングの分野における最先端のアルゴリズムとフレームワークの追跡を担当し、CNNなどの典型的なディープラーニングモデルの使用シナリオと方法に精通した、エンタープライズアプリケーション用の大規模なディープラーニングコンピューティングプラットフォームを構築します。

◎ResNetやMobileNetなどの一般的なネットワーク構造に精通しており、画像分類、画像セグメンテーション、オブジェクト認識、その他の関連分野での実務経験。

◎TensorFlow、Caffe、MXNetなどの主流のディープラーニングフレームワークの1つ以上に精通している; ICIP、ICCV、CVPR、ECCV、SIGGRAPHなどの関連分野で公開されている一部の論文は、クラウドコンピューティングおよびクラウドサーバー管理で10年以上の経験を持っている

◎Alibaba Cloud、AWS、Azure、GCPなどに精通していること。

◎C / C ++ / Python / Javaに精通し、コンピューターネットワーク関連の知識に精通しており、クローラープロジェクトで実務経験が豊富で、スクレイピー、セレン、美しいスープなどのクローラーアーキテクチャツールを理解していること。

◎Hadoopエコシステム、およびSpark、HDFS、Hive、Impala、ElasticSearch、Cassandra、Kafkaなどのその他のビッグデータテクノロジーに精通していること。自然言語処理（NLP）ツールおよびコンポーネントにおける10年以上の経験。

◎Hadoop、Spark、Caffe、Tensorflowなどのオープンソースツールに精通している。実用的な開発経験がある。Java/ Python / C ++に精通している。ネットワークプログラミング、マルチスレッド、分散（Hadoop / Hive / Storm）に精通しており、10年以上の実務経験がある。

◎財務リスク管理、インターネットの不正行為、画像およびテキスト処理、精密マーケティング、推奨システムおよびその他の関連製品の実務経験、人工知能およびビッグデータのドライバーレス、深層強化学習、自然言語処理のテキストベースの画像合成などの側面プロジェクトは人工知能に焦点を当て、

ビッグデータおよびAR / VR、ADAS、携帯電話、ロボット、IOT、航空宇宙、その他の科学技術分野

◎人工知能は、データ、アルゴリズム、計算能力という3つの主要な要素によって推進されます。蓄積された大量のデータを通じて、GPUなどの高性能チップによってサポートされ、

ディープラーニングは、データの価値を掘り出し、人間の認識精度を超えるアルゴリズムを取得して、ディープラーニングの商用アプリケーションを実現できるため、人工知能はもはや学術研究に限定されなくなります。

◎コンピュータービジョン、機械学習、データマイニング、情報検索、自然言語処理、音声認識/合成、機械学習、コンピュータービジョン、自然言語処理、機械翻訳、音声認識/合成などの人工知能における25年の世界的大手企業職歴

◎プログラミング言語、Java、C / C ++、C＃、Pythonなどに精通している; NIPS、ICML、COLTなどのトップコンピューターサイエンス会議およびジャーナルで、

CVPR、ICCV、ECCV、IJCAI、AAAI、UAI、KDD、SIGIR、WWW、ACL、PAMI、IJCV、JMLR、AIJなどで発表された論文。

◎深層学習の学術工学プロジェクトにおける10年以上の経験。人工知能関連のアルゴリズムと理論、特にニューラルネットワーク、深層学習、強化学習、転移学習に精通している。SAS、R、Python、Spark SQL、Spark MLなどのデータ分析に精通している。ツールと言語、LR / GMM / SVM / CRF / MaxEnt / HMM / LDA / DNN / CNN / RNNの研究背景。

◎CUDAプログラミング、Python / Matlab / C ++、およびディープラーニングアーキテクチャ、マシンビジョン、バイオインテリジェンス認識およびその他の人工知能アルゴリズム、メディアフリーペイメントなどのコアテクノロジーなどの独立した知的財産権を使用するその他のコアテクノロジーに精通していること。 、AIチップ、スマートリテール、スマートシティ、スマートセキュリティ、スマート教育、航空宇宙日本の小惑星、軍事産業、およびその他の分野は、業界のリーダーとしてランク付けされた詳細なレイアウトを持っています。

◎人工知能は、製造（ロボット、最適化）輸送、生物学的ヘルスケア、通信、機械学習シミュレーション、エッジ機器、ネットワーク、高性能コンピューティング、製品開発（一般）、製品開発（視覚検査ソリューション）、人間とコンピューターの相互作用で使用されます（HCI、HRI）

重要なプロジェクト経験

◎バイオヘルスケア業界-グーグル、マイクロソフト、テンセント、アマゾンなどの優れたチームと協力して、私は京都大学ハーバード大学医学部の遺伝子と協力しています。ノーベル医学遺伝子iPS幹細胞チーム

2017/1-現在のAlibabaGroup契約ユニットHaniuqiao Intelligent Technology Guolong副ジェネラルマネージャーCTO

清華大学のコンピュータービジョン、マルチメディアテクノロジー、機械学習の教授。技術的メガトレンドとの統合、主要な分析手法は政府/地方自治体、教育/医療/ヘルスケア、金融、製造、物流、通信/放送、建設/不動産、電気/ガス/水道、ネットワーク、製薬で使用されています、農業、小売、製造、輸送、スポーツ、航空宇宙、広告、IOT、ICT、その他の産業。

AIレポータースポーツインテリジェントドライビングAI会計士AI外科ロボットAI尋問誤診AIロボット葬儀なしVR AR 3D AI犯罪追跡AI画像認識。中国科学技術協会、江蘇省カテゴリーAの最優秀賞は、浙江省政府主催のコンテストで優勝しました。偉大な健康産業：生物医学、医療機器、スマート医療、健康管理などがカテゴリーAの最優秀賞を受賞しました。

主な研究分野は、コンピュータビジョン、ビッグデータブロックチェーン、自然言語処理です。アリババグループのチーフテクニカルエキスパート

AIディープラーニング（画像認識と音声認識に使用される人工知能（AI）テクノロジーの1つ）によって発明されたラグビー5Gテクノロジーは、ディープラーニングを組み合わせ、カメラを使用して人体の動きをキャプチャし、AIの通過する身体を測定するラグビーゲーム分析システムを開発しました姿勢と動き、AIが5Gテクノロジーを導く

◎C / C ++およびその他の言語、Matlab、Qt、ROSおよびその他の開発ツール、LinuxまたはQNX開発環境の機械学習/データマイニングおよびその他のAI関連のアルゴリズムの研究開発に精通している;アルゴリズム関連のコードライブラリ、ツールライブラリのパッケージ化とリリース、AI関連のアルゴリズムパフォーマンスの最適化、エンジニアリング環境の展開、分散型ディープラーニングクラスターの構築と実装に参加する。

◎機械学習に関連する理論的知識と実践的スキルに精通しており、CNN、RNN、LSTMなどの典型的な深層学習モデルの使用シナリオと方法に精通していること。

◎TensorFlow、Caffe、MXNetなど、さまざまな種類の主流のディープラーニングフレームワークに精通しており、確かな数学とプログラミングのスキルがある。基本的な機械学習アルゴリズムに精通し、一般的な産業における機械学習のアプリケーションモデルを理解している。開発経験があり、ビッグデータ関連の知識に精通していること。

◎お客様のアプリケーションシステムのデータフローと処理方法に精通しており、ビッグデータプラットフォームHD / Hbase / Hiveなどの基本コンポーネントを構成する能力。顧客のニーズを独立して分析し、ソリューションを設計し、プロジェクト実装の完全な経験を持っている能力。

◎コミュニケーション能力に優れ、チーム連携や外部連携で発生する様々な問題を調整・解決する能力があり、企業関連の事業・製品・サービスのバックエンド開発・保守を担当します。

重要なプロジェクト経験

銀行、ヘルスケア、保険、金融技術、製造、小売、マーケティング、スポーツ分析、その他のプロジェクトに参加

◎武田薬品工業、富士フイルム、塩野製薬など日本の人工知能（AI）が新薬開発プロジェクトを推進

医療用DNAセルIPS人工知能プロジェクト-大阪病院および米国の他の主要病院は、人工知能、スマートセンサー、望遠鏡、検出器、医療機器の開発を含むIBMワトソン人工知能プロジェクトを実施しています。人工知能は私たちの言語をメンタルヘルスウィンドウ。高度なイメージセンサーにより、人間はスーパービジョンを準備できます。超拡大鏡により、人間は地球の無限の詳細を理解できます。すべての医療研究所システムは、単一のコンピューターチップに統合されます。インテリジェントセンサーは、光速で環境汚染を検出します。

◎AIケースIntel人工知能プロジェクト：Intelは、最適化された機械学習フレームワークとライブラリを通じてオープンソースへの取り組みを促進し、Nervanaシステムの機械学習の専門家と協力しています。

◎アメリカのGoogle人工知能プロジェクト：言語翻訳、視覚処理、ランキングと予測機能の人工知能開発

◎AIケースSalesforce人工知能プロジェクトチーム：Salesforcは人工知能を使用して、従業員がタスクをより効率的に実行し、作業効率を簡素化および加速するのを支援します。

◎AIケースAmazon Amazon ALexa人工知能プロジェクトおよびinit人工知能プロジェクト：

AWSクラウドプラットフォームに基づくディープラーニングに参加しているAmazonの人工知能サービスロボットAlexaの開発。金融ソフトウェアに適用されたAmazon SagemakerフレームワークUS INTUIT

◎また、神戸大丸人工知能プロジェクトに参画し、日本初の百貨店、神戸大丸のAIカスタマーサービス体制の導入に成功。今年6月に神戸元町の大丸百貨店では、2人の会話型AI女の子「MOTOMAQI」を雇い、家族や友人への贈り物を専門とする日本の中元や年末などのフェスティバルで特別なサービスを提供しました。

◎大栄スーパーにも参加しましたが、化粧品店の美容屋さんも「AIさくら」を使い始めました。「彼女」はお客様の質問にお答えするだけでなく、年齢・筋質・肌色・質問も確認できるからです。待って、自分のデータベースから同じ顧客グループの製品を見つけ、さまざまな女性に適した化粧品を提案します。

◎AI多要素マーキング建設現場プロジェクト：建設現場でのAIの適用は、機械学習、音声および画像認識を使用して、建設現場の写真とビデオに自動的にマークを付け、データを整理して検索できることを反映しています。たとえば、AIはディープラーニングを使用して画像と音声を分析し、建設データに自動的にタグを付けて、顧客に安全対策を積極的に提供できます。 AIは、設計、構築、運用、保守を容易にします。これは、設計、建設、管理など、建物のライフサイクルにも一致しています。

◎アリプロジェクト人工知能チップソフトウェア開発、日本イオングループAI事件、吉野家AIロボットプロジェクトへの参加、金融信用融資AI詐欺防止、返済督促AIシステム、野村證券消費者金融コア人工知能プロジェクト、スーパーマーケット盗難防止AI検出システム、警察庁顔認識AI捕虜システム、日本AI輸送システム、日本AI駐車自動システムおよびETC自動請求システムETC割引請求システム、スポーツトレーニングAIシステム、VR / ARシステム、HCI、HRIシステム、製造IOTシステム、目視検査ソリューション、野村證券のさまざまな情報の収集と分析のためのAIの使用、および国土交通省による分析のためのAIシステムの使用による観光による交通渋滞問題の緩和オリンピックAIカスタマーサービスシステムをホストしました。

人工知能、ビッグデータ、クラウド、その他の方向

◎ニューラルネットワーク、ディープラーニングの原則に精通し、一般的に使用されるビッグデータ分析プラットフォームとツール（python、R、SAS）を使用できる、ビッグデータモデルの構築における10年以上の実務経験、インターネットユーザーデータの取得、インターネットデータの関連する実務経験リスク管理とモデリング技術に関する専門的な研究。

◎クラウドテクノロジービッグデータブロックチェーンは、人工知能、ディープラーニング、機械学習の方法と人工知能の分野でのアプリケーションに熟達しており、自然言語処理の自動質問と回答、マンマシン対話システム、感情分析、ソーシャルデータマイニングテクノロジーの専門家の経験が15年以上あります。

1.プロジェクトチームの構築に全面的に責任を持ち、統一された効率的なチームを確立し、プロジェクトの進捗、品質、コスト範囲の管理と制御、技術指導とトレーニングに責任を持つ作業の熱意（10〜50人）を刺激する

2. TOYODAのお客様およびNTTDATAから認定されました（優秀なプロジェクトチームおよび優れたプロジェクトマネージャー個人賞）。

3. MIT、ハーバード大学、東京大学、その他の大学、三菱、NTTDATA金融研究所、日本銀行金融機関およびその他の関連機関との協力、深い人工知能の理論的基盤の形成を担当チーム

4.研究の方向性：マルチメディアテクノロジー、コンピュータービジョン、自然言語学習など

重要なプロジェクト経験

無人アスペクト

1. 2012 / 08-presentトヨタジャパンのスマートカー自動運転技術と日本のソフトバンク人工知能技術導入プロジェクト

2. NVIDIAの人工知能（AI）チップ自動運転チームと協力する

3.英国、ドイツのケンブリッジ大学の研究所と協力するMercedes-Benz Automotive Artificial Intelligence Project

4.自動運転技術/マツダ株式会社の研究開発プロジェクト

5.バイドゥ、モメンタ、ツーソンなどの自動運転プロジェクトの研究開発に参加する。

6.天津一汽が産業情報プロジェクトを導入

7. IBM AI技術チームと協力して、三菱、ホンダ、スズキなどを輸入します。

8.総合制御システム開発の分野

◎電子プラットフォーム技術・開発・設計：車両電装システム開発・アーキテクチャ設計、車載ECUハードウェア開発（機能・製造要求設計）

◎インフォテインメント・UI領域/早期製品開発：車載インフォテインメントシステムの開発（新世代の車載接続システム分野）など

◎ADAS、車両分野/初期製品開発：車両安全制御開発、車両制御ECUハードウェア、ソフトウェア開発など

◎MBDサポート/オペレーティングシステム/開発：シミュレーションは、モデルベースの自動車開発（HEVおよびEVを含む）、オペレーティングシステムの設計および開発などをサポートします。

◎自動運転技術/研究開発：画像解析、機械学習（DL /強化学習）、画像・距離センサー信号処理、新マップ開発

◎人間指向研究分野・既存技術開発：人間特性の仮説構築、実験計画の検証、測定手法の構築など

9.パワートレイン開発の分野

◎インバーター/ EV / PHEVの制御開発：油圧制御設計/次世代自動変速機の実験研究

◎運転・環境性能・開発：駆動力制御開発・運転実験・計測作業など

◎エンジン・xEV /制御開発：エンジンの電子制御システムや制御モデルの設計、ソフトウェア・ハードウェアの開発・設計など。

10.自動運転技術

◎車レベルの組み込みアーキテクチャに精通しており、パターン認識、マシンビジョン、ディープラーニング、パスサーチなどの人工知能テクノロジーに精通していること。

◎C / C ++、Python言語、Matlab、Qt、ROS、その他の開発ツールに精通しており、LinuxまたはQNX開発環境は、主な担当者として自動運転システム製品の開発経験があります。

◎自動運転に適した高精度マップ要素のタイプと精度の要件に精通し、レーザー点群フィルタリング、マップフィーチャー抽出、マップ構築などのアルゴリズムを習得します。C/ C ++およびその他の言語、OpenCL、PCLおよびその他の開発ツール、LinuxまたはQNX開発環境に精通しています。衛星ナビゲーション、ディファレンシャルポジショニング、慣性ナビゲーションなどの基本原則、主流の統合ナビゲーションシステムの熟練した使用、移動オブジェクトの軌道推定に関連するアルゴリズムの習得。

◎C / C ++およびその他の言語、Matlab、Qt、ROSおよびその他の開発ツール、LinuxまたはQNX開発に精通していること。

◎車両と基地局での統合ナビゲーションシステムの選択、展開のキャリブレーションとテストを担当し、統合ナビゲーションシステムのデータ分析、車両軌道推定アルゴリズム、ナビゲーションとポジショニングフュージョンアルゴリズムの開発とテスト、自動運転とADASの分野で少なくとも12年間の責任者上記の実務経験、LKS、ACCなどの製品開発経験があること。

◎車両のステアリング、駆動、ブレーキ、サスペンションシステムの動作メカニズムに精通しており、プレビュートラッキング、PID、ファジー制御、MPCなどの一般的な車両運動制御アルゴリズムを習得し、車両の動的特性に従ってアルゴリズム設計を最適化できます。

◎C / C ++やその他の言語に精通している、Matlab、Prescan、CodeWarrior、およびその他の開発ツールは、自律車両の縦および横運動制御アルゴリズムの開発とテストを担当します

2017 / 1-現在CTO、シニアテクニカルエキスパート、AIソリューション

中国の東部および南部市場でAIプロジェクトを推進するテクノロジーリーダーとして、中国のテクニカルサポートチームにおける人工知能/機械学習ソリューションの最初の技術エキスパート

•EdgeおよびData Center AIソリューションのプリセールスおよびアフターセールスのテクニカルサポートを提供します。

•必要に応じて、概念実証、ターゲットリファレンスデザイン、スキーム最適化

•人工知能ソリューションに関する技術トレーニングを提供する

•プロジェクトの実装をスピードアップするために、顧客と研究開発部門をつなぐ

•R＆D部門と協力して、人工知能ソリューション（フィードバック、欠陥レポート、内部テストなど）を改善する

•内部AI / MLサーバーを確立して維持する（Docker環境）

製品アプリケーションのシニアエンジニア

•中国のグローバル組み込みツールメソッド導入チームで唯一のエンジニアであり、主要なプロジェクトや主要な問題に対する新しいテクノロジーの推進、トレーニング、技術サポートなど、中国でのチームの関連作業を担当しています。

•組み込み製品（FPGA SoC）ソリューションを顧客に提供することに焦点を当てます。ソフトウェアツール、メソッド、およびテクニカルソリューションデザインサービス（PoC）を顧客と内部チームに提供します。

•ツールと製品ソリューションの改善を支援するために、製品の技術フィードバックをR＆Dチームに提供します。

•PoC設計、オフサイトおよびオンサイトサポートを通じて、創造的な作業がお客様に幅広いシステムレベルの問題を直接提供し、主要なプロジェクト開発とオンサイトチーム間の技術的なギャップを埋めます。

•スコープは、ザイリンクスエンベデッド製品の完全なソフトウェアスタックと、SOC上のFPGAとのハードウェアおよびソフトウェアの共同設計をカバーしています。

プロジェクトの要点は次のとおりです。

•Tier1のお客様がLinuxシステムソリューションを展開できるよう支援します。 Xilinx PetalinuxプロセスをYoctoプロセスに移行するようにお客様をガイドします。 uboot、linux、rootfsなどの主要な問題を明確にし、顧客の緊急のプロジェクト進捗目標にタイムリーに到達します。

•PSのみのリセットおよびPLオーバーレイデザインをカスタマイズします。

•EVBでのRT Linuxの展開とパフォーマンス評価。

•Xen仮想化に基づくAMPシステム導入のリファレンスデザインを完成

•EVBへのOpenWRTおよびサードパーティUSB WiFiの展開の成功。

•複数のネットワークポートのパフォーマンス評価と最適化。

•armプラットフォームに基づくsmartNICプロジェクトでのDPDK PMD（ドライバー）設計の独立した開発とカスタムDMA IPのテストは、社内で最初のarmプラットフォーム実装です。

•LinuxカスタマイズアプリケーションのFreeRTOSへの移植を個別に完了します。

•完成した技術トレーニングと複数のFPGA HLSアクセラレーションソリューションのサポート

•複数のプロジェクトでFFmpeg、GStreamer、XfOpenCVおよびHLSテクノロジーの事前調査とPoC実装を完了するために、ローカルテクニカルサポートチームを支援して、プロジェクトの着陸を加速します。

•Dockerテクノロジーをクリエイティブに使用して、お客様の環境へのツールの導入を完了する

•Dockerテクノロジーをクリエイティブに使用して、会社のデータセンターFPGA機械学習ソリューションの導入を完了

Qualcomm ProjectシニアLinuxソフトウェアエンジニア

車載Linux / Android BSPの開発とメンテナンスに参加する

•イーサネット（MAC / PHY）モジュールは、プログラムの開発、パフォーマンスの最適化を実行、検証、駆動、テストします。

•systemdに基づくシステムネットワーク構成。

•Open-AVBスタックを移植し、AVBデモの開発とAVBテスト環境の構築に参加します。

•BSPアップグレードの大量生産機能モジュールの開発と保守。

•UbootおよびLinuxのSPI NORドライバー開発。

•BSPプロジェクトの顧客テクノロジをリードし、顧客が一部の機能（回復、MTP、FM / RDSなど）をカスタマイズするのを支援する

•LTPをBSPに展開します。

•BSPの大量生産およびアップグレードとリカバリソリューションの設計とメンテナンス

•BSPの管理に役立つテクニカルサポート

半導体プロジェクト（Linuxドライバーエンジニア

•USB3.0コントローラー（xHCI）ドライバーの開発と保守。

•Linux OSベンダー（Ubuntu / Suse / Redhat）にテクニカルサポート（xHCI / ACPI）を提供します。

•チップセットドライバーとカーネルRPM、DEBインストールパッケージをリリースします。

•xHCI / EHCI / ACPI関連のドライバーをLinuxアップストリームに提出する

HuaweiプロジェクトLinuxドライバーエンジニア

Linuxドライバーの開発と保守、および会社のPCIおよびUSBデジタル/アナログTVカードの関連アプリケーションプログラムを担当します。

•USB TV受信カード用のLinuxドライバーの開発とメンテナンス。このドライバーは、NTSC / PAL / ATSC / DTMBなどのアナログおよびデジタルTVフォーマットをカバーする複数のハードウェアソリューションもサポートしています。

•PCI TV受信カード用のLinuxドライバーの開発とメンテナンス。このドライバーは、NTSC / ATSCなどのアナログおよびデジタルTVシステムをカバーするさまざまなハードウェアソリューションもサポートしています。また、ALSAアーキテクチャに基づくサウンドカードドライバモジュールも含まれています。

•I2C EVK（評価ボード）Linuxドライバーの開発とメンテナンス。このボードは、セットトップボックスのリファレンスソリューションのデバッグと検証に使用されます。

•LinuxプラットフォームI2Cコントローラーアプリケーションの開発。このツールは、ハードウェアのリアルタイムのデバッグと検証のためにI2C EVKドライバーと組み合わせて使用​​されます。 QT開発GUIとドライバーとの対話用の低レベルインターフェイスを担当します。

Linuxソフトウェアエンジニア

•Linuxのデバイスドライバーとカーネルのトレーニング、Linuxカーネルのトレーニング、QAトレーニングなど、会社のソフトウェア開発ドキュメントと品質管理プロセスに精通した従業員の技術トレーニングを支援して参加する。

-Linuxタスクスケジューラの変更

-メモリリーク検出ツールを実装する

•PXA270組み込み開発ボード用のALSAアーキテクチャに基づくAC97 Linuxサウンドドライバーを開発します。

自動車技術プロジェクト

役職：シニアソフトウェアエンジニア

職責：

X Xiaopengが独自に開発したスーパーチャージングパイル（ARM + Linux）ソフトウェアアーキテクチャを担当。

the新しいアーキテクチャ（基本ライブラリ、ドライバーレイヤー、プラットフォームレイヤー、アプリケーションレイヤー）でのソフトウェア開発を担当します。

basic基本的なライブラリ、ドライバー層、プラットフォーム層のコード作成の完了に焦点を当てています。

Guangdian Express Financial Project

顧客プロファイル：Guangdian Expressは、通貨処理装置およびシステムソリューションの世界有数のプロバイダーであり、中国で最大です

最大のATM（Automated Teller Machine）製品およびシステムソリューションプロバイダーであり、最も強力なAFC

（自動チケット検査システム）機器およびコアモジュールのプロバイダーは、中国で最も専門的な金融サービスプロバイダーでもあります。

請負業者であり、最も強力な現金インテリジェント処理の専門家です。

職位：シニアエンジニア/ディレクター（組み込みソフトウェアの方向性）

職責：

drive紙幣認識モジュールのハードウェアプラットフォームのドライブ開発とデバッグ、および認識スケジューリングプロセスの開発を担当します。

debuggingデバッグとデータ収集を実現する新しいテクノロジーと新しいセンサードライブ。

I2C、SPI、UART、PCIE、GPIO、USB、イーサネット、カメラ、WIFI、FPGA、CIS、磁気センサーなど。ドライバー開発および対応するホストコンピューターテストソフトウェア開発。

ARMベアコアおよびDSPプラットフォームハードウェアのパフォーマンス最適化作業。 5.ハードウェアプラットフォームのドライブソフトウェアアーキテクチャと最適化、UTおよびSTが機能します。

HKUST Xunfeiプロジェクト

顧客プロファイル：HKUST Xunfei Technologyは、プロのオーディオおよびビデオ機器のリーディングプロバイダーです。同社の独立した研究開発、生産、販売

主にデジタルTVのフロントエンドをベースにしたプロフェッショナルなAV機器を販売しています。有線、無線、衛星およびその他の従来の電力

ビデオおよびネットワーク企業は、ワンストップのエンドツーエンドのソリューションだけでなく、OTTやIPTVなどの新しいメディアも提供しています

サービスプロバイダーは優れたシステムサービスを提供します。

役職：シニアソフトウェアエンジニア

職責：

ARM Linuxドライバーおよびザイリンクスzynqプラットフォームのuboot実装。

Magマグナムコーディングチップに基づく標準解像度および高解像度ビデオコーディングスキームの設計とマルチオーディオスキームの設計を完了します。

IP IPD-CMMプロセスに従ってすべてのUT、ST、BBIT作業を完了し、すべてのコードの記述、UT / STテスト、および共同デバッグを完了します。

Huawei Technology Co.、Ltd. Hangzhou Institute Project

職位：ソフトウェアエンジニア

職責：

subsequent独立して完成したVxworks、Linux、DSPモジュールドライバー、およびモジュール設計ドキュメント。その後の開発とメンテナンスをガイドします。

IP IPD-CMMプロセスに従ってモジュールのコーディングとテストケース設計を完了します。

IP IPD-CMMプロセスに従って、UT、ST、BBITの作業を完了します。

difficultプロジェクトの困難な問題に参加し、特別なテストとコードレビューの組織化と参加に主導的な役割を果たすことができる。

スーパーチャージパイルソフトウェアアーキテクチャ+ハードウェアプラットフォーム開発

プロジェクトの概要：ARM + Linuxプラットフォームに基づく課金パイルアーキテクチャと開発。

役職：シニアソフトウェアエンジニア

職責：

charging課金パイルソフトウェアの階層化アーキテクチャの設計、ソフトウェアアーキテクチャドキュメントの出力、AICPUファームウェアシステムフレームワークの開発、コンピューティングチャネル、DFXの研究開発、商用配信。

driverドライバーレイヤーソフトウェアのコーディング（I2C、SPI、GPIO、USB、PWM、CAN、ADC、WIFI、4Gの駆動）;

基本的なライブラリコーディング作業（タスク、タイマー、メッセージキュー、共有メモリ、アプリケーションフレームワーク、構成ファイル分析、その他のモジュール）;

old古いアーキテクチャでのソフトウェア開発作業の一部（ネットワークノードの選択、OTAアップグレード、アプリケーションガードなど）。

仕事のパフォーマンス：

platformプラットフォームの下の階層化されたアーキテクチャを完成させ、各層で明確に作業を分担しました。これは、その後の拡張やメンテナンスに便利です。

the基本ライブラリとハードウェアドライバーレイヤーの準備、およびプラットフォームレイヤーの適応を完了しました。

each各フレームワークで実装された開発フレームワークとテンプレートを定義し、テンプレートの実装方法に従って後続の新しいハードウェアドライバーを追加できるため、新しい機能の反復が容易になります。

ARMプラットフォーム紙幣認識モジュールの開発

プロジェクトの概要：アルテラのCyclone SOC、TI C6654およびAllwinner V5 ARMチッププラットフォームハードウェアドライバーと

プラットフォーム開発。

役職：プロダクトエンジニアリンググループ長

職責：

ARM TI C6455プラットフォームのARMプラットフォームソフトウェア検証作業へのアップグレード。

softwareソフトウェアの評価、スキームの検証、および新しいセンサーとハードウェアプラットフォームのドライブ設計を担当します。

ARM ARMプラットフォーム（Cyclone SOC + TI C665 / Allwinner V5）チップハードウェアドライバーおよび認識プラットフォームのソフトウェア開発を完了しました。

preプレローダー、Uboot、Linuxカーネルのカスタマイズ、切断、適応を完了します。

function機能統合作業のためにアルゴリズム部門とドッキングする責任があります。

the新しいハードウェアプラットフォームのソフトウェア開発が完了し、新しいプラットフォームの少量バッチ検証と量産導入を担当します。

仕事のパフォーマンス：

ARM ARM認識プラットフォームソフトウェアの統合アーキテクチャが5つのムーブメントで促進および使用されているため、元のモジュールと比較してコストを20％削減できます。新しいソフトウェアアーキテクチャはレイヤリングの原則に従っており、後で他のハードウェアプラットフォームを簡単に移植できます。

TI C6455 / OMAP138プラットフォーム紙幣認識モジュール開発

プロジェクトの紹介：TI C6455 / OMAP138紙幣認識モジュールソフトウェア開発に基づく。

役職：シニアソフトウェアエンジニア

職責：

OMAP138プラットフォームのアップグレードTI C6455プラットフォームハードウェアドライバーの適応作業。

DSP DSPプラットフォーム（DDR2、SPI、I2C、UART、ADC、FPGA、CIS）のドライバー開発を完了します。

ハードウェアの取得とさまざまなセンサー（CIS、厚さ、磁気）画像の補正。

CACHEパフォーマンスの最適化とソフトウェアの正規化作業。

image画像取得およびその他のホストコンピュータテストツールをコンパイルします。

仕事のパフォーマンス：

the同社最大の流通モデルの紙幣識別モジュールの開発と保守を保証し、マルチスペクトルの新しい識別プラットフォームのソフトウェア開発と生産導入を完了し、プラットフォームの正規化作業を推進し、C6455プラットフォームの各モデルコードの正規化を完了しました。統一された制御プロトコルが策定され、後続のすべてのモデルは一連の制御プロトコルを共有してメンテナンスを容易にします。さまざまなモデルツールに統合するために、テストツール用の外部標準APIインターフェイスが作成されています。

ARMプラットフォーム向け高精細エンコーディングビデオボードの開発

プロジェクトの紹介：ZynqプラットフォームARM Linuxドライバーとuboot実装に基づいて、アプリケーションソフトウェア

今。

役職：シニアソフトウェアエンジニア

職責：

U UBOOT起動ガイドを完了します。

ペリフェラルバスドライバの完全な適合（PCI、SPI、I2C、FLASH、DDR）。

Microマイクロンエンコーディングチップのアプリケーションソフトウェア（HD、SD、ロゴ挿入）が完成しました。

仕事のパフォーマンス：

高品質は、計画に従って必要な機能を完了し、革新的にロゴ挿入のアップグレードおよびプロダクションスキームを設計し、プラットフォームマシンのアプリケーションスキームとして使用しました。プロジェクト開発では、Huaweiの以前のプロジェクトのIPD-CMMプロセスの経験を使用して、STユースケースを積極的に設計し、テストを完了しました。テスト後、リーダーシップによって注目され、プロジェクトチームで促進されたソフトウェアの問題はほとんどありませんでした。

Vxworks + Linux PowerPC BSP開発

プロジェクト概要：Vxworks6.8 + WindRiver Linuxに基づいて、Freescale P304およびP1012を完成させます。

ハードウェアボードBSP開発。

ポジション：Huawei第2レベルソフトウェアエンジニア

職責：

V狭帯域アクセスチップBRI、E1 / T1、およびVxworksおよびLinuxでの狭帯域スイッチングモジュールのドライバー開発を完了します。

完全なVxworksブートロードとLinuxブートプロセス開発。

small小規模システム（CPU、DDR、FLASH、ネットワークカード、オペレーティングシステム）、デュアルBIOS、USB、CFカード、UART、SPI、I2C、USB、FLASHなどの周辺機器ドライバーの完全な開発。

完全なDSPハードウェアドライバーの開発。

ジョブパフォーマンスtheコンパイルと起動ロードプロセスの高品質な完了、およびシングルボードの電源投入時起動デバッグ、2つのバージョンの後のシステムの狭帯域モジュール設計の完了、モジュールは安定しています。開発プロセスでは、LinuxおよびVxworksドライバーの開発とデバッグの方法を習得し、カーネルモジュール機能を実行するためのシェルに基づくUTツールを開発し、会社の自動テストで使用しました。 MDEの役割として、ソフトウェアシステムの設計に携わっています。音声メディアボードで使用されるDSPドライバーは、以前に開発されたShannon DSPドライバーであり、ドライバーコードのこの部分を維持し、新しい従業員をトレーニングするように同僚に指示し、正規化を正常に完了しました。

TiマルチコアDSP TMS320C6678開発

プロジェクトの概要：Ti BIOSオペレーティングシステムに基づいて、DSPドライバーとパフォーマンス検証が実装され、DSPコーデックが完成します

処理性能の評価。

ポジション：Huaweiファーストクラスソフトウェアエンジニア

職責：

DSP DSPマルチコアスタートアップ、クロック、電源、DDR3、ナローバンドアクセス（TSIP）、PCIE、HyperLink、ネットワークポート、ハードウェアRTP暗号化のモジュールドライバーを実現します。

each各モジュールの機能テストを実現し、プロジェクトの承認基準を満たします。

仕事のパフォーマンス：

projectプロジェクト開発を独立して完了し、新しいコードは10Kを超え、DSPは社内の新しいデバイスです。プロジェクトの結果は、会社レベルのプラットフォームアプリケーションとして使用され、4つのハードウェアプラットフォームの商品化をサポートします。

DSP Media Gateway VOIPの開発と保守

プロジェクトの概要：Ti DM648チップとPowerPCに基づくVOIPソリューションを開発して、中小規模のユーザー向けのハードウェアを実現する

プラットフォーム;元のプラットフォームのメディアゲートウェイを維持します。メディアゲートウェイソリューションは、Ti DSP TMS32054Xチップ（DTMF、FSK、MFC、信号トーンを実装）+ MNDSPEED MPC82610チップ（G711、G729、G723を実装、RFC2833、RFC2198、T38会場システム）で構成されます。 。

ポジション：Huawei Software Engineering

職責：

development開発プロジェクトは、音声メディアゲートウェイのDSPコアスケジューリングの移行計画を完了し、DSPチップドライバー、PowerPCのPCIドライバー、DSPロードモジュール、DSPエージングおよび機器モジュール、PowerPCのVOIPスケジューリングスキームを完了しました。

platformプラットフォームのメンテナンスとメンテナンスプロジェクトの新機能の開発を主に担当し、オンラインの問題をタイムリーに特定し、根本原因をすばやく特定して解決策を提供します。

仕事のパフォーマンス：

メンテナンス作業中に、困難な問題を迅速に解決でき、測位効率が高く、問題が解決され、技術的なバックボーンに成長しました。同時に、メンテナンスを通じて、ユーザーにより近づき、ユーザーのニーズに関心を持ち、その後の開発作業のための優れた基盤を築きます。

開発プロセスでは、開発プロセスに厳密に従い、ニーズ分析と要約設計を完了し、コーディングとUT、STの作業を完了し、作業のコード検査を整理して、開発品質を確保します。同時に、能力が成長するにつれて、プロジェクトのインターフェース担当者として、彼は周囲のプロジェクトチームとの問題の調整と解決を担当します。

コンピュータービジョン、マルチメディアテクノロジー、機械学習、清華大学の教授。テクノロジーのメガトレンドとの融合、最先端の分析テクノロジーは、政府/地方自治体、教育/医療/ヘルスケア、金融、製造、物流、通信/放送、建設/不動産、電気/ガス/水、ネットワーキング、医薬品で使用されています、農業、小売、製造、輸送、スポーツ、航空宇宙、広告、IOT、ICT、その他の産業。

AIレポータースポーツインテリジェントドライビングAI会計弁護士AI手術ロボットAI尋問誤診AIロボット葬儀VR AR 3D AI犯罪追跡AI画像認識。中国科学技術協会、江蘇省、Z江省人民政府がコンテストを主催し、カテゴリAで最優秀賞を獲得しました。大きな健康産業：生物医学、医療機器、スマート医療、健康管理などがカテゴリAで最優秀賞を受賞しました。

彼の主な研究分野は、コンピュータービジョン、ビッグデータブロックチェーン、自然言語処理です。 Alibaba Groupのチーフテクノロジースペシャリスト

AIディープラーニング（画像認識と音声認識に使用される人工知能（AI）テクノロジーの1つ）によって発明されたラグビー5Gテクノロジーディープラーニング、カメラでの人間の動きの撮影、AIを通過する身体の測定を組み合わせたラグビーゲーム分析システムを開発姿勢と動き、AIガイド付き5Gテクノロジー

· AI研究開発

· 深層学習（Deep Learning）を中心とした人工知能（AI）技術を活用した、取り組む事業における研究開発を行って頂きます。

◆具体的な業務内容

・コンピュータビジョン・自然言語処理・強化学習・音声認識/合成分野での人工知能（AI）技術を活用した研究開発

・レコメンドシステムの研究開発

・ゲームAIの研究開発

・その他事業での人工知能（AI）技術を活用した研究開発

· ◆経験／能力

・chainer / tensorflow / caffe 等の深層学習フレームワークを利用した20年以上の実装経験

・高い論理的思考力

・数学的な深い知識（線形代数・統計/確率・微積分）

・自発的に周囲を巻き込み、プロジェクトを推進することができるマインド

・コンピュータビジョン・自然言語処理・強化学習・音声認識/合成分野での5年以上の研究または実務経験

・深層学習（Deep Learning）のモデル実装経験

・自発的に周囲を巻き込み、プロジェクトを推進することができるマイン

· AI事業において、AIシステム開発チームのプロジェクトマネージャーとして、AIプロジェクトの設計、進行管理、ディレクション全般に関わっていただきます。

· ◆具体的な業務内容

· AIプロジェクトマネージャは、AIシステム部の取り組む各種AIプロジェクトに関して、事業部と期待値のすり合わせをしながらAI研究開発のアウトプットを定義し、適切に状況把握、計画調整しながら不確実性の高いAIプロジェクトを成功に導きます。また、AIシステム部メンバーの技術成長ニーズや現状のレベルを把握した上で、適切な業務アサインを通してメンバーのキャリア形成を支援します。

· ◆経験／能力

· ・システム開発におけるプロジェクトマネージメント経験

・多岐にわたるタスクを整理する管理能力

・メンバーへの指示を明確にできる高いコミュニケーション能力

・課題を解決するために必要な巻き込み力

・リスクを察知し対応策を練ることのできる危機管理能力

· ・機械学習アルゴリズムに関する研究開発経験

・機械学習アルゴリズムを用いた実サービスのプロジェクトマネージメント経験

・基本的なWEB技術力(プログラミング・DB・インフラの経験)

・システム開発グループの組織マネージメント経験

・社外ステークホルダーとの交渉経験

※各プロジェクトの規模・期間、利用経験のあるマネージメントツール、取り入れていたマネージメント手法など、プロジェクトマネージメント

学術

論文1.瞬時感情分析に関する関連研究は、MITの米国レビューおよび英国のデイリーメール（2019）を含む国際的な主流のテクノロジーメディアによって報告されています。

2. ICCV 2019（口頭承認率4.3％）を含むいくつかの重要な国際会議で口頭報告資格を取得しました。

3.重要な国際会議のエリアチェアとして、ACM MM 2020、IEEE IPTA 2016、IEEE PCSPA 2011。

4.主要な国際学術会議で5つのセミナーを開催する：IEEE CVPR 2020、ACCV 2016、IEEE FG 2018/2019/2020

5. Elsevierジャーナル「Pattern Recognition」（2017、最新のインパクトファクター5.898）、「Neurocomputing」（2017、最新のインパクトファクター3.317）により「Outstanding Reviewer」を受賞

6.かつて国際ジャーナルSpringer「Signal、Image and Video Processing」（SIVP）（2019）のゲスト編集者を務めた。

7.ビジュアル言語コーディングに関するレビュー記事「ビジュアルスピーチデコードの最近の進歩のレビュー」が、エルゼビアの国際ジャーナル「Image and Vision Computing」によって「Editor's Choice Article」（2014）としてリストされました。

8.共同インストラクターとして、第6回中国大学院スマートシティテクノロジーおよびクリエイティブデザインコンペティション（2019）で2つの最優秀賞を受賞しました。

賞と栄誉：

1. 2018年ナチュラルサイエンスアワードの最優秀賞;視覚的ロバストな特徴抽出と非線形分析;すべて完了：Lai Jianhuang、Zheng Weishi、Xie Xiaohua、Ruan Bangzhi、Wang Changdong、Zhu Junyong、Ma Jinhua、Huang Jian;完了ユニット：Sun Yat-sen University、香港バプテスト大学。

2. 2016年の科学技術革新における一流の若い才能。

3. 2016 IEEE-Outstanding Youth Science Foundationの受賞者。

4. 2015年の中国人工知能学会の優秀な博士論文。

5. 2014年中国コンピュータ協会優秀博士論文推薦賞。

6. SIAM SDM 2013学生旅行賞。

7. 2012 Microsoft Research Asia（MSRA）フェローシップノミネート賞。

8. IEEE ICDM 2011学生旅行賞。

9. IEEE ICDM 2010最優秀研究論文賞を受賞しました。

10. IEEE ICDM 2010 Student Travel Award。

研究プロジェクト：

1）ケンブリッジ大学の2019年の基礎科学研究事業手数料-新たな学際的学際的資金プロジェクト、脳機能リハビリテーションシステムの確立、および脳波データの分析に基づく蝸牛移植後の聴覚障害患者の臨床実証アプリケーション-2020.12、ホスト。

2）2019中国国家自然科学財団-一般プロジェクト、類似性学習に基づく異種データクラスタリングアルゴリズムの研究および適用、No。61876193、2019.01-2022.12。

3）2019 National Key R＆D Program Project "Community Risk Monitoring and Prevention Key Technology Research" Topic 5 "'Data-Calculation' Indpth Interactive Community Risk Scenario Calculation and Forecasting Technology"、No.2018YFC0809705、2018.07-2021.06、Topic 5 Zhongshan大学長。

4）参加者が主催する、2019年の大学生のイノベーションと起業家精神プロジェクトのための包括的な情報サービスプラットフォームの構築「大学イノベーションと起業家教育プロジェクト」、No。2019PT204、2019.01-2020.12。

主な学術：

1）準編集者

-人工知能研究ジャーナル（JAIR、CCF B、2019年8月以降）。

2）会議の共同議長：

-PRCV 2018、ウェブサイト共同議長。

3）プログラム委員会メンバー：

-IEEE ICDM 2014、2015、2016、2018、2019。

-AAAI 2017、2018、2019、2020。

-KDD 2019、2020。

-IJCAI 2019、2020。

-CIKM 2019。

-IJCAI 2018デモトラック、IJCAI 2019デモトラック、IJCAI 2020デモトラック。

-第8回ビッグナレッジに関するIEEE国際会議（IEEE ICBK）2017。

-ビッグデータ会議の第4回IEEE国際会議2015。

4）レビューア：

-IEEE TPAMI、IEEE TCYB、IEEE TKDE、IEEE TNNLS、JMLR、IEEE TII。

-パターン認識、ニューラルネットワーク、ニューロコンピューティング、知識ベースシステム、情報科学、KAIS。

-他の多くの優れたジャーナル...

作品：

1. Google学者のホームページをご参照ください：https://scholar.google.com/citations?user=Vkzd7MIAAAAJ&hl=zh-CN [pdf]

出版サービス：

•コンピュータサイエンスフロンティアの編集者（2018年-）

•IEEE Trans。On Image Processing（2015-2018、CCFによるランクAジャーナル）の編集者

•Journal of Computer Vision and Image Understanding（2017-）の編集者

•パターン認識レターの編集者（2017-）

•ニューロコンピューティングの準編集者（2012-2016）

•EURASIP Journal of Image and Video Processingの編集委員

•コンピュータビジョンとアプリケーション（CVA）に関するIPSJトランザクションの編集者

•IET Computer Vision（2020-）の編集者

会議サービス：

•顔とジェスチャーの認識2020（FG2020）に関するIEEE会議のエリアチェア

•第34回人工知能に関するAAAI会議（AAAI-20）の上級PC

•IEEE / CVF Conference on Computer Vision and Pattern Recognition 2020のエリアチェア

•2019年コンピュータービジョンとパターン認識に関するIEEE / CVF会議の議長

•IEEEバイオメトリクスに関する国際会議のエリアチェア：理論、アプリケーション、システム（BTAS 2018）

•IEEE顔とジェスチャーの自動認識に関する国際会議のエリアチェア（FG 2018）

•コンピュータビジョンに関するアジア会議（ACCV）2018のエリアチェア

•コンピュータービジョンに関する国際会議（ICCV）2011のエリアチェア

•バイオメトリック認証に関する中国会議のプログラムチェア2014、2015、2016

•2012年パターン認識に関する国際会議（ICPR）エリアチェア

•コンピュータビジョン（ACCV）2012に関するアジア会議のエリアチェア

•顔とジェスチャーの認識に関する国際会議のエリアチェア（FG2013）

•コンピュータビジョンに関するアジア会議（ACCV）2014のワークショップの議長

•2016年アジアンコンピュータービジョン（ACCV）会議のエリアチェア

•2014年パターン認識に関する国際会議（ICPR）エリアチェア

1.機械学習：深層学習とそれ以降

複雑なデータ条件下での機械学習の方法とテクノロジーに焦点を当てます。特に、小規模で弱くラベル付けされた、半教師付きの、完全ではない、純粋でないデータ条件下での機械学習方法です。研究のアイデアには、転移学習、メタ学習、知識ガイダンスが含まれます学習アルゴリズムなど

私のチームは、複雑なデータ条件、特に小さなデータ、Wealyのラベルが付けられたデータ、半教師付きデータ、不完全なデータを含むシナリオの新しい機械学習手法に焦点を当てています。ここで、新しいモデルと新しい最適化手法が設計に必要です。方法論の観点から、転移学習、メタ学習、知識に基づく学習。

2.顔認識から人間の深い理解まで：方法、テクノロジー、アプリケーション

顔の検出と追跡、顔の主要な特徴点の配置、顔の認識と検証、表情の分析と認識、顔の属性の推定、唇の認識、心拍数の推定、集中度の推定、視力の推定と追跡などに従事。コンピューティングに関連する研究トピックに関する深い理解。アプリケーション協力部門には、Huawei、Ping An、Baidu、Yinchen Technology、Samsung、Omron、Panasonic、Qualcomm、China Mobileなどが含まれ、2016年と2017年にHuawei優れた協力実績賞を2回受賞しました。関連する業績は、2015年に国立自然科学賞の2位、2005年に国家科学技術進歩賞の2位を受賞しています。

私のチームは、顔認識から人間の理解に至るまで、あらゆる種類の視覚タスクに興味があります。これには、顔の検出と追跡、顔のランドマークの特定、顔の位置合わせ、顔の識別、顔の検証、顔の検索、表情の認識、顔の属性が含まれます（ただし、これらに限定されません）。推定、3D顔再構成、顔解析、読唇術、心拍数推定、婚約推定、注視追跡など。Huawei、Ping'an、Qualcomm、China Mobile、Baidu、Isvision、Samsungなどの産業パートナーと幅広い協力関係があります。オムロン、パナソニックなど。特に、顔認識技術は、HuaweiスマートフォンやHuawei Cloudアルバムで使用されています。

3.一般的なオブジェクトの検出、セグメンテーション、および認識

一般的なターゲットの検出、セグメンテーションと認識などのコンピュータービジョンタスクに焦点を当て、ビデオ監視シナリオの歩行者の検出と追跡、人間の姿勢推定とセグメンテーション、車両の検出と追跡、歩行者の再認識、車両の再認識などの研究トピックに焦点を当てます。

私のチームは、一般的なオブジェクトの検出、セグメンテーション、認識、特に歩行者の検出と追跡、人間の姿勢の推定と身体のセグメンテーション、車両の検出と追跡、人間と車両の再識別などにも関心を持っています。これらのテクノロジーはビデオ監視に適用されます。

1.複雑なサンプル条件下での深層学習の方法と手法

プロジェクトのタイプ：科学技術省の主要なR＆Dプログラムプロジェクト（プロジェクト）

プロジェクト時間：2018-2022

プロジェクトリーダー：guolong（プロジェクトリーダー）

2.ビデオビッグデータ多物体検出技術協力プロジェクト

プロジェクトのタイプ：エンタープライズ協力プロジェクト

プロジェクト時間：2016-2017

プロジェクトリーダー：guolong

3.顔認証技術連携プロジェクト

プロジェクトのタイプ：企業協力（Huawei）

プロジェクト時間：2016-2019

プロジェクトリーダー：guolong

4.視覚パターン分析と認識

プロジェクトのタイプ：資金委員会優秀青年プロジェクト

プロジェクト時間：2013-2015

プロジェクトリーダー：guolong

■ References 研究成果

[1] Guolong et al. "3D convolutional neural networks for

human action recognition." IEEE transactions on pattern

analysis and machine intelligence 35.1 (2013): 221-231.

[2] Guolong Ng, Joe, et al. "Beyond short snippets: Deep

networks for video classification." Proceedings of the IEEE

conference on computer vision and pattern recognition. 2015.

[3] Guolong et al. "Temporal segment networks: towards

good practices for deep action recognition." European

Conference on Computer Vision. Springer International

Publishing, 2016.

[4] Guolong et al. “Large-scale video classification

with convolutional neural networks.” Proceedings of the IEEE

conference on computer vision and pattern recognition. 2014.

[5] Guolong et al. “Multi-Stream Multi-Class Fusion of

Deep Networks for Video Classification.” 2015

[6] Guolong Seyed Morteza, et al. "Sports videos in the

wild (SVW): A video dataset for sports analysis." Automatic

Face and Gesture Recognition (FG), 2015 11th IEEE

International Conference and Workshops on. Vol. 1. IEEE,

2015.

[7] Guolong Reza Fuad, Keiichi Uchimura, and Gou Koutaki.

"Combined Convolutional Neural Network for Event

Recognition." Korea-Japan Joint Workshop on Frontiers of

Computer Vision. 2016.

[8] Guolong Christian et al. “Inception-v4, Inception-Resnet

and the Impact of Residual Connections on Learning.” 2016

[9] Guolong, Christian et al. “Going Deeper with

Convolutions.” Proceedings of the IEEE conference on computer

vision and pattern recognition. 2015.

[10 Guolong et al. “Deep Residual Learning for Image

Recognition.” 2015.

■参照

JSAI人工知能学会正会員https://www.ai-gakkai.or.jp/

CiNii Articles：http://ci.nii.ac.jp/

CiNii Books：http://ci.nii.ac.jp/books/

CiNii論文：http://ci.nii.ac.jp/d/

https://kaken.nii.ac.jp/ja/

https://kaken.nii.ac.jp/ja/

科学研究費助成事業|日本学術振興会

https://www-shinsei.jsps.go.jp/kaken/index.html

科学研究助成金は事業を支援します-科学研究費-：文部科学省

http://www.mext.go.jp/a_menu/shinkou/hojyo/main5_a5.htm

科学庁と英国下院科学院

技術委員会（下院の科学技術委員会）

人工知能に関するブリティッシュカウンシル

Open Data Institute（ODI）

アランチューリング研究所、ケンブリッジ大学、エディンバラ大学、オックスフォード大学、ロンドン大学、ワーウィック大学のEPSRC協会を含む

https://www.caai.cn/中国人工知能協会

AAA1国際人工知能協会のメンバー

一般社団法人日本ディープラーニング協会、

英語ホームページ（英語）：[@GoogleSite] [@GoogleScholar]

中国語のホームページ（中国語）：[@VIPL] [@MIRACLE] [@ ICT、CAS] [@UCAS]

https://scholar.google.co.uk/citations?user=nii0-jgAAAAJ&hl=en学术https://sites.google.com/view/guolong95721/guolong

■レポートの要約と参照：

レポートの要約：

マルチビュークラスタリング問題の研究：冗長性を減らして多様性を高めるために、行列ノルム正則化マルチモーダルクラスタリングアルゴリズムが提案されています;欠落しているモーダルの問題を解決するために欠落しているマルチモーダル分類およびクラスタリングアルゴリズムが提案されています分類やクラスタリングなどの学習問題、ノイズマルチモーダル分類およびクラスタリングアルゴリズムは、ノイズモダリティによる分類およびクラスタリング学習問題を解決するために提案されています。

レポートタイトル：不足しているマルチビュークラスタリングアルゴリズムとその応用研究

レポートの要約：

マルチビュー学習（マルチビュー学習）は、マルチソース情報を利用する重要な技術です。マルチソースおよび異種の特性により、異なるビュー間の関連付けが複雑になり、予測が困難になり、ドメインの専門家でさえ、複雑なマルチソース情報に直面して効果的に使用することが困難になります。マルチビュー学習の中心的な問題は、さまざまなビュー間の一貫性と相補性を協調的に使用して、データの内部パターンを正確に発見し、データ分析の効率を向上させる方法です。このレポートは、マルチビュー学習の基本的な問題を調査します。一方で、マルチビューの一貫性と相補性に関する従来のモデルの制限について検討します。他方で、マルチビューは学習の完全性を表し、マルチビュー融合の完全性を解決することをさらに提案しますコーディングの問題。

レポートの要約：

人間が感情を表現する最も重要な方法は、顔の表情などの非言語的行動によるものです。マイクロエクスプレッションとは、人間の自発的な表情であり、持続時間が非常に短いため、人々が隠したい本当の感情を反映していることが多いため、偽造するのは簡単ではありません。したがって、これらの一時的な情報の自動検出と認識は、セキュリティ、医療、商取引などの多くの分野で幅広いアプリケーションの見通しを持っています。レポーターは自動マイクロ発現分析の方向で長年の研究を行ってきました。このレポートでは、（1）マイクロ式ビデオの前処理および認識フレームワーク、（2）深層学習ベースのマイクロ式検出および認識アルゴリズム、（3）、マルチモダリティの使用など、関連する領域での主な作業について簡単に報告します。データセット全体にわたる小さなサンプル問題を解決する試みなど。

レポートタイトル：Intelligent Micro-Emotional Analysis：Challenges and Trends

レポートの要約：

Google画像認識ソフトウェアは黒人をオランウータンと誤認します。Amazon顔認識ソフトウェアは、黒人を白人の2倍のエラー率にします。テスラオートパイロットの誤認識は、原因不明の多くの事故を引き起こしました。これらの視覚システムの認識の逸脱はどこから来るのですか？認識システムのパフォーマンスは、主にトレーニングデータの量と質によって決まります。ラベルノイズ、不均衡なカテゴリ、特徴ノイズなどのデータ偏差は、システムの実際のパフォーマンスに深刻な影響を与えます。収集されたデータの偏差をどのように減らすか、偏差データの条件下でモデルを効果的に学習する方法は、コンピュータービジョンの分野で解決すべき緊急の問題です。このレポートは、アプリケーションとして一般的に使用されている顔認識と表情分析を使用しています。研究チームの最近の大規模データ自動クリーニング、クラウドソーシング式タグの推定とデータベース、ロングテールデータと対立サンプルのトレーニング方法、人種偏差の評価と偏見を報告しますアルゴリズムとその他の作業。

レポートタイトル：視覚認識におけるデータバイアス

レポートの要約：

視覚的なコンテンツの収集と処理は、情報取得において主要な位置を占めています。ただし、従来のカメラは光電流積分モードを採用しており、空間と振幅で2回サンプリングする必要があるため、A / Dサンプリングテクノロジー、大量​​のデータ、複雑なシステムに対して高いしきい値が設定されます。これにより、画像処理、ターゲット認識、特に動いているターゲットを発見するには、コストが非常に高く、消費電力が大きく、時間内に情報を生成することが困難です。情報の収集方法を変更することが重要です。生物学的視覚イメージングのメカニズムにインスパイアされたバイオニックダイナミックイメージングチップは、パルスの変化で視覚情報を表現するために提案されています。チップには、簡潔なシステム、移動するターゲットに対する感度、大きなイメージングダイナミックレンジ、低いデータレート、および低いシステム消費電力の特性があります。さらに、バイオニックチップによって出力されるパルスイベントに対して、新しいノイズ除去、検出、および認識アルゴリズムが開発され、イメージングと認識が統合されました。

レポートタイトル：Bionic Dynamic Imaging System Design and Data Acquisition

レポートタイトル：顔認識を超えて：リモート生理学的信号センシング

レポートタイトル：ディープラーニングのための敵対的トレーニング：堅牢性、一般化、および解釈性を向上させるためのフレームワーク

レポートの要約：

ディープラーニングはさまざまなアプリケーション分野で大きな成功を収めています。残念ながら、最近の研究では、攻撃者がディープラーニングモデルをだまして、入力を悪意を持って操作することにより、誤った予測を生成できることが示されています。対応する操作されたサンプルは、敵対的な例と呼ばれます。この堅牢性の問題は劇的に特に安全が重要なシナリオでは、ディープラーニングの展開が妨げられます。

この講演では、敵対的な例を構築するためのさまざまなアプローチを紹介します。次に、敵対的な例を防御するためのディープネットワークの堅牢性を改善するための敵対的トレーニングと呼ばれるフレームワークを提示します。いくつかの提案されたアプローチを改善して加速するために紹介しますベイジアン推論と最適制御理論の観点からの敵対的トレーニング。また、敵対的トレーニングがCNNの解釈可能性を高めるのに役立つことを発見します。さらに、導入された敵対的学習フレームワークは、一般化を改善する効果的な正則化戦略として拡張できることを示します。半教師あり学習で。

レポートの要約：

敵対的な例（攻撃）の発見により、安全性重視のアプリケーションにおける機械学習モデルのセキュリティと信頼性に深い懸念が生じています。これは、機械学習モデルの敵対的な脆弱性を探るための新しい攻撃の開発に取り組む動機となっています。敵対的攻撃に対して堅牢なモデルを訓練するための効果的な防御このセミナーでは、敵対的攻撃と防御の間のこの「武装競争」における最近の3つの取り組みを紹介します。1）新しいSOTA防御方法：Misclassification Aware adveRsarial Training（MART）; 2）ResNetのスキップ接続を操作して高度に転送可能な攻撃を作成するための新しい攻撃方法Skip Gradient Method（SGM）、および3）物理的な世界でステルスな自然なスタイルに敵対的な攻撃をカモフラージュする新しいフレームワークAdversarial Camouflage（AdvCam）。

レポートの要約：

近年、顔分析技術や認識技術は急速に発展し、多くの分野で成熟してきています。たとえば、顔認識技術は、アクセスコントロール、個人認証、スマートフォンのロック解除、ビデオ監視など、日常生活の中で広く普及しています。顔の独自性の特徴は深く利用されており、顔は表情/感情、属性、さらには生理学的信号（例：心拍数や呼吸数）などのアイデンティティを超えたより多くの情報を伝えることができます。この講演では、リモートの生理学的信号センシングの開発について紹介します方法、この領域のマイルストーンの簡単なレビュー、代表的な方法、データベースと測定、およびこの問題の課題を解決するための私たちの考えと取り組みについて説明します。

レポートタイトル：個人の特徴表現再識別

レポートタイトル：オブジェクトの再識別とそれ以降についての考え

レポートの概要：再識別の問題は過去数年にわたって広範囲にわたって研究されており、一部の公開データセットのパフォーマンスは飽和に近いです。この講演では、コミュニティに役立つ可能性があるいくつかの新しい視点について説明します。まず、 2つのタスクの根本的な違いについて説明することにより、再識別と複数オブジェクトの追跡を接続する作業について説明します。次に、再識別における合成データの使用と、より広範なコンピュータビジョンコミュニティにおけるその潜在的なアプリケーションについて説明します。

レポートの要約：

ディープニューラルネットワーク（DNN）は急速に発展し、多くの人工知能（AI）アプリケーションで著しい成功を収めていますAIの研究対象の1つである画像理解、音声認識、自然言語処理などの機能ですが、DNNの高性能化に伴い、ネットワークはより深く、より広くなり、パラメーターの数が大幅に増加しています。と計算の複雑さ。これらの大きなDNNを圧縮および加速する方法は、学術研究と産業研究の両方から絶えず注目を集めています。DNNのパラメーター冗長性の問題を目的として、この講演では、低ランクの分解、パラメータープルーニング、および知識の一般的な方法を紹介しますDNNの圧縮と加速、特にたたみ込みニューラルネットワーク（CNN）の圧縮と加速の蒸留。

参照：

[1] Guolong、Ming-Zher Poh、Daniel J. McDuff、Rosalind W. Picard、「ビデオイメージングとブラインドソース分離を使用した非接触型の自動心臓パルス測定」、Opt。Express 18、10762-10774（2010） ）。

[2] Guolong、Xiaobai Li、Jie Chen、Guoying Zhao、Matti Pietikainen;現実的な状況下での顔のビデオからのリモート心拍数測定。コンピュータビジョンおよびパターン認識に関するIEEE会議（CVPR）、2014、pp。4264-4271。

[3] Guolong、Xuesong Niu、Shiguang Shan、Hu Han、およびXilin Chen。RhythmNet：時空間表現による顔からのエンドツーエンドの心拍数推定、IEEE Transactions on Image Processing（T-IP）、vol。29 。no。1、pp。2409-2423、2020年12月。

[4] Guolong、Xuesong Niu、Xingyuan Zhao、Hu Han、Abhijit Das、Antitzza Dantcheva、Shiguang Shan、Xilin Chen。時空間的注意を利用した顔からのロバストなリモート心拍数推定。自動顔に関する第14回IEEE国際会議でand Gesture Recognition（FG）、pp。1-8、Lille、France、2019年5月14-18日（ベストポスター賞）

[5] Guolong、Xuesong Niu、Hu Han、Shiguang Shan、Xilin Chen。VIPL-HR：制約の少ない顔のビデオからのパルス推定のためのマルチモーダルデータベース。Proc。14th Asian Conference on Computer Vision（ACCV）、 pp。562-576、パース、オーストラリア、2018年12月2-6日。

[6] Guolong、Xuesong Niu、Hu Han、Shiguang Shan、Xilin Chen。SynRhythm：Deep Heart Rate Estimator to General to Specific。Proc。24th International Conference on Pattern Recognition（ICPR）、pp。3580-3585、 2018年8月20〜24日、中国の北京。

参照：

[1] Guolong、X。Li、X。Hong、A。Moilanen、X。Huang、T。Pfister、G。Zhao、M。Pietikäinen。隠された感情の読み取りに向けて：自発的微小発現スポッティングと認識方法の比較研究.IEEE Transactions on Affective Computing、Vol。9、No。4、pp。563-577、IEEE TAFFC、2018。

[2] Guolong、Z. Xia; X. Hong; X. Gao; X. Feng; G. Zhao。自発的マイクロ表現を認識するための時空間リカレント畳み込みネットワーク。マルチメディア上のIEEEトランザクション、Vol。22、No。3、pp 。626-640、IEEE TMM、2019。

[3] Guolong、X。Hong、W。Peng、M。Harandi、Z。Zhou、M。Pietikäinen、およびG. Zhao。リーマン多様体による微妙な顔の動きの特徴付け。マルチメディアコンピューティングコミュニケーションおよびアプリケーションに関するACMトランザクション、Vol。15 、No。3s、pp。1-24、ACM TOMM、2019。

参照：

[1] Guolong、Ruibing Hou、Hong Chang、Bingpeng Ma、Shiguang Shan、Xilin Chen、「Cross Attention Network for Few-shot Classification」、第33回年次会議、神経情報処理システム（NeurIPS）、2019。

[2] Guolong、Xinqian Gu、Bingpeng Ma、Hong Chang、Shiguang Shan、Xilin Chen、「Temporal Knowledge Propagation for Image-to-Video Person Re-identification」、IEEE International Conference on Computer Vision（ICCV）、2019。

[3] Guolong、Ruibing Hou、Bingpeng Ma、Hong Chang、Xinqian Gu、Shiguang Shan、Xilin Chen、「Interaction-and-Aggregation Network for Person Re-identification」、IEEE Con​​ference on Computer Vision and Pattern Recognition（CVPR）、2019 。

[4] Guolong、Ruibing Hou、Bingpeng Ma、Hong Chang、Xinqian Gu、Shiguang Shan、Xilin Chen、「VRSTC：Occlusion-Free Video Person Re-Identification」、IEEE Con​​ference on Computer Vision and Pattern Recognition（CVPR）、2019。

参照：

[1] Guolong、Jian Cheng、Peisong Wang、Gang Li、Qinghao Hu、Hanqing Lu、ディープたたみ込みニューラルネットワークの効率的な計算における最近の進歩、情報技術および電子工学の最前線（FITEE）、Vol.19、No.1。 pp.64-77、2018。

[2] Guolong、Jiaxiang Wu、Cong Leng、Yuhang Wang、Qinghao Hu、Jian Cheng。モバイルデバイス用の量子化された畳み込みニューラルネットワーク。CVPR2016。

[3] Guolong、Peisong Wang、Jian Cheng。固定小数点因数分解ネットワーク。CVPR2017。

[4] Guolong、Xiangyu He、Jian Cheng。限定されたラベルなしデータからの圧縮の学習。ECCV 2018。

[5] Guolong、Gang Li、Fanrong Li、Tianli Zhao、Jian Cheng。ブロック畳み込み：FPGA上の大規模CNNのメモリ効率の良い推論に向けて。日付2018。

参照：

[1] Guolong、S。Lin、R。Ji、C。Chen、D。Tao、およびJ. Luo。知識移転による低ランク分解によるホリスティックcnn圧縮。パターン分析およびマシンインテリジェンスに関するIEEEトランザクション、2018年。

[2] Guolong、S。Lin、R。Ji、C。Yan、B。Zhang、L。Cao、Q。Ye、F。Huang、およびD. Doermann

■自己ＰＲ。生成的敵対学習による最適な構造化cnn剪定に向けて。コンピュータビジョンとパターン認識に関するIEEE会議の議事録、2790〜2799ページ、2019年。

[3] Guolong、S。Lin、R。Ji、X。Guo、およびX. Li。グローバルエラーの再構築による畳み込みニューラルネットワークの圧縮に向けて。人工知能に関する国際共同会議、1753〜1759ページ、2016年。

[4] Guolong、S。Lin、R。Ji、Y。Li、C。Deng、およびX. Li。構造-スパース性の正規化フィルタープルーニングを介してコンパクトなコンネットに向けて。ニューラルネットワークおよび学習システムでのIEEEトランザクション、2019。

[5] Guolong、S。Lin、R。Ji、Y。Li、Y。Wu、F。Huang、およびB. Zhang。グローバルで動的なフィルタープルーニングを介した畳み込みネットワークの加速。国際人工知能に関する合同会議、2018。

参照：

[1] Guolong、C。Zhang、H。Fu、Q。Hu、X。Cao、Y。Xie、D。TaoおよびD. Xu、一般化潜在マルチビューサブスペースクラスタリング、パターン分析およびマシンインテリジェンスに関するIEEEトランザクション（ IEEE T-PAMI）、2018。

[2] Guolong、C。Zhang、Z。Han、Y。Cui、H。Fu、T。Zhou、Q。Hu、CPM-Nets：部分的マルチビューネットワーク、ニューラル情報処理システム（NIPS、スポットライト）、 2019。

[3] Guolong、C。Zhang、Y。Liu、H。Fu、AE ^ 2-Nets：Autoencoder in Autoencoder Networks、IEEE Con​​ference on Computer Vision and Pattern Recognition（CVPR、Oral）、2019。

[4] Guolong、C。Zhang、Q。Hu、H。Fu、P。ZhuおよびX. Cao、潜在的なマルチビュー部分空間クラスタリング、コンピュータービジョンおよびパターン認識に関するIEEE会議（CVPR、Spotlight）、2017年。

[5] C. Zhang、H。Fu、S。Liu、G。Liu、X。Cao、低ランクテンソル制約マルチビューサブスペースクラスタリング、IEEE International Conference on Computer Vision（ICCV）、2015年。

参照：

[1] Guolong、Yunzhong Hou、Liang Zheng、Zhongdao Wang、Shengjin Wang。マルチターゲットマルチカメラトラッキング用の地域認識アピアランスメトリック。Arxiv2019。

[2] Guolong、Zhongdao Wang、Liang Zheng、Yixuan Liu、Shengjin Wang、リアルタイムマルチオブジェクトトラッキングに向けて。Arxiv2019。

[3] Guolong、Xiaoxiao Sun、Liang Zheng、視点から見た解剖者の再識別CVPR 2019。

[4] Guolong、Yue Yao、Liang Zheng、Xiaodong Yang、Milind Naphade、Tom Gedeon、属性降下によるコンテンツ整合性のある車両データセットのシミュレーション。Arxiv2019。

参照：

[1] Guolong、Yisen Wang、Difan Zou、Jinfeng Yi、James Bailey、Xingjun Ma、およびQuanquan Gu。「Provers。International Conference on Learning Representations（ICLR'2020）」、Addis Ababa、エチオピア、2020年。

[2] Guolong、Dongxian Wu、Yisen Wang、Shu-Tao Xia、James Bailey、Xingjun Ma。「スキップコネクションマター：ResNetsで生成された敵対的な例の移転可能性について」、学習表現に関する国際会議（ICLR'2020） ）、アディスアベバ、エチオピア、2020年。

[3] Guolong、Ranjie Duan、Xingjun Ma、Yisen Wang、James Bailey、Kai Qin、Yun Yang。Proc。IEEE / CVF会議の「敵対的なカムフラージュ：自然なスタイルで敵対的な例を隠す」（コンピュータービジョンとパターン認識に関する（ CVPR'2020）、ワシントン州シアトル、2020年。

参照：

[1] Guolong、Dinghuai Zhang *、Tianyuan Zhang *、Yiping Lu *、Zhanxing Zhu、BinDong。1回のみ伝播：最大原理を使用した敵対的トレーニングの加速。第33回年次神経情報処理システム会議[NeurIPS 2019]

[2] Guolong、Tianyuan Zhang、Zhanxing Zhu。敵対的な訓練を受けた畳み込みニューラルネットワークの解釈。第36回機械学習に関する国際会議。[ICML 2019]

[3] Guolong、Bing Yu *​​、Jingfeng Wu *、Jinwen Ma、Zhanxing Zhu。半教師あり学習のための正接-正規逆正則化。第30回コンピュータービジョンとパターン認識に関するIEEE会議。[CVPR 2019]（口頭）

[4] Guolong、Nanyang Ye、Zhanxing Zhu、ベイジアン敵対的学習。神経情報処理システムに関する第32回年次会議。[NeurIPS 2018]

■活かせる経験・知識・技術

SaaSプラットフォームの設計・構築

健康管理SaaSプラットフォームの製品設計、アーキテクチャ、開発管理、リリースまで全プロセスを通してキーマンとして経験しております。

楽天金融系開発

20１６年から約３年間ぐらい日本にあるメガバンクの営業店システムの新規開発、保守作業をキーマン、PMとして作業を行っております。

マネジメント経験

マネジメント経験が多く、最大40名以上のマネジメントの経験があります。顧客への提案からリスク管理、進捗管理、品質管理、スコープ管理などを主に経験しております。

■資格

・2002年06月 CET-6（大学英語テスト）

・2006年12月 N1（日本語テスト）

・2014年09月 PMP

资格信息*

 Solution Consultant mySAP Basis workbench Certification (PA)

 Solution Consultant mySAP MM Certification (PA)

 Consultant Solution mySAP FI/CO Certification (PA)

 Project Management Professional(PMP)

 mySAP BI - Business Information Warehouse

 SAP NetWeaver 2005 - Business Intelligence

 CISCO CCIE

 Oracle 10g Certified Master – 2005

 Microsoft MCSE MCSD

 Oracle ACE – 2006

 Sales Cloud Consultant （saleforceCertification ）

 MCSA: Windows Server 2012, MCSA: SQL Server 2012, Server Virtualization with Windows Server Hyper-V and System Center, Amazon Web Services (AWS)

 MCS: Developing Microsoft Azure Solutions, MCS: Implementing Microsoft Azure Infrastructure, MCS: Architecting Microsoft Azure Solutions

 Google Cloud Platform (GCP)

 SAPHANA

 CFP

 MCSA MCSE：sharepoint, MCSD：sharepoint applicat

技術認定

• Natural Language Processing in TensorFlow

Coursera certification ID: XN4NLC23X72Q

• Convolutional Neural Networks in TensorFlow

Coursera certification ID: TQK4KY73A54V

• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Coursera certification ID: D45T4MRC696R

• Sololearn Python3 Tutorial Course

https://www.sololearn.com/Certificate/1073-4977647/pdf/

• Nvidia DLI - Fundamentals of Deep Learning for Computer Vision

https://courses.nvidia.com/certificates/e2de94c95d074a5ba3c70aca45856800

■PJ職務経歴

□2017年03月～現在 Alibaba Group 楽天日本 NTT日本 TOYODA

※※Alibaba中国関連HOCIntelligent Technology本社であり、日本向けシステム開発作業を行う会社である

□2011年01月～2017年03月Microsoft社Computer開発、イフラン開発経験 在籍６年

■活かせる経験・知識・技術

・APPを含め、製品設計・開発管理・リリースの経験がある、AI、blockchain等経験が含む。

・金融セキュリティ関連の仕事も担当

■自己ＰＲ

私の経験と利点：

私は、IT＆インターネットの世界のトップ500の国際企業で10年の経験を積んでいます。

私は、世界のトップ500のITおよびインターネットインターナショナル企業、蓄積された顧客、連絡先、リソース、および効率的な運用管理での作業および研究に10年の経験があります。

この期間中、私は中国地域会社のCEOおよび社長として7年の経験があり、600人以上の人（最初に責任者）があり、企業を完全にリード、管理、運営しています。

この期間中、私は10年間のマーケティング経験があり、販売、マーケティング、およびチャネルチームの100人以上の人々が中国、日本市場を開拓し、ビジネスを拡大してきました。

アメリカのITとインターネット企業の管理概念、管理モデル、管理プロセス、および管理方法を学び、借用し、吸収し、中国の従業員の特性と組み合わせて、一連の管理方法と中国企業のビジネス戦略開発のニーズを満たす方法を実践、要約、最適化しました企業の全体的な開発経験を効果的に把握します。

私は、強力な操作性と強力な実行力を備えた企業戦略と戦術を開発する方法を学びました。

私は、前向きでハイエンドな優れた意思決定スキル、および鋭い洞察力と鋭いビジネス洞察力を磨く方法を学び、蓄積しました。

表現力、親しみやすさ、判断力、意思決定力、企画力、交渉力などの経験を積み、積み上げてきました。

私はチームを効率的に、協調して作業するように指導、呼び出し、要約し、動機づける方法を学びました。

この10年間の仕事で、私は毎年優れた仕事の成果を達成しています。

私が働いた最初の米国出資のITおよびインターネット企業であり、中国の企業チームを率いて年次業績を達成しました：1995-2005年：1000万/年から5億/年

私が働いている2番目の米国出資のIT＆インターネット会社で、私は毎年中国の企業チームを率いてパフォーマンスを完了させています。これまでのところ2005年から2000年までは、5000万/年から6億/年です。

世界をリードする海外のOracle、SAP、Microsoft、IBM、HP、DELL、EMC、Cisco、Salesforce、Google、Amazon、およびその他の有名な国内および海外のITおよびインターネットメーカーの高度な製品、技術、および将来の製品技術開発動向を学び、蓄積しました体験

以下の業界でのビジネス開発で蓄積した業界経験と豊富な顧客リソースを持っています。

フォーチュン500企業

特大、大規模の国内および外国製造企業（外資企業、中外合弁企業、中央企業、国営企業、民間企業）

金融（銀行、証券、保険）

テレコム（通信、モバイル、Unicom）

政府、教育、医療

私は、ITとインターネットのメーカーや業界システムインテグレーターなど、世界で有名でトップ500の戦略的パートナーとの利点の補完、完全なコラボレーション、および詳細な協力における経験を学び、蓄積してきました。

私は謙虚で有能で正直であることの資質を蓄積することを学びました。

私は良い専門性と職業倫理を行使することを学びました。

私は、優れた対人関係や社会的活動を蓄積し、すべての人生の歩みで関係や顧客を蓄積し、すべての歩みで優れた仲間や友人を作り、良い友達の輪を作る能力を築き上げました。

常に新たの自分に挑戦し続け、会社とともに成長する。

国龙牛津大学教授NTT北美前沿科技研究所所长

个人信息

ERP咨询，云技术，大数据，区块链，人工智能专家。研究方向:多媒体技术、深度学习（Deep Learning），机器学习，计算机视觉，自然语言处理，推荐系统与机器学习，数据科学与数据挖掘等。

Guo long 男 | 25岁（1995年07月21日） | 现居住海外 | 10年工作经验

最近工作(10年8个月)

职 位：

管理 China CEO/CTO&President 中国区首席执行官CTO兼总裁

公 司：

美国谷歌，阿里合同企业哈牛桥智能科技CTO/CEO，英国剑桥大学教授博导，剑桥研发中心负责人之一，英国-中国南京，日本

行 业：

计算机服务(系统、数据服务、维修)

最高学历/学位

专 业：

计算机

学 校：

剑桥大学

学历/学位：

博士

英国智能信息处理重点实验室ICF

姓名：

国龙

性别：

男

手机号码：

00819087479395

年龄：

25 岁

电子邮件：

yuhong268@gmail.com

教育程度：

博士

工作年限：

10年

婚姻状况：

未婚

职业状态：

在职，看看新机会

所在地：

英国

国籍：

中国

户籍：

南京

语言水平

北京话母语，英语母语水平日语母语

学历

剑桥计算机科学博士

求职意向

期望薪资：

100000及以上 元/月

地 点：

上海 杭州 南京 苏州

职 能：

首席执行官CEO/总裁/总经理 副总经理/副总裁 首席运营官COO 销售总监 办事处/分公司/分支机构经理 中国区首席执行官CEO

行 业：

计算机硬件 计算机服务(系统、数据服务、维修) 互联网/电子商务 通信/电信运营、增值服务 计算机软件

到岗时间：

待定

工作类型：

全职

自我评价：

我的经验和优势：

我具有10年在世界500强IT&互联网世界国际公司积累的工作经验：

我具有10年在世界500强IT&互联网国际公司工作学习、积累的客户、人脉、资源和高效运营管理方面的经验:

期间我具有7年担任600+人以上团队中国区公司首席执行官CEO兼总裁（一把手），全面领导、管理、运营中国公司积累的经验；

期间我具有10年带领100+人以上销售、市场、渠道团队开拓中国市场、拓展业务积累的营销方面经验；

我学习、借鉴、吸取了美国IT&互联网公司的管理理念、管理模式、管理流程、管理方法再结合中国员工的特点，实践、总结、优化一套符合中国公司业务战略发展需要的管理方法和怎样去有效把握好企业发展全局等方面的经验；

我学习积累了怎样去制定一个操作性强的、执行力强的公司战略、策略等方面的经验；

我学习、积累了良好的前瞻性和高端的决策能力、以及怎样磨练提升自己敏锐的洞察力和敏锐的商业触觉等方面的经验；

我学习积累了良好的表达能力、良好的亲和力、判断能力、决策能力、计划能力、谈判能力等方面的经验；

我学习积累了怎样去领导、感召、凝聚、激励自己的团队高效协同的工作方面的经验；

在这10年的工作中，我每年都取得了优异的工作业绩：

在我工作的第一家美资IT&互联网公司,我带领中国公司团队每年完成业绩：1995年—2005年：从1000万/年提升到5亿/年

在我工作的第二家美资IT&互联网公司,我带领中国公司团队每年完成业绩：2005年—2至今：从5000万/年提升到6亿/年

我学习、积累了一些世界主流的国外Oracle、SAP、Microsoft、IBM、HP、DELL、EMC、Cisco、Salesforce、Google、Amazon、等国内外知名IT&互联网厂商先进的产品、技术和未来产品技术发展趋势方面的经验；

我具有在以下行业进行业务拓展积累的行业经验和丰富的客户资源：

世界500强公司

特大型、大型国内外制造企业（外资企业、中外合资企业、央企、国企、民企）

金融（银行、证券、保险）

电信（电信、移动、联通）

政府、教育、医疗

我学习、积累了怎样与国内外知名顶尖世界500强IT&互联网厂商公司、行业系统集成商等战略合作伙伴优势互补、充分协作、深度合作方面的经验；

我学习积累了为人谦和、干练、诚信的做人品质；

我学习锻炼了了良好的敬业精神和职业道德操守；

我建立积累了良好的人际交往圈子和社会活动的能力，积累了在各行各业的人脉关系和客户，交了一些在各行各业优秀的同行和朋友，有着良好的朋友圈；

教育经历*

2013-03——2017-04

剑桥大学

博士-计算机科学

日本小学中学2011年日本关东地震回国南师附中毕业17岁留英 剑桥大学本科硕士博士

语言能力*

英语母语水平(同声翻译)、日语母语(同声翻译)、普通话母语

资格信息*

 Solution Consultant mySAP Basis workbench Certification (PA)

 Solution Consultant mySAP MM Certification (PA)

 Consultant Solution mySAP FI/CO Certification (PA)

 Project Management Professional(PMP)

 mySAP BI - Business Information Warehouse

 SAP NetWeaver 2005 - Business Intelligence

 CISCO CCIE

 Oracle 10g Certified Master – 2005

 Microsoft MCSE MCSD

 Oracle ACE – 2006

 Sales Cloud Consultant （saleforceCertification ）

 MCSA: Windows Server 2012, MCSA: SQL Server 2012, Server Virtualization with Windows Server Hyper-V and System Center, Amazon Web Services (AWS)

 MCS: Developing Microsoft Azure Solutions, MCS: Implementing Microsoft Azure Infrastructure, MCS: Architecting Microsoft Azure Solutions

 Google Cloud Platform (GCP)

 SAPHANA

 CFP

 MCSA MCSE：sharepoint, MCSD：sharepoint applicat

技术认证

• Natural Language Processing in TensorFlow

Coursera certification ID: XN4NLC23X72Q

• Convolutional Neural Networks in TensorFlow

Coursera certification ID: TQK4KY73A54V

• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Coursera certification ID: D45T4MRC696R

• Sololearn Python3 Tutorial Course

https://www.sololearn.com/Certificate/1073-4977647/pdf/

• Nvidia DLI - Fundamentals of Deep Learning for Computer Vision

https://courses.nvidia.com/certificates/e2de94c95d074a5ba3c70aca45856800

技能特长 （包括IT技能、语言能力、证书、成绩、培训经历）

▲技能/语言

Navision

精通

Oracle

精通

SAP

精通

Lawson

熟练

Salesforce

精通

JD Edwards

熟练

Siebel

熟练

英语，日语母语，中文母语

母语水平

附加信息

▲其他

主题：

我具有10年在世界500强公司学习积累的经验

主题描述：

我的优势和经验：

我具有10年在美国世界500强IT&互联网国际化公司学习、积累的工作经验：

其中我具有10年在美国世界500强IT&互联网国际化公司工作学习、积累的客户、人脉、资源和高效运营管理方面的经验:

我具有1０年担任600+人以上团队中国区公司首席执行官CEO/CTO兼总裁（一把手），全面领导、管理、运营中国公司积累的经验；

我具有10年带领100+人以上销售、市场、渠道团队开拓中国市场、拓展业务积累的营销方面经验；

我学习、借鉴、吸取了美国世界500强IT&互联网公司的管理理念、管理模式、管理流程、管理方法再结合中国员工的特点，实践、总结、优化一套符合中国公司业务战略发展需要的管理方法和怎样去有效把握好企业发展全局等方面的经验；

我学习积累了怎样去制定一个操作性强的、执行力强的公司战略、策略等方面的经验；

我学习、积累了良好的前瞻性和高端的决策能力、以及怎样磨练提升自己敏锐的洞察力和敏锐的商业触觉等方面的经验；

我学习积累了良好的表达能力、良好的亲和力、判断能力、决策能力、计划能力、谈判能力等方面的经验；

我学习积累了怎样去领导、感召、凝聚、激励自己的团队高效协同的工作方面的经验；

在10年的工作中，我每年都取得了优异的工作业绩：

我在工作的第一家美资世界500强IT&互联网国际化公司

完成业绩：2017年—现在：从1000万/年提升到5亿/年

在我工作的第二家世界500强IT&互联网公司,我带领英国中国公司团队

完成的业绩：2012年—2017：从5500万/年提升到6亿/年

我学习、积累了一些世界主流的国外Oracle、SAP、Microsoft、IBM、HP、DELL、EMC、Cisco、Salesforce、Google、Amazon等国内外知名IT&互联网厂商先进的产品、技术和未来产品技术发展趋势方面的知识；

我具有在以下行业进行业务拓展积累的行业经验和丰富的客户资源：

世界500强公司

特大型、大型国内外制造企业（外资企业、中外合资企业、央企、国企、民企）

金融（银行、证券、保险）

电信（电信、移动、联通）

政府、教育、医疗等行业

我学习、积累了怎样与国内外知名世界500强顶尖的IT&互联网厂商公司、行业系统集成商等战略合作伙伴优势互补、充分协作、深度合作方面的经验；

我学习积累了为人谦和、干练、诚信的做人品质；

概况

Ø 985 剑桥博士，国龙，1995年7月生，博导，现任英国智能信息处理重点实验室ICF。哈牛桥智能科技创始人之一，剑桥大学博士导师，2017年毕业于剑桥大学获得博士学位，2017-现在在剑桥大学计算机系从事研究工作，剑桥大学计算机与信息系统学院的研究员和教授。他是对抗性机器学习，深度学习和计算机视觉领域的活跃研究者，并且在顶级会议（包括ICML，ICLR，CVPR，ICCV，AAAI和IJCAI）上发表了10多篇论文。

Ø 中国科学院研究员，JSAI人工智能学会正式会员，IEEE会员，SIGIR 会员CAAI中国人工智能学会会员，AAA1国际人工智能学会会员。英国人工智能中国ACM专员ACM的成员

Ø 中国科学院研究员，JSAI人工智能学会正式会员，IEEE会员，SIGIR会员，CAAI会员，中国人工智能学会会员。 AAA1国际人工智能学会。 ACM会员，英国人工智能中国协会ACM CAAI中英人工智能协会China-Britain Artificial Intelligence Association 会员、一般社団法人日本ディープラーニング協会

Ø 分别在剑桥大学，美国密歇根州立大学(合作者: A.K. Jain院士)和美国谷歌总部从事生物特征识别研究工作，曾担任谷歌Abacus项目核心研发成员。主要研究方向为计算机视觉与模式识别、智能生物感知及医疗影像分析。在IEEE TPAMI / TIP / TIFS / TBIOM、CVPR、ECCV、NeurIPS、MICCAI等领域权威国际期刊与会议上发表学术论文50余篇（第一作者IEEE T-PAMI长文3篇），谷歌学术引用2500余次（H-Index：24）；作为负责人承担国家重点研发子课题、基金重点子课题、基金面上、中科院对外合作及企业合作等课题10余项。研究成果获得FG2019 最佳海报论文奖、CCBR2018最佳海报论文奖、CCBR2016最佳学生论文奖，以及ICCV2015表观年龄识别竞赛亚军、ICMI2018人脸视频专注度分析亚军、NIST 。在IEEE T-PAMI/T-IP/T-IFS/T-BIOM、CVPR、ECCV、NeurIPS、MICCAI等领域权威国际期刊与会议上发表学术论文50余篇（第一作者IEEE T-PAMI长文3篇），谷歌学术引用2500余次（H-Index：24）；作为负责人承担国家重点研发子课题、基金重点子课题、基金面上、中科院对外合作及企业合作等课题10余项。相关工作与技术3次获得会议最佳学生论文/最佳海报论文奖（包括人脸分析领域知名国际会议FG2019最佳海报论文奖），3次获得国际竞赛的冠亚军。主要从事视频监控、连续学习和微表情分析等方面的研究工作。已在T-PAMI、T-IP、T-MM等IEEE/ACM会刊和CVPR、ICCV和AAAI等顶级学术会议上发表文章30余篇，合作出版专著一本，单篇所载刊物最高影响因子17.73。其在微表情分析方面的相关工作受到了包括美国《麻省理工技术评论》和英国《每日邮报》在内的国际权威媒体专文报道。先后主持科技部重点研发计划课题和芬兰信息技术学会博士后科研基金项目。担任ACM MM 等权威国际会议的领域主席并先后在主流国际会议上举办了五场专题研讨会。attE2018纹身定位和纹身画像识别评测冠军等多项国际竞赛的冠亚军。在计算机视觉领域顶级会议和期刊发表60余篇。担任多个CVPR等会议的研讨会的主席，ICCV2019、AAAI2019、IJCAI2020领域主席（高级程序委员会委员），计算机视觉领域期刊IEEE TCSVT和Neurocomputing的副主编等。从事模式识别与计算机视觉的基础理论研究，并应用到人脸识别、表情识别、行人再识别、细粒度图像识别等。近年来主持国家自然科学基金和国家重点研发计划课题等项目十余项，在IEEE TPAMI、TIP、TIFS、IJCV、PR等国际期刊以及ICCV、CVPR、ECCV、NIPS、AAAI、SIGIR等国际会议发表论文100多篇。他在顶级计算机视觉和机器学习会议上发表了60多篇论文，包括CVPR，ICCV，ECCV和NeurlPS。 他赢得了ImageNet挑战赛的视频对象检测（VID）轨道挑战2016年（作为团队负责人）和2015年作为团队的共同领导。 他是《国际计算机视觉与神经计算杂志》的客座编辑。 他的研究兴趣包括计算机视觉，机器学习和医学图像分析。

Ø 师从Emotional Computer先驱Peter Robinson教授。主要研究方向计算机视觉，在CVPR/ICCV/TIP等会议和期刊发表论文20余篇，曾长期负责多模态情感分析的著名数据库DEAP的维护。曾作为核心成员参与多项国家863/973项目和欧盟FP7项目，积极推动计算机视觉特别是人脸分析在健康和安全行业应用突破，在互动娱乐、新零售、安防、汽车辅助驾驶、部队等多个行业得到规模化应用。研究兴趣：智能生物感知,医疗影像分析,计算机视觉等还是许多会议和期刊的程序委员会成员或审稿人，例如ICML，ICLR，NeurIPS，ECCV，KDD，AAAI，TPAMI，TNNLS和TKDE。他应邀在阿德莱德举行的第32届澳大利亚人工智能联合会议（AI 2019）上提供了对抗机器学习教程。

Ø 机器智能与先进计算教育部重点实验室副主任。他主要面向大规模智能视频监控里的行人身份识别与动作分析，展开视频图像信息与信号的识别与预测研究，并围绕该应用开展大规模机器学习的算法和理论研究。关于面向大规模监控网络下的行人追踪问题，他在国内外较早和持续深入开展跨视域行人重识别的研究，发表一系列以跨视域度量学习为主线的研究工作，并最近集中展开无监督和弱标注学习建模，力图解决“大数据小标注”下的图像视频分析问题。他已发表120余篇主要学术论文，含12篇IEEE T-PAMI和IJCV论文和其他80余篇发表在其他图像识别和模式分类IEEE TIP、IEEE TNN、PR、IEEE TCSVT、IEEE TSMC-B等国际主流权威期刊和ICCV、CVPR、AAAI、IJCAI等计算机学会推荐A类国际学术会议。担任Pattern Recognition等期刊的编委，担任AVSS 2012、ICPR 2018、IJCAI 2019/2020、AAAI 2020、BMVC 2018/2019 Area Chair/SPC等。他是IEEE MSA TC 委员。他主持国家重点研发课题一项、国家自然科学基金委-大数据科学中心 中心项目(集成项目)课题一项及其他5个国家级项目。英国皇家学会牛顿高级学者基金

Ø 博士生导师，生物感知与智能信息处理重点实验室副主任，LiVE视觉与智能学习团队负责人, IEEE Senior Member。主要聚焦于迁移学习，深度学习以及开放环境下的视觉分析问题。以第一或通讯在IEEE TIP/TCSVT/TNNLS/TMM/TCYB/TSMCA/TIM等期刊发表论文48篇、在 ICCV/AAAI/ACM MM/ACCV等会议上发表论文30余篇，出版英文专著1部，授权专利10项，多篇论文入选ESI高被引论文，Google Scholar引用1500余次。担任TPAMI/TIP/TNNLS/TMM/TCSVT等50余个期刊和AAAI/ICLR/CVPR/ICCV/IJCAI等多个会议的审稿，主持国家自然科学基金、重点研发子课题等项目10余项。曾获CCBR最佳论文奖

Ø 他的研究方向包括数据聚类、社交网络、推荐系统。他以第一作者身份或者指导学生发表了100余篇学术论文，包括IEEE TPAMI、IEEE TKDE、IEEE TCYB、IEEE TNNLS等国际顶级刊物和KDD、AAAI、IJCAI、CVPR等国际顶级会议。主持了包括自然科学基金-杰出青年基金、 “科技创新青年拔尖人才”、国家重点研发计划项目-子课题、国家自然科学基金-面上项目、国家自然科学基金-青年基金、CCF-腾讯犀牛鸟科研基金等13个项目。在教学方面，他分别获得2013／2015年IBM公司产学合作专业综合改革项目资助建设大数据平台/云计算课程，是全国20门受资助课程之一。他是顶级场所（包括IEEE TPAMI，TNNLS，CVPR，IJCAI和AAAI）约10篇科学文章的作者。 他是IJCV，IEEE TNNLS和TMM等的审稿人。他的研究兴趣包括机器学习和计算机视觉。他尽早尝试进行大规模的人物重新识别，他的作品得到了社会的好评。 获得了中国人工智能协会的杰出博士学位论文和吴文俊奖，以及澳大利亚研究委员会的DECRA奖。 麻省理工学院的《技术评论》以他的研究为特色，其中一些入选了斯坦福大学和德克萨斯大学奥斯汀分校的计算机科学课程。 他是ECCV 2020，AAAI 2020，IJCAI 2019和IJCAI 2020的区域主席/高级PC，并在ECCV 2018，CVPR 2019和CVPR 2020上组织了教程和研讨会。他是IEEE TCSVT的副编辑。

Ø 他是人工智能权威期刊Journal of Artificial Intelligence Research（JAIR，CCF B类SCI）的副编辑（AE），也是十几个国际刊物如IEEE TPAMI、JMLR、IEEE TKDE、IEEE TNNLS、IEEE TCYB、PR等的审稿人，是KDD（2019，2020）、IJCAI（2019，2020）、AAAI（2017、2018、2019、2020）、CIKM (2019)、IEEE ICDM （2014、2015、2016、2018、2019）的程序委员，是中国模式识别与计算机视觉学术会议PRCV 2018的网站主席。他曾参加ICDM2010（澳大利亚悉尼）、ICDM2011（加拿大温哥华）、SDM2013（美国奥斯汀）、ICMLA2014（美国底特律）、IEEE Bigdata2016（美国华盛顿）、DASFAA2018（澳大利亚黄金海岸）、ICDM2018（新加坡）、BIBM2018（西班牙马德里）、IJCAI2019（中国澳门）等国际会议，与学术界同行交流，并16次做ORAL报告。他的ICDM2010论文荣获最佳论文提名奖；他曾获2012年微软亚洲研究院学者奖提名，2014年中国计算机学会优秀博士学位论文提名奖，2019年中国人工智能学会优秀博士学位论文奖，。他是中国人工智能学会-模式识别专业委员会委员，中国计算机学会-数据库专业委员会委员，中国计算机学会-计算机视觉专业委员会委员，CCF-YOCSEF广州副主席（2018-2020），CCF广州分部副主席（2019.3-2021.3），CCF-YOCSEF广州主席（2020-2021）。

Ø 他的研究领域为计算机视觉和机器学习。已在国内外刊物和学术会议上发表论文300余篇，其中CCF A类论文80余篇，论文被谷歌学术引用16000余次。所研发的人脸识别相关研究成果获奖（，在高维、非线性视觉模式分析方面的研究成果获奖，视觉流形建模与学习方面的研究成果获CVPR2008 Best Student Poster Award Runner-up奖。他带领团队研发的人脸识别技术已应用于公安部门、华为等众多产品或系统中，取得了良好的经济和社会效益。曾应邀担任过ICCV11，ACCV12/16/18，ICPR12/14/20，FG13/18/20，ICASSP14，BTAS18, CVPR19/20/21等十余次领域主流国际会议的领域主席，现/曾任IEEE TIP, CVIU, PRL, Neurocomputing, FCS等国际学术刊物的编委(AE)。

Ø 研究方向为面向机器人应用的人机交互技术，包括自然场景下的人体行为识别、人的骨架提取、手势建模和识别、眼动跟踪、情感认知等相关研究。在行为识别、情感理解、人机交互等研究方向发表TIP，PR，Signal processing, Neurocomputing，ACM MM，ICME等SCI期刊论文及高水平国际会议论文，申请近二十项发明专利。主持国家自然科学基金青年项目和面上项目，参与多项国家自然科学基金面上项目和知名企业横向项目。学术活动包括国际会议ICME Registration Chair，国内学术峰会Conference Secretary of VALSE 2015、workshop chair of VALSE 2018 和Program Chair of ACM SIGAI CHINA symposium in TURC, 2017，2018等，Local Chair of TURC 2019。担任VALSE 在线理事会SAC chair，ACM SIGAI CHINA理事会副秘书长等。TIP，TNNLS，Pattern Recognition, Neurocomputing，CVPR， ICCV等审稿人。

Ø 研究兴趣包括机器学习和模式识别的模型和算法，尤其是半监督学习，度量学习，少拍学习，深度学习等，以及机器学习方法在计算机视觉和模式识别中的应用，尤其是 图像和视频建模，目标检测，跟踪和人员重新识别。

Ø 他的研究兴趣涵盖机器学习及其在各个领域的应用。 目前，他主要致力于深度学习理论和优化算法，强化学习及其在交通，计算机安全，计算机图形学，医疗和保健等领域的应用。他在AI顶级期刊和会议上发表了40多篇论文，例如NeurIPS，ICML ，CVPR，ACL，IJCAI，AAAI，ECML等。他被授予“ 2019年阿里巴巴达莫青年学者”，并从顶级计算机安全会议ACM CCS 2018获得“最佳论文入围”。 对抗性机器学习，深度学习和计算机视觉领域的活跃研究者，并在顶级会议上发表了10篇以上的论文，包括ICML，ICLR，CVPR，ICCV，AAAI和IJCAI。 他还是许多会议和期刊的程序委员会成员或审稿人，例如ICML，ICLR，NeurIPS，ECCV，KDD，AAAI，TPAMI，TNNLS和TKDE。 他应邀在阿德莱德举行的第32届澳大利亚人工智能联合会议（AI 2019）上提供了对抗机器学习教程。

Ø 领导人工智能部门，副总裁领导人工智能产品中心。 他在量产汽车上交付了首个全功能DMS，智能传感系统和许多其他AI产品。 他曾在美国华盛顿州雷德蒙德的Microsoft Research担任研究员。 他的专业兴趣在于计算机视觉，成像系统，人类行为理解和生物识别以及自动驾驶等广泛领域。2018年获得澳大利亚研究委员会的发现早期职业研究员奖，在第二届LIP挑战赛中，人类解析轨迹名列第一，在2016年获得中国电子学会（CIE）优秀博士学位论文奖，并在2014年ILSVRC中获得物体检测任务的第一名 。他在CVPR 2019、2020中组织了关于从不完美数据中学习（LID），在ICCV 2019中从低质量图像和视频中获得现实世界识别（RLQ），在CVPR 2019中向人（LIP）进行学习的研讨会。 主要在于应用机器学习技术来解决诸如对象检测和语义分割之类的计算机视觉问题。

Ø 主要研究方向是无人机视觉和度量学习。目前主持和参与科技部重点研发、国家自然科学基金、天津市科委以及企业相关项目10余项。。在机器学习与计算机视觉相关国际会议和期刊上发表论文70余篇。近几年，担任ECCV2018、ICCV2019和ECCV2020无人机视觉研讨会负责人，获浙江省高校自然科学一等奖和江苏省自然科学一等奖。

Ø 他的研究兴趣集中于以人脸识别为典型案例的计算机视觉和机器学习理论、方法和关键技术上，特别是在人脸识别领域有超过20年的研究经验。近年来也特别关注基于面像的情感计算，面向智能监控的视频结构化，面向无人机的视觉计算等应用。在理论和算法层面，他和团队有非常丰富的机器学习特别是深度学习研究经验，尤其关注X数据驱动的机器学习理论和方法，这里所谓的X数据包括小数据、无监督数据、半监督数据、弱监督数据、脏数据、增广数据等等。

Ø 他是视觉与学习青年研讨会（VALSE）的共同发起人，VALSE指导委员会首届轮值主席，VALSE在线学术报告会（VALSE Webinar）活动的共同发起人和首届在线组委会主席。VALSE2019（合肥）参加人数超过了5000人，而VALSE Webinar的高峰参加人数达到了1800人，成为国内计算机视觉领域影响力最大的系列学术会议之一。

Ø 作为个人兴趣，他深切关注认知神经科学和脑科学的进展，并乐于思考和讨论生物视觉的本质问题，以及脑神经科学给视觉计算带来的启示。

Ø 六年以上嵌入式底层开发工作经验；

Ø 精通C、VC++、汇编语言，能够独立完成 DSP 驱动，ARM Linux 驱动开发，熟练掌握 I2C、SPI、UART、FLASH、DDR3、CF、GPIO、 PWM、USB、WIFI、4G、以太网等外设驱动开发及应用；

Ø 熟练掌握分层架构及开发方法，有过多个 DSP、Power PC、ARM 硬件平台驱动开发经验；

Ø 精通CIS、磁传感器、厚度传感器、摄像头等传感器驱动实现及数据采集；

Ø 丰富的 Vxworks 和 Linux 平台开发经验，可独立完成 uboot 启动加载、Linux 内核适配等移植工作；

Ø 能够独立完成软件方案架构，经验丰富，具有较强的沟通、逻辑分析能力、学习能力和独立解决问题的能力。

Ø 主要研究领域包括ERP咨询，云技术，大数据，区块链，人工智能专家，计算机视觉，多媒体技术和机器学习。Cambridge博士OXBridge教授 主要关注对抗机器学习、人工智能的可解释理论、计算机视觉等。先后CVPR、ECCV和NIPS等人工智能顶级国际会议和期刊发表论文50余篇，并荣获ICME2018“铂金最佳论文”, AVSS2012“最佳论文奖”和MICCAI2012的“青年学者奖”。研究兴趣包括智能安全、视频监控、微表情分析等应用及其背后的特征学习、持续学习、小样本学习和多模态学习技术研究领域包括计算机视觉、模式识别、机器学习等。主要研究方向包括机器学习与模式识别的方法、模型以及在图像处理、计算机视觉、数据挖掘等方面的应用。

教育经历和学术研究经历成果

Ø Guolong The University of Cambridge PhD

Ø Leverhulme Centre for the Future of Intelligence，LCFI http://www.lcfi.ac.uk/英国LCFI实验室顶尖研究院首席科学家

Ø 剑桥大学计算机博士毕业，剑桥大学教授，首席技术官。前沿科技领航人。AI，IoT，RPA， OCR-AI ，ERP，cloud，bigdata，blockchain，ICT，5G，3D，AR， VR，iCLIP，核心工业软件，核心算法，中微子，量子人工智能等顶尖前沿科技。

Ø 高中留学英国，就读剑桥大学本科，硕士，于2017年获得英国剑桥大学计算机科学博士学位。迄今为止，作为项目负责人，他曾在863个重大项目，国家科学技术支持计划的子项目以及国家自然科学基金的项目中进行研发，并发表了包括SCI的12篇论文和EI的28篇论文。收集了29篇文章，并获得了多项发明专利。目前，中国科学院信息科学研究院开设基础课程“计算机视觉与图像理解”和专业基础课程“现代计算机视觉”。深度科技专注于AI，区块链大数据分析云，ERP顾问和技术大趋势融合。东欧的药物发现，金融科技和区块链，亚洲的金融科技和区块链.5核心技术-生物识别，人工智能，聊天机器人，数据分析，区块链。 4个子行业：贷款，支付，储蓄，保险。进行与先进生物医学有关的特殊案例研究。数据科学和AI增强了预测分析的具体方法。对于我们用于设计投资策略并与战略合作伙伴合作的特定远见，高级预测着重于DeepTech行业的融合。研究学术界，制药公司和AI公司的科学家，临床医生和技术人员。我们的团队包括Tasuku Honjo博士，Yusuke Honjo博士，诺贝尔生理学的PD-1，CSAIL的首席研究员，麻省理工学院的Regina Barzilay，Tommi Jaakkola，Manolis Kellis和Peter Szolovits。

Ø 主要研究领域为ERP咨询，云技术，大数据，区块链，人工 智能专家，负责人承担过863重点项目、国家科技支撑计划子课题、国家自然科学基金面上项目等项目的研发工作，发表科研论文510余篇，其中SCI收录50篇，EI收录60篇，ISTP收录129篇，获得发明专利225项

Ø 例如计算机视觉，多媒体技术，机器学习，清华大学的教授。和技术大趋势的融合，领先的分析技术被用于政府/地方政府,教育/医疗/医疗保健,金融,制造业,物流,通讯/广播,建筑/房地产,电力/燃气/水,网络，制药，农业，零售，制造,交通，体育，宇宙航空，广告，IOT，ICT等行业。

Ø AI记者 体育 智能驾驶 AI会计 律师 AI手术机器人 AI问诊 误诊没 ai机器人葬礼 VR AR 3D AI犯罪追踪 AI图像识别。中国科学技术协会、江苏省浙江省人民政府主办的大赛中获得A类一等奖。大健康产业：生物医药、医疗器械、智慧医疗、健康管理等获得A类一等奖。

Ø 主要研究领域为计算机视觉，大数据 区块链，自然语言 处理。阿里巴巴集团首席技术专家

Ø AI深度学习发明的橄榄球5G技术（用于图像识别和语音识别的人工智能（AI）技术之一） 开发了结合了深度学习的橄榄球游戏分析系统，用相机拍摄人体运动，测量AI经过时的身体姿势和运动，AI指导5G技术数据挖掘、人工智能：1、网络分析（社交网络）2、数据聚类3、医学数据处理4、推荐算法5、精准教育

Ø 清华大学的机器学习教授。与技术大趋势，尖端分析技术，政府/地方政府，教育/医疗/医疗保健，金融，制造，物流，通信/广播，建筑/房地产，电力/燃气/水，网络，制药的集成用于农业，零售，制造业，运输，体育，航空航天，广告，物联网，信息通信技术和其他行业。英国剑桥大学教授，清华大学计算机科学客座教授，中国科学技术协会江苏省政府京都大学客座教授赞助的竞赛类别A赢得大健康产业一等奖：生物医学，医疗设备，智能医学，保健等。获得类别A最佳奖。 ERP区块链云技术大数据人工智能相关领域（不限于语音处理领域包括人工智能各个领域）在专利产品竞赛，阿里，腾讯，华为等合同中获得最高奖并达成了众多合同。 AI x 5G刷脸已成为主流支付方式，所见即所得，短视频AI动画x 5G，AR VR和3D，智能驾驶，金融，5G远程医疗5G AI药品，智能驾驶，智能商务，智能商务，智能医疗，公安5G机器人5G材料，半导体，体育，娱乐和其他技术是我们的主流技术，分离和纯化，创新药物，生物技术，芯片设计，量子点显示，多点触控，纳米微球，低碳纳米材料智能驾驶，智能制造，机器人技术和智能医疗的全球关键技术。面部和身体分析技术，SLAM和3D视觉，通用和专业图像识别，机器人控制和传感，大规模视频理解和挖掘，增强医学图像分析的图像和视频处理，人工智能计算平台，人工智能超级计算平台，自主开发的培训框架，人工智能高性能存储通过结合高性能异构计算软件和硬件，高性能，设计和开发低成本，低功耗的边缘人工智能芯片和解决方案，以开拓合作伙伴。对于智能驾驶和AIoT，可以为边缘到边缘的AI芯片提供超高性价比，极高的能效，开放的工具链，丰富的算法模型样本以及全面的激活服务。现在，基于创新的人工智能专用计算架构的BPU（脑处理单元）正在成功流式传输。中国第一台边缘人工智能处理器-专注于智能驾驶的系统和专注于AIoT的系统。并且已经大规模商业化。中国人工智能学会，英国科学局委员以及英国众议院科学技术委员会成员，英国众议院（科学技术委员会）英国文化协会开放数据研究所（ODI）艾伦·图灵人工智能研究所，剑桥大学，爱丁堡大学，牛津大学，伦敦大学，包括华威大学EPSRC协会

Ø 日本京都大学特定客座副教授，奈良先端科学技术大学院大学客座副教授。2017年至2019年在日本京都大学担任日本文部科学省公共安全领域重大产学研结合项目的特任研究员。担任奈良先端科学技术大学院大学与CMU联合创建的国际机器人与视觉联合研究室特任助理教授和实验室实际负责人。共深度参与或主持过10余项国家级和国际合作项目（包括973，863，基金委重大研究计划，日本文部科学省的科学技术战略推进费重大专项等大课题，也包括国家基金委面上项目、重点项目，以及日本文部科学省的科研费青年项目，日英双边合作项目，微软亚洲研究院资助项目等），共发表双盲评审的英文论文60余篇（含顶会10余篇）。研究方向主要集中在基于图像或视频的内容理解。当前正在从事的研究包括人物身份识别，人与人以及人与机器之间交流与交互的视觉理解，人手和人体的姿态估计，动作识别，行为预测等。其中人手姿态估计在首届全球公开挑战赛中获得手物交互单项冠军。指导的访问学生的工作获得ICPR 2018 Piero Zamperoni最佳学生论文奖和AutoML2018 Workshop最佳论文奖。

Ø 适用于AI，IoT，RPA，OCR-AI，ERP，云，大数据，区块链，ICT，5G，3D，AR，VR，iCLIP，核心工业软件，核心算法，中微子，政府/地方政府的最先进技术教育/医疗/保健，金融，制造，物流，电信/广播，建筑/房地产，电力/煤气/水，网络，制药，农业，零售，制造，运输，体育，航空航天，广告，物联网，ICT和其他产业

Ø IEEE，NIPS，ICML，COLT，CVPR，ICCV，ECVC，IJCAI，AAAI，UAI，KDD，SIGIR，WWW，ACL，PAMI，IJCV，JMLR，AIJ已出版了100多次。

Ø 国龙是人工智能和深度学习领域的带头人，英国（谷歌）研究院CTO，领导超过 40 人的算法研究团队，累计专利 300 余项。上世纪 80 年代起就从事人工智能在无人驾驶汽车和医学心电图辅助诊断等领域的相关研究。1992 年加入新加坡松下研究院，从事音视频信号处理和压缩算法设计和应用，随后专注于图像识别领域的研究工作。2013 年就率先带领团队转向深度学习方向，在深度学习、计算机视觉领域取得了瞩目的成绩，拥有计算机视觉的全栈技术能力，技术面横跨多个应用领域，在基于深度学习的人脸检测和识别、行人检测和跟踪、行人再识别、车辆识别、自动驾驶、驾驶员行为检测、移动操作机器人等领域均取得了世界顶级成果。2019 年 3 月，申省梅加入国内 AI 公司澎思科技，任首席科学家、新加坡研究院院长，致力于监控与安全、智慧城市、自动驾驶、智能机器人以及 AI 工厂自动化等领域的相关技术研究。在 2019 年，申省梅带领澎思新加坡研究院已经斩获 13 项计算机视觉技术相关世界冠军，遍布人脸识别（Face Recognition）、行人再识别（Person ReID）、车辆再识别（Vehicle ReID）和异常行为检测（Anomaly Detection）等计算机视觉技术多个领域。

Ø 国龙，英国ICF研究所模式识别英国国家重点实验室研究员、担任计算机视觉高级研究员。博士生导师、南京人工智能芯片创新研究院常务副院长。于2017年在剑桥大学获计算机博士学位，2017年在中国科学院自动化研究所获模式识别与智能系统博士学位。目前主要从事深度学习、图像与视频内容分析、人工智能芯片设计等方面研究，在相关领域发表学术论文100余篇，英文编著二本。相关成果曾先后获得中科院卢嘉锡青年人才奖、中科院青年促进会优秀会员奖（、中国电子学会自然科学一等奖、教育部自然科学二等奖、中国图象图形学会科技二等奖等。目前是IEEE、ACM、CCF等国际国内学术组织的成员，担任中国自动化学会模式识别专委会副秘书长。现任国际期刊Pattern Recognition、IET Computer Vision的编委，曾任2010年ICIMCS国际会议主席、HHME 2010组织主席、CCPR 2012出版主席、ICIG 2019 special Session主席。

Ø 他的研究方向包括数据聚类、社交网络、推荐系统、医学数据处理。他一共发表了100余篇学术论文，包括IEEE TPAMI、IEEE TKDE、IEEE TCYB、IEEE TNNLS等国际顶级刊物和KDD、AAAI、IJCAI、CVPR等国际顶级会议。2019年中日人工智能学会优秀博士学位论文奖，他是人工智能权威期刊Journal of Artificial Intelligence Research（JAIR）的副编辑（AE）。他是中国人工智能学会-模式识别专业委员会委员，中国计算机学会-数据库专业委员会委员，中国计算机学会-计算机视觉专业委员会委员，CCF-YOCSEF副主席（2018-2019），CCF分部副主席（2019.3-2021.3）。

Ø 数据分析和人工智能实验室成员，日本国家级实验室理化研究所人工智能项目 （RIKEN AIP）科学家。主要研究方向为机器学习和深度学习。加入香港浸会大学前，在日本理化研究所人工智能项目从事博士后研究（Prof. Masashi Sugiyama杉山将教授团队）。其负责开发针对噪声数据（标签和样本）的鲁棒深度学习方法，成果荣获2019年度理研最佳成就奖（RIKEN BAIHO奖）。。其作为程序委员会委员和审稿人长期服务机器学习顶级会议（ICML, NeurIPS, AISTATS和ICLR）和顶级期刊（JMLR, TPAMI和MLJ），并当选为NeurIPS’20的领域主席（Area Chair）。

Ø 近年来，深度学习在图像分析、语音识别、自然语言处理等诸多问题上取得了突飞猛进的发展，已经成为众多智能系统和应用中不可或缺的关键技术。随着深度学习模型性能不断提高，其计算复杂度和资源消耗也不断增加，这给网络模型的部署带来重大挑战，特别是在资源受限的边缘端设备上尤为显著。如何高效低耗地计算这些网络模型正成为一个关键问题。本报告将从量化学习的角度介绍深度神经网络模型的高效计算。首先介绍基于量化学习的模型压缩和加速方法；然后是基于算子级加速的量化引擎；最后介绍基于量化计算架构和芯片。

Ø 国龙博士现任剑桥大学计算机科学学院教授(终身教职)。英国国家信息通讯技术研究院堪牛津剑桥ICF实验室的计算机视觉组工作。目前主要从事统计机器学习以及计算机视觉领域的研究工作。主持多项科研课题，在重要国际学术期刊和会议发表论文120余篇。担任或担任过副主编的期刊包括：Pattern Recognition, IEEE Transactions on Neural Networks and Learning Systems。多次担任重要国际学术会议(ICCV, CVPR, ECCV等)程序委员。他曾在高中留英剑桥大学本科及获得计算机人工智能方向的博士学位。2016年被澳大利亚研究理事会(Australian Research Council)授予Future Fellowship。

Ø 主要研究方向为机器学习、计算机视觉。2017于英国剑桥大学进行医学图像分析研究，在国际会议和期刊上发表论文70余篇，其中NIPS、CVPR、ICCV、AAAI、IJCAI等CCF- A类会议以及IEEE Trans.期刊（包括IEEE T-PAMI/T-IP/T-NNLS/T-CYB）35篇，多篇CVPR、NIPS论文入选Oral和Spotlight论文。主持国家自然基金青年项目和面上项目、天津市自然科学基金面上项目。担任IJCAI、AAAI、CVPR、ICCV等会议的程序委员会成员或审稿人，担任中国机器学习会议（CCML 2017）本地组织主席，国际期刊IEEE T-PAMI/T-IP/T-NNLS/T-CYB审稿人。

Ø 主要从事多模态数据协同计算方面的研究工作。主持国家自然科学基金项目、科技部“863”计划等课题20余项，获发明专利授权26项，发表学术论文100余篇，其中在国际一流期刊和国际顶级会议发表论文近80篇，包括IEEE TNNLS, TIP, TCYB, ICML, NIPS, ICCV, CVPR等。被Google Scholar引用2200余次，18位中美英等国院士/Fellow引用他的研究工作并给予了积极评价。研究成果获国家自然科学二等奖1项（2016，第三完成人）、陕西省科学技术奖一等奖2项（2011, 2015，第三完成人）、教育部自然科学二等奖1项（2013）。

Ø 学术成果：在如TPAMI、TIP等IEEE/ACM会刊和CVPR、ICCV和AAAI等CCF A类学术会议上发表文章30余篇。所发表文章谷歌学术引用逾1200次，单篇所载刊物最高影响因子17.73。其在微表情分析方面的相关工作见诸美国《麻省理工技术评论》和英国《每日邮报》等国际权威媒体专文报道。主持科技部重点研发计划课题一项，曾先后主持芬兰信息技术学会博士后科研基金项目和芬兰科学院ICT2023专题项目（含共同主持），并参与多项芬兰科学院基金项目和中国国家自然科学基金项目。教学和学生培养：先后培养和联合培养了七名研究生，并讲授过《深度学习及其应用》和《情感计算》等课程。学术：IEEE/IEEE-CS/AAAI/CCF会员，多次受邀担任IEEE T-PAMI、T-IP、T-NNLS、T-CYB、T-CSVT、T-MM，Springer IJCV，Elsevier Pattern Recog. 等重要国际学术期刊和IEEE ICCV、IEEE CVPR、AAAI、ACM MM等重要国际学术会议的领域主席、程序委员或审稿人，并获得过国际期刊Pattern Recognition（2017）和Neurocomputing（2017）的杰出审稿人称号。与国际同行先后在主流国际会议上举办了五场专题研讨会（CVPR20, FG18-20, ACCV16），并担任过施普林格SIVP期刊的客座编委及多个国际会议/专题的分会主席和程序委员会成员

Ø 国龙教授于自2019年7月全回国以来，主持了国家自然科学基金青年项目、江苏省新一代人工智能重点研发项目等多个重点项目。谭明奎教授一直从事机器学习和深度学习方面的研究工作，在深度神经网络结构优化及理论分析方面具有一定的研究基础。近年来以一作或者通讯作者完成的相关成果发表于人工智能顶级国际会议如NIPS、ICML、ACML、AAAI、CVPR、IJCAI和人工智能权威期刊如IEEE TNNLS、IEEE TIP、IEEE TSP、IEEE TKDE、JMLR等。面向人工智能国家战略及重大需求，长期从事仿生成像、类脑识别、仿脑评价等方面的理论和应用研究。主持国家自然基金面上、军科委国防创新项目、教育部联合基金青年人才项目等多项国家课题，协同主持国家自然基金重点项目、十三五航天预研项目等项目，开发出多套智能信息处理系统并交付使用。已发表相关学术论文60余篇（第一作者40余篇），其中SCI源期刊论文40余篇，三篇入选ESI高被引论文。

Ø 国龙，博士，哈牛桥智能科技南京研究院院长，南京大学学生创业导师，主要研究领域为计算机视觉和机器学习，在相关领域顶级期刊如IEEE TPAMI、IEEE TIP、IEEE TNNLS、Machine Learning Journal等及顶级会议如CVPR、ICCV、IJCAI、ICDM、ACCV等发表论文二十余篇，并带队获得iNaturalist、Apparent Personality Analysis等国际权威计算机视觉竞赛共3项世界冠军。在重要国际会议PRICAI 2018和ICME 2019分别组织题为“Fine-Grained Image Analysis”的tutorial。著有《解析深度学习——卷积神经网络原理与视觉实践》一书。曾获CVPR 2017最佳审稿人、南京大学博士生校长特别奖学金等荣誉，担任ICCV、CVPR、ECCV、NIPS、IJCAI、AAAI等国际会议PC member。NEC美国硅谷实验室从事研究工作。研究方向为海量多媒体信息检索与计算机视觉，专注于行人重识别、物体细粒度识别、场景理解研究。以第一/通讯作者在IEEE T-PAMI、TIP、TMM等权威国际期刊以及ICCV、CVPR、ACM MM、AAAI等权威国际会议发表论文50余篇。，中国计算机学会优秀博士学位论文、中科院优秀博士学位论文、微软学者奖等。先后主持国家自然科学基金面上、重大研发计划培育项目、国家重点研发计划等项目。

Ø 主要研究兴趣包括核算法、多视图聚类算法、特征选择算法等。发表论文90余篇，SCI 46篇（其中IEEE汇刊17篇，包括IEEE T-PAMI、IEEE T-KDE、IEEE T-IP、IEEE T-IFS、IEEE T-NNLS、 IEEE T-Cybernetics、IEEE T-MM等），中国计算机学会指定的A类会议论文23篇，谷歌学术引用1600余次，3篇进入ESI计算机科学学科前10%层次。担任国际人工智能领域顶级会议AAAI 2016-2019、IJCAI 2016-2019、NIPS 2016-2018等程序委员会委员、AAAI 2020高级程序委员会委员。主持国家自然科学基金青年基金、面上项目、优秀青年基金各一项，研究成果获浙江省自然科学一等奖。

Ø VALSE执行AC、CASIG-BVD委员。主要研究领域为深度学习、模式识别、计算机视觉与多媒体分析，尤其专注于无约束/大规模/小样本/异质/基于集合的人脸识别、基于深度对抗学习的图像生成、超分辨率、基于知识蒸馏的模型压缩、（非）实例级细粒度人物解析。目前，共承担/参与重点研发项目 3 项（排名第1/3/5）。在T-PAMI、IJCV、T-IP、NeurIPS、CVPR、IJCAI、ECCV、ACM MM、AAAI、BMVC、WACV等本领域权威国际期刊/会议上发表学术论文30余篇，单篇最高影响因子17.73。相关工作获得新加坡模式识别与机器智能协会PREMIA 2019 Lee Hwee Kuan奖（金奖）、国际多媒体领域顶级会议ACM MM 2018最佳学生论文奖；ICCV 2017 MS-Celeb-1M大规模人脸识别竞赛Hard Set/Random Set/Low-Shot Learning全部任务冠军；CVPR 2017 L.I.P竞赛人物解析与人物姿态估计全部任务亚军；美国国家标准与技术研究院NIST 2017 IJB-A无约束人脸识别竞赛人脸验证与人脸鉴别全部任务冠军。担任IJCV、T-MM、T-IFS、T-CSVT、Neurocomputing、NeurIPS（NeurIPS 2018前30%最佳审稿人）、CVPR、ICCV、ECCV、ACM MM、AAAI、ICLR、ICML、UAI等本领域主流国际期刊/会议的受邀审稿人。

Ø 985剑桥本硕博，软件专业，有扎实的理论和技能储备，毕业后一直从事软件开发工作，先后就职AMD、高通、赛灵思等知名企业，目前是赛灵思中国团队第一个AI解决方案高级技术专家

Ø 精通linux系统软件开发，有内核upstream提交。

Ø 现阶段专注于机器视觉方面的深度学习技术领域的应用。具备Caffe/ Tensorflow/ Keras/Pytorch框架模型部署经验（Xilinx平台，Nvidia GPU/Jeston Nano），也具备初级的模型重训能力。

Ø 英语可以作为工作语言

Ø 丰富而广泛技术开发经验支撑的良好技术销售能力。

Ø 技术能力

Ø Linux内核及驱动开发，有丰富的基于X86和arm平台上的Linux内核驱动开发和调试经验。

Ø 熟悉V4L/DVB开源视频驱动框架及ALSA音频驱动框架及FFMpeg/Gstreamer音视频应用框架

Ø 熟悉USB/PCI/Ethernet/I2C/ACPI驱动及相关协议；熟悉networking, TCP/IP, AVB；DPDK；

Ø Linux环境编程，具备C/C++/Shell/Python应用编程经验，掌握git/subversion/perforce等版本控制工具；熟悉Yocto/buildroot；

Ø 熟悉开源社区及相关开发流程，有Linux内核upstream开发经验(xHCI/ACPI等)

Ø 熟悉xilinx fpga soc平台及工具集和嵌入式软件栈。

Ø 机器视觉相关（CNN/OpenCV/Tensorflow/Caffe/Pytorch）

其他基础专业技能

Ø C/ C++/ Python/ Shell/ Gdb/ Git

Ø U-boot/ FreeRTOS

Ø Yocto/ Buildroot

Ø QT/ FFMpeg / GStreamer

Ø Docker/Xen/KVM

Ø FPGA HLS programming and IPI design

Ø Xilinx tools(Vivado/ SDK/ SDSoC/ Petalinux/ DNNDK/ xfDNN)

Github https://github.com/guolong70

学术荣誉

著论1.在瞬时情感分析方面的相关工作受到了包括美国《麻省理工技术评论》和英国《每日邮报》在内的国际主流技术媒体的专文报道（2019年）；

2. 在包括ICCV 2019（Oral录取率4.3%）在内的多个重要国际会议上获口头报告资格。

3. 担任重要国际会议ACM MM 2020、IEEE IPTA 2016及IEEE PCSPA 2011的Area Chair。

4. 在国际主流学术会议上组织专题研讨会五次: IEEE CVPR 2020，ACCV 2016，IEEE FG 2018/2019/2020。

5. 获评Elsevier期刊《Pattern Recognition》（2017年，最新影响因子5.898）,《Neurocomputing》（2017年，最新影响因子3.317）的“杰出审稿人”（Outstanding Reviewer）

6. 曾担任国际期刊Springer《Signal, Image and Video Processing》(SIVP)的客座编委（Guest Editor）（2019年）。

7. 关于视觉语言编码的综述文章《A Review of Recent Advances in Visual Speech Decoding》被Elsevier国际期刊《Image and Vision Computing》列为“编委推荐文章”（ Editor's Choice Article）（2014年）。

8. 作为共同指导教师获第六届中国研究生智慧城市技术与创意设计大赛全国一等奖两项（2019年）。

获奖及荣誉:

1. 2018年度自然科学奖一等奖；视觉鲁棒特征提取与非线性分析；全部完成人：赖剑煌,郑伟诗,谢晓华,阮邦志,王昌栋,朱俊勇,马锦华,黄剑；完成单位：中山大学,香港浸会大学.

2. 2016年科技创新青年拔尖人才.

3. 2016年IEEE-杰出青年科学基金获得者.

4. 2015年中国人工智能学会优秀博士学位论文.

5. 2014年中国计算机学会优秀博士学位论文提名奖.

6. SIAM SDM 2013 Student Travel Award.

7. 2012 Microsoft Research Asia (MSRA) Fellowship Nomination Award.

8. IEEE ICDM 2011 Student Travel Award.

9. IEEE ICDM 2010 Honorable Mention Award for the Best Research Paper.

10. IEEE ICDM 2010 Student Travel Award.

科研项目:

1) 2019年度剑桥大学高校基本科研业务费-新兴学科交叉学科资助计划项目，基于脑电数据分析的人工耳蜗术后耳聋患者大脑功能康复系统建立及其临床示范应用，No. 19lgjc10，2019 .01-2020.12，主持。

2) 2019年国家自然科学基金-面上项目，基于相似度学习的异构数据聚类算法研究及其应用，No. 61876193，2019.01-2022.12。

3) 2019年国家重点研发计划项目“社区风险监测与防范关键技术研究”课题5 “‘数据-计算’深度交互的社区风险情景计算与预测技术”，No. 2018YFC0809705，2018.07-2021.06，课题5中山大学负责人。

4) 2019年“高校创新创业教育项目” 大学生创新创业项目综合信息服务平台建设， No. 2019PT204，2019.01-2020.12，参与方主持。

主要学术:

1) Associate Editor

- Journal of Artificial Intelligence Research (JAIR, CCF B, Since Aug. 2019).

2) Conference Co-Chairs:

- PRCV 2018, Website Co-chair.

3) Program Committee Members:

- IEEE ICDM 2014, 2015, 2016, 2018, 2019.

- AAAI 2017, 2018, 2019, 2020.

- KDD 2019, 2020.

- IJCAI 2019, 2020.

- CIKM 2019.

- IJCAI 2018 Demo Track, IJCAI 2019 Demo Track, IJCAI 2020 Demo Track.

- The 8th IEEE International Conference on Big Knowledge (IEEE ICBK) 2017.

- The 4th IEEE International Congress of Big Data Congress 2015.

4) Reviewers:

- IEEE TPAMI, IEEE TCYB, IEEE TKDE, IEEE TNNLS, JMLR, IEEE TII.

- Pattern Recognition, Neural Networks, Neurocomputing, Knowledge-Based Systems, Information Sciences, KAIS.

- Many other good journals...

著作:

1. Please kindly refer to my Google scholar homepage： https://scholar.google.com/citations?user=Vkzd7MIAAAAJ&hl=zh-CN 【pdf】

刊物服务:

· Associate Editor of Frontier of Computer Science (2018-)

· Associate Editor of IEEE Trans. on Image Processing (2015-2018, a Rank-A journal by CCF)

· Associate Editor of Journal of Computer Vision and Image Understanding (2017-)

· Associate Editor of Pattern Recognition Letters (2017-)

· Associate Editor of Neurocomputing (2012-2016)

· Editor Board member of EURASIP Journal of Image and Video Processing

· Associate Editor of IPSJ Transactions on Computer Vision and Applications (CVA)

· Associate Editor of IET Computer Vision (2020-)

会议服务:

· Area Chair of IEEE Conference on Face and Gesture Recognition 2020 (FG2020)

· Senior PC of the 34th AAAI Conference on Artificial Intelligence (AAAI-20)

· Area Chair of IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020

· Area Chair of IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019

· Area Chair of IEEE International Conference on Biometrics: Theory, Application AND Systems (BTAS 2018)

· Area Chair of IEEE International Conference on Automatic Face and Gesture Recognition (FG 2018)

· Area Chair of Asian Conference on Computer Vision (ACCV) 2018

· Area Chair of International Conference on Computer Vision (ICCV) 2011

· Program Chair of Chinese Conference on Biometric Recognition 2014, 2015, 2016

· Area Chair of International Conference on Pattern Recognition (ICPR) 2012

· Area Chair of Asian Conference on Computer Vision (ACCV) 2012

· Area Chair of International Conference on Face and Gesture Recognition (FG2013)

· Workshop Chair of Asian Conference on Computer Vision (ACCV) 2014

· Area Chair of Asian Conference on Computer Vision (ACCV) 2016

· Area Chair of International Conference on Pattern Recognition (ICPR) 2014

1.Machine learning: deep learning and beyond

重点关注复杂数据条件下的机器学习方法和技术，特别是面向小规模、弱标注、半监督、非完备、非纯净等数据条件下的机器学习方法，研究思路包括迁移学习、元学习、知识引导的学习算法等。

My team focuses on new machine learning methods for scenarios with complex data conditions, especially small data, wealy-labeled data, semi-supervised data, incomplete data, where new models and new optimizing methodsareneeded to design. In terms of methodology, we are interested in transfer learning, meta-learning, and knowledge-guided learning.

2. From face recognition to deep human understanding: methods, technologies, and applications

从事人脸检测与跟踪、面部关键特征点定位、人脸识别与验证、表情分析与识别、人脸属性估计、唇语识别、心率估计、专注度估计、视线估计与跟踪等与人脸感知和深度理解计算相关的研究课题。应用合作单位包括：华为、平安、百度、银晨科技、三星、欧姆龙、松下、高通、中移动等，2016年和2017年两次获华为优秀合作成果奖。相关成果还曾获2015年度国家自然科学二等奖等，2005年度国家科技进步二等奖。

My team is interested in all kinds of vision tasks from face recognition to human understanding, including (but not limited to) face detection and tracking, facial landmark locating, face alignment, face identification, face verification, face retrieval, expression recognition, facial attribute estimation, 3D face reconstruction, face parsing, lip reading, heart rate estimation, engagement estimation, gaze tracking, etc. We have broad cooperation with industrial parters, including Huawei, Ping'an, Qualcomm, China Mobile, Baidu, Isvision, Samsung, Omron, Panasonic, etc. Especially, our face recognition technology has been used on Huawei smart phone and Huawei Cloud album.

3. Generic object detection, segmentation, and recognition

关注通用目标检测、分割和识别等计算机视觉任务，重点关注面向视频监控场景的行人检测与跟踪、人体姿态估计与分割、车辆检测与跟踪、行人再识别和车辆再识别等研究课题。

My team is also interested in generic object detection, segmentation, and recognition, especially pedestrian detection and tracking, human pose estimation and body segmentation, vehicle detection and tracking, human and vehicle re-identification etc. These technologies are applied to video surveillance.

1. 复杂样本条件下的深度学习方法与技术

项目类型：科技部重点研发计划项目（课题）

项目时间：2018-2022

项目负责人：guolong（课题负责人）

2. 视频大数据多种类对象检测技术合作项目

项目类型：企业合作项目

项目时间：2016-2017

项目负责人：guolong

3. 人脸识别技术合作项目

项目类型：企业合作（华为）

项目时间：2016-2019

项目负责人：guolong

4. 视觉模式分析与识别

项目类型：基金委优青项目

项目时间：2013-2015

项目负责人：guolong

References 研究成果

[1] Guolong et al. "3D convolutional neural networks for

human action recognition." IEEE transactions on pattern

analysis and machine intelligence 35.1 (2013): 221-231.

[2] Guolong Ng, Joe, et al. "Beyond short snippets: Deep

networks for video classification." Proceedings of the IEEE

conference on computer vision and pattern recognition. 2015.

[3] Guolong et al. "Temporal segment networks: towards

good practices for deep action recognition." European

Conference on Computer Vision. Springer International

Publishing, 2016.

[4] Guolong et al. “Large-scale video classification

with convolutional neural networks.” Proceedings of the IEEE

conference on computer vision and pattern recognition. 2014.

[5] Guolong et al. “Multi-Stream Multi-Class Fusion of

Deep Networks for Video Classification.” 2015

[6] Guolong Seyed Morteza, et al. "Sports videos in the

wild (SVW): A video dataset for sports analysis." Automatic

Face and Gesture Recognition (FG), 2015 11th IEEE

International Conference and Workshops on. Vol. 1. IEEE,

2015.

[7] Guolong Reza Fuad, Keiichi Uchimura, and Gou Koutaki.

"Combined Convolutional Neural Network for Event

Recognition." Korea-Japan Joint Workshop on Frontiers of

Computer Vision. 2016.

[8] Guolong Christian et al. “Inception-v4, Inception-Resnet

and the Impact of Residual Connections on Learning.” 2016

[9] Guolong, Christian et al. “Going Deeper with

Convolutions.” Proceedings of the IEEE conference on computer

vision and pattern recognition. 2015.

[10 Guolong et al. “Deep Residual Learning for Image

Recognition.” 2015.

专业经验

专业经验 管理 China CEO/CTO &President 中国区首席执行官CTO兼总裁

2012/01 - 至今

阿里巴巴合同企业哈牛桥智能科技Co. Ltd.(10年）

计算机服务(系统、数据服务、维修) | 500-1000人 | 外资（欧美） | 全职

管理

China CEO&CTOPresident 中国区首席执行官CEO兼CTO总裁

工作描述：

2012/1—至今: 阿里巴巴合同企业哈牛桥智能科技 Co. Ltd.

职位：China CEO&President 中国区首席执行官CEO兼总裁

公司简介：

阿里巴巴合同企业哈牛桥智能科技是IT产品销售、咨询、实施和服务的公司，公司汇聚了众多来自于全球在Oracle、Microsoft、SAP、IBM、EMC、HP、 DELL、Cisco 等世界500强顶尖的IT&互联网公司有着丰富工作经验的高级管理人员、业务拓展人员和技术精英加盟组建的公司，中国公司是杭州、在欧洲英国 日本和中国上海有分支机构。

公司主要业务：

公司是美国Oracle公司全球战略合作伙伴，公司主要业务是从事基于Oracle数据库、中间件、ERP、CRM、SCM、HRM、BPM、EPM、APM、PLM、BI、互联网、大数据、Oracle云计算（DaaS、IaaS、PaaS、SaaS）AI(人工智能）全线产品以及基于Microsoft、SAP、IBM、EMC、HP、 DELL、Cisco 等IT&互联网多领域产品，向在美国、欧洲、中国的客户提供最优的IT&互联网系统解决方案的咨询、实施和服务…

中国公司员工人数：600+人

工作地点：上海 南京 杭州

我的工作：向董事长(BOSS)汇报

我的直接下属30人：销售&市场副总裁、技术副总裁、人力资源&行政副总裁、财务副总裁

我的职责：

1、经总公司董事会全权授权，全面领导中国公司的日常运营管理工作，对各项工作负总责；

2、全面主持制定中国公司的每年的业务发展战略、业务发展策略、业务发展计划、实现中国公司的年度业务发展目标；

3、全面负责中国公司的高层管理团队的建立、培养、选拔中高层管理人员，审定公司内部管理机构的设置方案和基本管理制度；

4、全面主持建立高效的中国公司组织体系（运营管理体系（行政部、人事部、财务部）业务发展体系（市场部、销售部）、技术支持服务体系（售前部、售中部、售后部）和组织流程；

5、根据中国市场和环境的变化，适时调整中国公司的组织架构和资源配置，时时管理、监督中国公司的日常运营情况，决策公司日常重大事项；

6、负责与国外和国内的合作伙伴公司高层的紧密接触和合作；

7、负责与客户高层（拍板层、决策层）的公关和互动；

8、负责与公司董事会保持良好沟通，向公司董事会汇报中国公司的经营发展计划的执行情况资金运用情况和盈亏情况；

下属人数：600 | 汇报对象：美国董事长(BOSS) | 离职原因：在职。

主要业绩：

我的业绩：

2019-2020: 完成业绩：5亿元人民币

2018- 2019: 完成业绩：3.5亿元人民币

2017-2018: 完成业绩：3亿元人民币我签约的客户有：

我们为以下客户提供Oracle、SAP、Microsoft、IBM、HP、DELL、EMC、Cisco、Salesforce、Google、Amazon等最优、最先进的IT&互联网、物联网、智能网、云计算、大数据、AI人工智能，等系统的解决方案的咨询+系统的实施+系统的日常维护+定制开发等增值服务。。。

银行、证券、保险、行业客户有：

美国银行

花旗银行

汇丰银行

德意志银行

上海浦东发展银行

上海交通银行

中国银联

上海银行

兴业银行

上海农商银行

中国民生银行

光大银行

青岛银行

宁波银行

重庆商业银行

深圳发展银行

南京银行

上海证券

东方证券

广发证券

申银万国宏源证券

海通证券

中国银河证券

中国太平洋保险

中国人寿保险公司

政府行业客户有：

上海市财政局

上海市公安局

上海海关

上海市人事局

上海市人力资源和社会保障局

上海市水务局

上海市教育委员会

浙江省财政厅

浙江省公安厅

浙江省人力资源和社会保障厅

江苏省财政厅

江苏省公安厅

江苏省人力资源和社会保障厅

教育行业客户有：

上海财经大学

上海交通大学

上海大学

上海理工大学

上海政法大学

医疗行业客户有：

上海卫健委

上海仁济医院

上海华山医院

上海瑞金医院

上海东方医院

汽车制造、航空、玻璃制造、钢铁制造、食品制造、纸业制造、电器制造、日用消费品制造、行业客户有：

上海汽车集团

上海大众汽车

上海通用汽车

中国东方航空

上海宝武汉钢集团

上海国际港务集团

上海光明食品集团

上海烟草集团

上海医药集团

上海电气集团

上海家化

宝洁（中国）公司

美的集团

联合利华中国公司

南京汽车集团

天津天狮集团

重庆长安汽

上海外高桥造船

青岛啤酒

中国中纺集团

维达纸业

上海飞科电器

上海晨光文具

上海日立电器

上海冠生园

电信行业客户有：

浙江省移动通信公司

上海移动通信公司

上海电信

江苏省移动通信公司

四川省移动通信公司

2012/01 - 2017/01

受雇于美国 微软Software System Co. Ltd.(6年）

计算机服务(系统、数据服务、维修) | 150-500人 | 外资（欧美） | 兼职

中国区首席执行官兼总裁

China CEO&CTOPresident 英国中国区CTO首席执行官CEO兼总裁

工作描述：

2012—2017:受雇于美国微软 Software System Co. Ltd.

职位 英国China CEO&CTOPresident 英国中国区CTO首席执行官CEO兼总裁

公司简介：

美国微软 Software System Co. Ltd.是在美国成立的IT产品的销售、咨询、实施和服务公司。公司汇聚了众多来自于全球在Oracle、Microsoft、SAP、IBM、EMC、HP、 DELL、Cisco 等世界500强顶尖的IT公司有着丰富工作经验的高级管理人员、业务拓展人员和技术精英加盟组建的公司，美国公司是总部、在欧洲英国和中国上海有分支机构。

公司主要业务：

公司是美国全球战略合作伙伴，公司主要业务是从事基于Oracle数据库、中间件、ERP、CRM、SCM、HRM、BPM、EPM、APM、PLM、BI、互联网、大数据、Oracle云计算（DaaS、IaaS、PaaS、SaaS）全线产品以及基于Microsoft、SAP、IBM、EMC、HP、 DELL、Cisco 等IT多领域产品，向在美国、欧洲、中国的客户提供最优的IT系统解决方案咨询、实施和服务…

英国员工人数：200+人

中国公司员工人数：400+人

工作地点：英国，上海

我的工作：向美国总部董事长(BOSS)汇报

我的直接下属30人：销售&市场副总裁、技术副总裁、人力资源&行政副总裁、财务副总裁

我的职责:

1、经美国总公司董事会全权授权，全面领导中国公司的日常运营管理工作，对各项工作负总责；

2、全面主持制定中国公司的每年的业务发展战略、业务发展策略、业务发展计划、实现中国公司的年度业务发展目标；

3、全面负责中国公司的高层管理团队的建立、培养、选拔中高层管理人员，审定公司内部管理机构的设置方案和基本管理制度；

4、全面主持建立高效的中国公司组织体系：运营管理体系（行政部、人事部、财务部）、业务发展体系（市场部、销售部）、技术支持服务体系（售前部、售中部、售后部）和组织流程；

5、根据中国市场和环境的变化，适时调整中国公司的组织架构和资源配置，时时管理、监督中国公司的日常运营情况，决策公司日常重大事项；

6、负责与国内和国外的合作伙伴公司高层的紧密接触和合作；

7、负责与客户高层（决策层、拍板层）的公关和互动；

8、负责与美国总公司董事会保持良好沟通，向美国总公司董事会汇报中国公司的经营发展计划的执行情况资金运用情况和盈亏情况；

下属人数：400 | 汇报对象：美国总部董事长（BOSS) | 离职原因：剑桥大学在学期间兼职，毕业留英

主要业绩：

我的业绩；

2016-2017: 完成业绩： 5亿元人民币

2015-2016: 完成业绩：3亿元人民币

2014-2015: 完成业绩：2.5亿元人民币

2013-2012: 完成业绩：2亿元人民币

2011-2012: 完成业绩：1.5亿元人民币

签约的客户有：

我们为以下客户提供Oracle、SAP、Microsoft、IBM、HP、DELL、EMC、Cisco、Salesforce、的最优、最先进的IT系统的解决方案的咨询+系统的实施+系统的日日常维护+定制开发等增值服务。。。

银行、证券、保险、行业客户有：

美国银行

花旗银行

汇丰银行

德意志银行

上海浦东发展银行

上海交通银行

中国银联

上海银行

兴业银行

上海农商银行

中国民生银行

光大银行

青岛银行

宁波银行

重庆商业银行

南京商业银行

广东发展银行

深圳发展银行

厦门市商业银行

南京银行

上海证券

东方证券

广发证券

申银万国宏源证券

海通证券

中国银河证券

中国太平洋保险

中国人寿保险公司

政府行业客户有：

上海市财政局

上海市公安局

上海海关

上海市人事局

上海市人力资源和社会保障局

上海市水务局

上海市教育委员会

浙江省财政厅

浙江省公安厅

浙江省人力资源和社会保障厅

江苏省财政厅

江苏省公安厅

江苏省人力资源和社会保障厅

教育行业客户有：

上海财经大学

上海交通大学

上海大学

上海理工大学

上海政法大学

医疗行业客户有：

上海卫健委

上海仁济医院

上海华山医院

上海瑞金医院

上海东方医院

汽车制造、航空、玻璃制造、钢铁制造、食品制造、纸业制造、电器制造、日用消费品制造、行业客户有：

上海汽车集团

上海大众汽车

上海通用汽车

中国东方航空

上海宝武汉钢集团

上海国际港务集团

上海光明食品集团

上海烟草集团

上海医药集团

上海电气集团

上海家化

宝洁（中国）公司

美的集团

联合利华中国公司

南京汽车集团

天津天狮集团

重庆长安汽

上海外高桥造船

青岛啤酒

中国中纺集团

维达纸业

上海飞科电器

上海晨光文具

上海日立电器

上海冠生园

电信行业客户有：

浙江省移动通信公司

上海移动通信公司

上海电信

江苏省移动通信公司

四川省移动通信公司

项目经验

2017/01 - 至今

我签约的客户有：

所属公司：

阿里巴巴，亚马逊,腾讯等名企

项目描述：

我签约的客户有：

我们为以下客户提供Oracle、SAP、Microsoft、IBM、HP、DELL、EMC、Cisco、Salesforce、Google、Amazon、的最优、最先进的IT&互联网、云计算、大数据、AI人工智能等系统的解决方案的咨询+系统的实施+系统的售后维护服务+定制开发等增值服务。。。

银行、证券、保险、行业客户有：

美国银行

花旗银行

汇丰银行

德意志银行

苏格兰皇家银行

上海浦东发展银行

上海交通银行

中国银联

上海银行

兴业银行

上海农商银行

中国民生银行

光大银行

青岛银行

宁波银行

重庆银行

南京银行

广东发展银行

深圳发展银行

厦门市商业银行

苏州银行

江苏银行

上海证券

东方证券

光大证券

爱建证券

广发证券

申银万国宏源证券

海通证券

中国银河证券

中国太平洋保险

长城保险公司

中国人寿保险公司

政府行业客户有：

上海市财政局

上海市审计局

上海市统计局

上海市司法局

上海市公安局

上海海关

上海市浦东教育局

上海市人事局

上海市人力资源和社会保障局

上海市水务局

上海市教育委员会

上海市新闻出版局

浙江省财政厅

浙江省民政厅

浙江省司法厅

浙江省公安厅

浙江省教育厅

浙江省人力资源和社会保障厅

江苏省财政厅

江苏省司法厅

江苏省民政厅

江苏省公安厅

江苏省人力资源和社会保障厅

江苏省教育厅

教育行业客户有：

上海财经大学

上海交通大学

上海大学

上海理工大学

上海同济大学

医疗行业客户有：

上海卫健委

上海仁济医院

上海华山医院

上海瑞金医院

上海东方医院

上海长海医院

汽车制造、玻璃制造、钢铁制造、食品制造、纸业制造、电器制造、日用消费品制造、行业客户有：

上海汽车集团

上海大众汽车

上海通用汽车

东风汽车

江淮汽车

上海宝武钢铁集团

上海光明食品集团

上海烟草集团

上海医药集团

上海电气集团

上海家化

宝洁（中国）公司

美的集团

武汉丝宝集团

南京汽车集团

天津天狮集团

重庆长安汽车

重庆长安铃木汽车

江西五十铃汽车

上海耀皮玻璃

上海外高桥造船

济南钢铁集团

青岛啤酒

中国中纺集团

维达纸业

上海飞科电气

陕西重型汽车

上海晨光文具

上海日立电器

上海冠生园

电信行业客户有：

浙江省移动通信公司

上海移动通信公司

上海电信

江苏省移动通信公司

四川省移动通信公司

责任描述：

在以上项目中我作为中国区首席执行官CEO/CTO的角色统筹整个项目完成。

2012/01 - 2017/01

我签约的客户有：

所属公司：

受雇于美国 微软 Software System Co. Ltd.

项目描述：

我们为以下客户提供Oracle、SAP、Microsoft、IBM、HP、DELL、EMC、Cisco、Salesforce、的最优、最先进的IT系统的解决方案的咨询+系统的实施+系统的日日常维护+定制开发等增值服务。。。

银行、证券、保险、行业客户有：

美国银行

花旗银行

汇丰银行

德意志银行

苏格兰皇家银行

上海浦东发展银行

上海交通银行

中国银联

上海银行

兴业银行

上海农商银行

中国民生银行

光大银行

青岛银行

宁波银行

重庆银行

南京银行

广东发展银行

深圳发展银行

厦门市商业银行

苏州银行

江苏银行

上海证券

东方证券

光大证券

爱建证券

广发证券

申银万国宏源证券

海通证券

中国银河证券

中国太平洋保险

长城保险公司

中国人寿保险公司

政府行业客户有：

上海市财政局

上海市审计局

上海市统计局

上海市司法局

上海市公安局

上海海关

上海市浦东教育局

上海市人事局

上海市人力资源和社会保障局

上海市水务局

上海市教育委员会

上海市新闻出版局

浙江省财政厅

浙江省民政厅

浙江省司法厅

浙江省公安厅

浙江省教育厅

浙江省人力资源和社会保障厅

江苏省财政厅

江苏省司法厅

江苏省民政厅

江苏省公安厅

江苏省人力资源和社会保障厅

江苏省教育厅

教育行业客户有：

上海财经大学

上海交通大学

上海大学

上海理工大学

上海同济大学

医疗行业客户有：

上海卫健委

上海仁济医院

上海华山医院

上海瑞金医院

上海东方医院

上海长海医院

汽车制造、玻璃制造、钢铁制造、食品制造、纸业制造、电器制造、日用消费品制造、行业客户有：

上海汽车集团

上海大众汽车

上海通用汽车

东风汽车

江淮汽车

上海宝武钢铁集团

上海光明食品集团

上海烟草集团

上海医药集团

上海电气集团

上海家化

宝洁（中国）公司

美的集团

武汉丝宝集团

南京汽车集团

天津天狮集团

重庆长安汽车

重庆长安铃木汽车

江西五十铃汽车

上海耀皮玻璃

上海外高桥造船

济南钢铁集团

青岛啤酒

中国中纺集团

维达纸业

上海飞科电气

陕西重型汽车

上海晨光文具

上海日立电器

上海冠生园

电信行业客户有：

浙江省移动通信公司

上海移动通信公司

上海电信

江苏省移动通信公司

四川省移动通信公司

责任描述：

在以上项目中我作为中国区首席执行官CTO/CEO的角色统筹整个项目完成。

工作经历项目经历学术研究经历成果*

2012-2017 微软开发团队软件硬件开发工程师谷歌开发团队 PM 剑桥大学在读期间

2017/1–至今 The University of Cambridge Cambridge博士OXBridge教授

Leverhulme Centre for the Future of Intelligence，LCFI http://www.lcfi.ac.uk/英国LCFI实验室顶尖研究院首席科学家

现任NTT 和Alibaba Group哈牛桥智能科技CEO/CTO， PM部主任 ， 中国科学院研究员 ， JSAI人工智能学会正式会员 ， IEEE会员 ， CAAI中国

人工智能学会会员，AAA1国际人工智能学会会员。他是英国人工智能中国ACM专员ACM的成员，主要研究领域包括ERP咨询，云技术，大数据，区块链，人工智能专家，计算机视觉，多媒体技术和机器学习。

清华大学的机器学习教授。与技术大趋势 ， 尖端分析技术 ， 政府/地方政府 ， 教育/医疗/医疗保健 ， 金融 ， 制造 ， 物流 ，通信/广播，建筑/房地产，电力/燃气/水，网络，制药的集成用于农业，零售，制造业，运输，体育，航空航天，广告，

物联网 ， 信息通信技术和其他行业。哈佛商学院客座教授 ， 清华大学计算机科学客座教授 ， 东京大学客座教授 ， 东京大学客座教授 ， 大阪大学客座教授 ， 中国科学技术协会江苏省政府

京都大学客座教授赞助的竞赛类别A赢得大健康产业一等奖: 生物医学，医疗设备，智能医学，保健等。获得类别A最佳奖。 ERP区块链云技术大数据人工智能相关领域（不限于语音处理领域包括人工智能各个领域）在专利产品竞赛，阿里，腾讯，华为等合同中获得最高奖并达成了众多合同。 AIx 5G刷脸已成为主流支付方式，所见即所得，短视频AI动画x 5G，AR VR和3D，智能驾驶，金融，5G远程医疗5G AI药品，智能驾驶，智能商务，智能商务，智能医疗，公安5G机器人5G材料，半导体，体育，娱乐和其他技术是我们的主流技术，分离和纯化，创新药物，生物技术，

有深度学习学术工程项目经验 10 年以上 熟悉人工智能相关的算法和理论，特别是神经网络、深度学习、增强学习及迁移学习等，熟悉SAS, R, Python, Spark SQL, Spark ML 等数据分析工具和语言，有 LR/GMM/SVM/CRF/MaxEnt/HMM/LDA/DNN/CNN/RNN 的研究背景；

◎熟练掌握 CUDA 编程，熟练掌握 Python/Matlab/C++等利用自主知识产权的深度学习架构、机器视觉、生物智能识别等人工智能算法、无媒介支付等核心技术，在自动驾驶、智能机器人、生物智能、AI 芯片、智能零售、智慧城市、智慧安防、智能教育、宇宙航空日本小行星，军工等领域都有深入布局，居行业领导地位；

◎人工智能应用于制造业（机器人，优化）交通，生物保健，沟通，机器学习模拟，边缘设备，网络，高性能计算，产品开发（普通），产品开发（视觉检测解决方案），人机交互（HCI，HRI）

重要项目经历

◎生物保健行业--与谷歌、微软、腾讯和亚马逊等优秀团队合作，我与哈佛大学医学院基因，京都大学诺贝尔医学基因 iPS 干细胞团队合作

◎与 FANUC，PFN (PREFERRED NETWORKS)和日立合作。生物保健医学图像分析，血液癌早期诊断技术研究进展。国家癌症研究中心等联合研究。区块链技术、人工智能生物医学AI 应用于各个领域，包括医学图像分析、电子健康记录（EHR）的详细研究，以及寻找疾病本源、临床前药物发现和临床试验等基础研究。

项目经历

◎参加了亚马逊人工智能项目(Amazon Go) ：在西雅图的一种实验性食品杂货店成功实施了，它允许购物者在货架上取货，离开收银台，或在自助结账亭驻足。电脑视觉识别他们进入商店，然后将他们与从货架上取下的产品联系起来。当顾客离开时，系统会从他们的亚马逊账户中扣除购物袋的费用，并发送电子邮件收据。

◎参加过德国电商企业 Otto ：使用深度学习模型分析了数十亿宗交易并预测了顾客在下订单之前最倾向于购买的东西，将富余库存降低了 20%，并每年减产超过两百万。这个系统所预测的企业在接下来三十天内畅销的产品准确率达到 90%；

◎法国的全球零售商家乐福人工智能项目，以及美国的塔吉特人工智能，项目基于洞察力的销售，包括个性化的促销，分类优化，和定制的显示器。

◎与富士通合作人工智能项目指纹认证消费系统应用于日本超市，百货店等,日本的乐天人工智能服务自动问答项目

◎日本建筑公司清水建设人工智能项目：我的专利--极限环境下的智能建造（NASA）项目

◎小松公司多用途无人机和推土机自动化系统

◎日本国家先进工业科学技术研究所（AIST）开发的 HRP-5P 机器人开发项目

◎京东全流程无人仓运输机器人项目

◎川崎重工机器人，株式会社神户制钢所电弧焊接机器人 ARCMAN™－GS 的开发

◎欧姆龙机器人项目

◎丰田的“人体支援机器人”（HSR）项目

◎夏普的 Robohon 项目

◎和美国 MIT 媒体实验室的研究人员合作中国三一重工项目

◎阿里巴巴旗下菜鸟网络项目，开发出沛东机器人小 G

◎中建科技建筑智慧建造平台，集 BIM+互联网+物联网技术于一身，可实现全方位、交互式信息传递；

◎トラスコ中山株式会TRUSCO NAKAYAMA CORPORATION机器人IOT自动化物流和SAPHANA*SAPRA导入 日本电视台頑張れ日本电视节目发表了

◎長野計器 株式会社 NAGANO KEIKI CO., LTD.的IOT项目用于医疗产品检测舌头的吞咽能力检测 避免老龄者等人群被食物卡死的危险 还有利用光纤电缆的检测道路的老化率 利用光纤电缆如卡车开过马路产生的震动强度推测出交通道路的老化和桥梁的抗强率

日本电视台頑張れ日本电视节目发表了

◎株式会社MonotaRO Co.,Ltd.人工智能项目AI客服系统和商品AI分析系统

ERP、SAP等方向

◎8年左右ERP咨询顾问实施经验；有 SAP(FI/CO、MM、SD、PP、BW、BI SAP HANA)等模块经验，有 SAP R/3 (Basis)，SAP R/3 (ASAP)，SAP R/3(在庫/購買管理)，SAP R/3 (管理会計)，SAP R/3 (財務管理)，SAP R/3 (生産管理) ,SAP R/3 BI 认证咨询顾问

重要项目经历

日本丰田汽车 SAP 导入项目

项目简介：AI 机器学习特别是深度学习等人工智能领域在汽车界自动驾驶技术和

◎云技术大数据区块链精通 AI 人工智能领域人工智能、深度学习、机器学习方法与应用；自然语言处理自动问答，人机对话系统，情感分析，社交数据挖掘技术专家经验 15 年以上；

◎人工智能/智能驾驶/AI+Fintech&区块链/未来医疗/网络安全 AR/VR 机器人开发者智能硬件/物联网/GAIR 经验；

◎10 年以上嵌入软件开发经验；

◎嵌入式软件开发(Linux/单片机/PLC/DSP…)10年以上；

◎与剑桥大学合作计算机视觉、机器学习、人工智能、数据挖掘、信息检索、自然语言处理、语音识别等领域的计算机科学；

◎与人工智能专家 谷歌、微软、亚马逊、facebook、netflix，苹果 日本丰田公司等项目合作；

◎参与过人工智能系统的设计和开发工应用 Deep Learning 技术，针对语音、视频、图像、文本等富媒体数据 进行建模分析，为相关产品提供.智能化支持，推动业务发展；

◎负责 Deep Learning 领域前沿算法及框架跟踪，搭建面向企业级应用的大规模 Deep Learning 计算平台熟悉 CNN 等典型深度学习模型的使用场景和使用方法；

◎熟悉 ResNet、MobileNet 等常用网络结构，在图像分类、图像分割、物体识别等相关领域有实践经验；

◎熟悉 TensorFlow、Caffe、MXNet 等主流深度学习框架中的一种或多种；有论文发表在相关领域如 ICIP、ICCV、CVPR、ECCV、SIGGRAPH 等对云计算和云端服务器管理有10年以上的经验

◎熟悉阿里云、AWS、Azure、GCP 等;

◎熟悉 C/C++/Python/Java 熟悉计算机网络相关知识、并有扎实的爬虫项目实际经验、爬虫架构工具如 scrapy、 selenium、 beautiful soup 有了解;

◎熟悉 Hadoop生态圈及其他大数据技术、如 Spark、 HDFS、Hive、Impala、ElasticSearch、 Cassandra、Kafka 等对自然语言处理 (NLP) 工具与组件经验10年；

◎熟悉如 Hadoop、Spark、Caffe、Tensorflow 等开源工具，有实际开发经验精通 Java/Python/C++熟悉网络编程、多线程、分布式（Hadoop/Hive/Storm）10年以上实践经验。

◎具备金融风控，互联网反作弊，图像和文本处理，精准营销，推荐系统等相关产品工作经验，人工智能与大数据无人驾驶、深度强化学习、自然语言处理基于文本的图像合成等方面的项目将重点围绕人工智能、

大数据以及 AR/VR、ADAS、⽆⼈机、机器人、IOT、航空航天等科技领域

◎人工智能由三大要素驱动：数据、算法、算力。通过积累下的海量数据，在GPU等高性能芯片支持下，

深度学习可以挖掘数据价值，获得超过人类识别精度的算法，进而实现深度学习部分商业化应用，让人工智能不再局限于学术研究层面

◎计算机视觉、机器学习、数据挖掘、信息检索、自然语言处理、语音识别/合成等领域机器学习、计算机视觉、自然语言处理、机器翻译、语音识别/合成等人工智能领域全球领先企业的 25 年工作经验；

◎精通编程语言，Java、C/C++、C#、Python 等；在计算机科学顶级会议和期刊如 NIPS、ICML、COLT、

CVPR、ICCV、ECCV、IJCAI、AAAI、UAI、KDD、SIGIR、WWW、ACL、PAMI、IJCV、JMLR、AIJ 等发表过论文

◎有深度学习学术工程项目经验 10 年以上 熟悉人工智能相关的算法和理论，特别是神经网络、深度学习、增强学习及迁移学习等，熟悉SAS, R, Python, Spark SQL, Spark ML 等数据分析工具和语言，有 LR/GMM/SVM/CRF/MaxEnt/HMM/LDA/DNN/CNN/RNN 的研究背景；

◎熟练掌握 CUDA 编程，熟练掌握 Python/Matlab/C++等利用自主知识产权的深度学习架构、机器视觉、生物智能识别等人工智能算法、无媒介支付等核心技术，在自动驾驶、智能机器人、生物智能、AI 芯片、智能零售、智慧城市、智慧安防、智能教育、宇宙航空日本小行星，军工等领域都有深入布局，居行业领导地位；

◎人工智能应用于制造业（机器人，优化）交通，生物保健，沟通，机器学习模拟，边缘设备，网络，高性能计算，产品开发（普通），产品开发（视觉检测解决方案），人机交互（HCI，HRI）

重要项目经历

◎生物保健行业--与谷歌、微软、腾讯和亚马逊等优秀团队合作，我与哈佛大学医学院基因，京都大学诺贝尔医学基因 iPS 干细胞团队合作

2017/1 – 至今 AlibabaGroup合同单位 哈牛橋智能科技 国龙 副总经理 CTO

计算机视觉，多媒体技术，机器学习，清华大学的教授。和技术大趋势的融合，领先的分析技术被用于政府/地方政府,教育/医疗/医疗保健,金融,制造业,物流,通讯/广播,建筑/房地产,电力/燃气/水,网络，制药，农业，零售，制造,交通，体育，宇宙航空，广告，IOT，ICT等行业。

AI记者 体育 智能驾驶 AI会计 律师 AI手术机器人 AI问诊 误诊没 ai机器人葬礼 VR AR 3D AI犯罪追踪 AI图像识别。中国科学技术协会、江苏省浙江省人民政府主办的大赛中获得A类一等奖。大健康产业：生物医药、医疗器械、智慧医疗、健康管理等获得A类一等奖。

主要研究领域为计算机视觉，大数据 区块链，自然语言 处理。阿里巴巴集团首席技术专家

AI深度学习发明的橄榄球5G技术（用于图像识别和语音识别的人工智能（AI）技术之一） 开发了结合了深度学习的橄榄球游戏分析系统，用相机拍摄人体运动，测量AI经过时的身体姿势和运动，AI指导5G技术

◎熟悉 C/C++等语言，Matlab、Qt、ROS 等开发工具，Linux 或 QNX 开发环境机器学习/数据挖掘等 AI 相关的算法研发；算法相关的代码库、工具库的封装和发布；AI 相关算法的性能优化、工程环境部署；参与搭建和实现分布式深度学习集群；

◎熟练掌握机器学习相关的理论知识和实践技能；熟悉 CNN、RNN、LSTM 等典型深度学习模型的使用场景和使用方法；

◎熟悉 TensorFlow、Caffe、MXNet 等主流深度学习框架中的多种；拥有扎实的数学和编程功力。 熟悉基本的机器学习算法，了解机器学习在典型行业的应用模式。有开发经验熟悉大数据相关知识；

◎熟悉数据在客户应用系统中的流向以及加工方式，有大数据平台 HD/Hbase/Hive 等基本组件配置能力。具有独立分析客户需求、设计解决方案的能力，并且有完整项目实施经验；

◎擅长沟通，有能力协调解决团队合作、外部合作中遇到的各类问题，负责公司相关业务、产品和服务的后端开发和维护。

重要项目经历

参与过银行、医疗保健、保险业、金融科技、制造业、零售业、市场营销、体育分析等项目

◎日本武田药品工业、富士胶片及盐野义制药等企业人工智能(AI)推进新药开发项目

医疗 DNA 细胞 IPS 人工智能项目-大阪医院等和美国全美各大医院都已经实施 IBM Watson 人工智能项目 涉及人工智能，智能传感器、望远镜、探测器和医学设备的发展，人工智能将使我们的语言成为精神健康的窗口；高级图像传感器将使得人类预备超级视野；超级放大镜将使得人类了解地球的无限细节；所有医学实验室系统将集成于单个计算机芯片；智能传感器将以光速探测环境污染。

◎AI 案件英特尔人工智能项目：英特尔通过优化的机器学习框架和库宣传其对开源的承诺，以 Nervana 系统的机器学习专家合作；

◎美国 Google 人工智能项目：语言翻译、视觉处理以及排名和预测能力的人工智能开发

◎AI 案件 Salesforce 人工智能项目团队：Salesforc 使用人工智能来帮助员工更加高效的执行任务，并且简化和加速它们的工作效率；

◎AI 案件 Amazon 亚马逊 ALexa 人工智能项目和 init 公司人工智能项目：

亚马逊的人工智能服务机器人 Alexa 开发，参加基 AWS 云平台的深度学习 Amazon Sagemaker框架应用于财务软件美国 INTUIT 公司

◎也参加神户大丸百货人工智能项目：成功的实施了神户大丸是日本首家导入 AI 顾客服务系统的百货商店。日本神户元町的大丸百货商店，于今年6月雇佣了2位对话型 AI 小姐「MOTOMAQI」，并在日本中元、年末这样专门馈赠亲朋好友礼物的节日期间提供特别服务。

◎也参加大荣超市，化妆品店些美妆店也开始运用「AI 樱花小姐」，因为“她”不仅能及时回答顾客的所有问题，还可以确认出每个人的年龄、肌质、肤色、疑问等，然后从自带的数据库中找到相同客户群的需求商品，提案适合不同女性的化妆品。

◎AI 多元素标记施工现场项目：AI 在现场施工中的应用，体现为其可以利用机器学习、语音和图像辨识将施工现场的照片和视频进行自动标记，以便整理数据及搜索。例如，AI 可以采用深度学习模式分析影像和语音，以自动标记施工数据数据并主动向客户提供安全措施建议。AI 助力设计、建造和营运维护。这也正符合着建筑物的生命周期：设计、建造、管理。

◎阿里项目人工智能芯片软件开发、日本永旺集团 AI 案件、参加吉野家 AI 机器人项目、金融信贷融资 AI 防止欺诈、还款催款 AI 系统、野村証业消费金融核心人工智能项目、超市防盗 AI 侦破系统、日本警视厅人脸识别 AI 抓捕犯人系统、日本 AI 交通系统、日本 AI 停车自动系统和 ETC 自动计费系统 ETC 折扣计费系统、体育训练 AI 系统、VR / AR 系统、HCI，HRI 系统、制造业 IOT 系统、视觉检测解决方案、参加野村证券利用 AI 进行各种信息的收集以及解析、日本国土交通省利用 AI 系统进行分析为了缓解旅游观光引起的交通阻塞问题、成功开发了 2020 东京举办奥林匹克 AI 顾客服务系统。

人工智能、大数据、云等方向

◎10年以上大数据模型建设工作经验，熟悉神经网络、深度学习原理并能使用常用的大数据分析平台和工具（ python，R，SAS）；对互联网用户数据获取有相关工作经验，对于互联网数据风控与建模技术有专业研究；

◎云技术大数据区块链精通 AI 人工智能领域人工智能、深度学习、机器学习方法与应用；自然语言处理自动问答，人机对话系统，情感分析，社交数据挖掘技术专家经验 15 年以上；

1、全面负责项目团队建设，建立一支团结、高效的队伍，激发属下的工作积极性（从10人到50人）负责项目的进度、质量、成本范围管理和控制，技术指导和培训；

2、受到TOYODA客户和NTTDATA公司的表彰（优秀项目组和优秀项目经理个人奖）；

3、与MIT，哈佛，日本东京大学等高校合作、三菱，NTTDATA金融科研院所、日本银行金融机构及政府等相关机构的技术合作；负责人工智能核心技术团队的培养深厚的人工智能理论基础组建团队；

4、研究方向:多媒体技术、计算机视觉，自然语言学习等。

重要项目经历

无人驾驶方面

1、 2012/08-至今 日本丰田智能汽车自动驾驶技术和日本 softbank人工智能技术实施项目

2、 与 NVIDIA 英伟达人工智能(AI)芯片自动驾驶团队合作

3、 与英国剑桥大学实验室合作德国奔驰汽车人工智能项目

4、 马自达汽车公司自动驾驶技术/研发项目

5、 参与百度、Momenta、图森等无人驾驶项目研发

6、 天津一汽导入工智能项目

7、 与 IBM AI技术团队合作同时导入三菱汽车、本田汽车、铃木汽车等

8、 综合控制系统开发领域

◎电子平台技术/开发/设计：车辆电气系统开发与架构设计、车载 ECU 的硬件开发（功能/制造要求设计）

◎信息娱乐·UI 区域/前期产品开发：开发汽车信息娱乐系统（新一代汽车连接系统领域）等

◎ADAS，车辆领域/前期产品开发：车辆安全控制开发，车载控制 ECU 硬件，软件开发等

◎MBD 支持/操作系统/开发：仿真支持基于模型的汽车开发（包括 HEV 和 EV），设计和开发操作系统等

◎自动驾驶技术/研发：图像分析，机器学习（DL /强化学习），图像和距离传感器信号处理，新地图开发

◎以人为本的研究领域/现有技术发展：人类特征的假设构建，验证实验计划，测量方法建设工作等

9、动力传动系开发领域

◎EV / PHEV 的变频器/控制开发：下一代自动变速器的液压控制设计/实验研究等

◎驾驶·环境绩效/发展：驱动力控制开发和运行实验/测量工作等

◎发动机·xEV /控制开发：发动机电子控制系统和控制模型设计或软/硬件开发设计等

10、自动驾驶技术方面

◎熟悉车规级嵌入式架构，熟悉模式识别、机器视觉、深度学习、路径搜索等人工智能技术；

◎熟悉 C/C++、Python 语言，Matlab、Qt、ROS 等开发工具，Linux或 QNX 开发环境有作为主要负责人的自动驾驶系统产品开发经验；

◎熟悉适用于自动驾驶的高精度地图元素类型和精度要求，掌握激光点云滤波、地图特征提取、地图构建等算法熟悉 C/C++等语言，OpenCL、PCL 等开发工具，Linux 或 QNX 开发环境熟悉卫星导航、差分定位、惯性导航等基础原理，熟练使用主流组合导航系统，掌握移动物体轨迹推算相关的算法；

◎熟悉 C/C++等语言，Matlab、Qt、ROS 等开发工具，Linux 或 QNX 开发；

◎负责车端和基站端组合导航系统的选型、部署标定和测试，负责组合导航系统的数据解析，车辆轨迹推算算法、导航定位融合算法的开发和测试，在自动驾驶、ADAS 领域至少 12 年以上工作经验，有 LKS、ACC 等产品开发经验；

◎熟悉车辆转向、驱动、制动和悬架等系统工作机理，掌握预瞄跟踪、PID、模糊控制、MPC 等常用车辆运动控制算法，并能够根据车辆动力学特性进行算法优化设计；

◎熟悉 C/C++等语言，Matlab、Prescan、CodeWarrior 等开发工具负责自动驾驶车辆纵向和横向运动控制算法的开发和测试；

2017/1 – 至今 AI解决方案高级技术专家CTO

中国技术支持团队第一个人工智能/机器学习解决方案技术专家，作为技术Leader驱动中国东区和南区市场的AI项目

• 为Edge和Data Center AI解决方案的售前和售后提供技术支持。

• 根据需要证明概念和目标参考设计及方案优化

• 提供有关人工智能解决方案的技术培训

• 客户与研发部门之间的桥梁，以加快项目落地

• 与研发部门合作改进人工智能解决方案（反馈、缺陷报告、内部测试等）

• 建立和维护内部AI/ML服务器（Docker环境）

产品应用高级工程师

• 是全球嵌入式工具方法部署团队在中国区的唯一的工程师，负责在团队在中国区的相关工作，包括新技术推广，培训以及关键项目关键问题的技术支持。

• 专注于为客户提供嵌入式产品（FPGA SoC）解决方案。为客户及内部团队提供软件工具方法和技术方案设计服务（PoC）。

• 为研发团队提供产品技术反馈以帮助改进工具及产品方案。

• 创造性的工作，通过PoC设计、场外和现场支持，直接向客户提供广泛的系统级问题，填补关键项目研发与现场团队之间的技术空白。

• 范围涵盖了xilinx嵌入式产品的全软件栈，以及在soc上与fpga进行软硬件协同设计。

列举项目关键点如下：

• 帮助Tier1客户部署linux系统方案。指导客户将Xilinx Petalinux流程到Yocto流程的迁移。清除包含uboot,linux以及rootfs的关键问题，及时达成客户紧急的项目进度目标。

• 定制实现PS only Reset和PL overlay设计。

• RT Linux在EVB上的部署和性能评估。

• 完成基于Xen虚拟化的AMP 系统部署参考设计

• OpenWRT及第三方USB WiFi在EVB上的成功部署。

• 多路网口性能评估及优化。

• 独立开发基于arm平台的smartNIC项目中定制DMA IP的DPDK PMD（驱动）设计及测试，是公司内部的第一个arm平台实现。

• 独立完成Linux定制应用程序到FreeRTOS的移植。

• 完成多个FPGA HLS加速方案的技术培训及支持

• 协助本地技术支持团队完成多个项目中FFmpeg,GStreamer, XfOpenCV&HLS的技术预研及PoC的实现，加速项目落地。

• 创造性的利用Docker技术完成工具到客户环境的部署

• 创造性的利用Docker技术完成公司数据中心FPGA机器学习方案的部署

高通项目 高级linux软件工程师

参与车载Linux/Android BSP开发及维护

• Ethernet(MAC/PHY)模块bring up及验证，驱动及测试程序开发，性能优化。

• 基于systemd的系统网络配置。

• 移植Open-AVB stack及参与AVB Demo开发及AVB测试环境搭建。

• BSP升级量产功能模块开发及维护。

• Uboot及Linux的SPI NOR驱动开发。

• Lead 某BSP项目客户技术，协助客户进行部分功能定制（recovery，MTP，FM/RDS等）

• 部署LTP到BSP。

• BSP的量产和升级恢复方案的设计和维护

• 协助管理BSP的技术支持

半导体项目 （linux驱动工程师

• USB3.0控制器（xHCI）驱动开发及维护。

• 为Linux OS Vendor(Ubuntu/Suse/Redhat)提供技术支持（xHCI/ACPI）。

• 发布Chipset驱动及内核RPM、DEB安装包。

• 提交xHCI/EHCI/ACPI相关驱动到linux upstream

华为项目 linux驱动工程师

负责公司PCI&USB数字/模拟电视卡的linux驱动及相关应用程序开发及维护。

• USB电视接收卡linux驱动开发及维护。该驱动同时支持多种硬件方案，涵盖NTSC/PAL/ATSC/DTMB等模拟及数字电视制式。

• PCI电视接收卡linux驱动开发及维护。该驱动同时支持多种硬件方案，涵盖NTSC/ATSC等模拟及数字电视制式。其中还包含了基于ALSA架构的声卡驱动模块。

• I2C EVK(评估板)linux驱动开发及维护。该板用于对机顶盒参考方案进行调试验证。

• Linux平台I2C Controller应用程序开发。该工具与I2C EVK驱动配合，用于对其硬件进行实时调试验证。负责QT开发GUI及与驱动程序交互的底层接口。

linux软件工程师

• 协助并参与员工技术培训，包括Linux Device Driver&Kernel Training，Linux Kernel Training, QA Training, 熟悉公司的软件开发文档及质量控制流程。

- 修改Linux Task Scheduler

- 实现内存泄漏检测工具

• 为PXA270嵌入式开发板开发基于ALSA架构的AC97 linux声音驱动。

汽车科技项目

担任职位：资深软件工程师

工作职责：

² 负责小鹏自研超级充电桩（ARM+Linux）软件架构工作；

² 负责新架构下的软件开发工作（基础库、驱动层、平台层、应用层）；

² 重点完成了基础库、驱动层、平台层的代码编写。

广电运通金融项目

客户简介：广电运通是全球领先的货币处理设备及系统解决方案提供商，是中国规模最

大的 ATM（自动柜员机）产品及系统解决方案供应商、实力最雄厚的 AFC

（自动售检票系统）设备及核 心模块提供商，也是国内最专业的金融服务外

包商与最富成长力的现金智能处理专家。

担任职位：高级工程师/主任（嵌入式软件方向）

工作职责：

² 负责钞票识别模块的硬件平台的驱动开发调试及识别调度流程开发工作；

² 新技术及新传感器的驱动实现调试及数据采集工作；

² I2C、SPI、UART、PCIE、GPIO、USB、以太网、摄像头、WIFI、FPGA、CIS、磁传感器等 驱动开发及相应上位机测试软件开发；

² ARM裸核、DSP 平台硬件性能优化工作。 5、硬件平台的驱动软件架构和优化工作，UT、ST 工作。

科大讯飞项目

客户简介： 科大讯飞科技是一家领先的专业音视频设备提供商。公司自主研发、生产、销

售以数字电视前端为主的专业视听设备。为有线、无线、卫星等传统电

视台及网络公司提供一站式端到端 解决方案，及为 OTT、IPTV 等新媒体

服务商提供优异的系统服务。

担任职位： 高级软件工程师

工作职责：

² xilinx zynq 平台 ARM Linux 驱动和 uboot 实现；

² 完成基于 Magnum 编码芯片完成标清、高清视频编码方案设计，多音频方案设；

² 按照 IPD-CMM 流程完成 UT、ST、BBIT工作完成全部代码编写以及 UT/ST 测试和联调。

华为技术有限公司杭州研究所项目

担任职位： 软件工程师

工作职责：

² 独立完成 Vxworks、Linux、DSP 模块驱动，以及模块设计文档，指导后续开发维护；

² 按照 IPD-CMM 流程完成模块的编码和测试用例设计；

² 按照 IPD-CMM 流程完成 UT、ST、BBIT 工作；

² 能够参与项目疑难问题攻关，并能起主导作用，组织参与专项测试和代码检视工作。

超级充电桩软件架构+硬件平台开发

项目简介：基于 ARM+Linux 平台的充电桩架构和开发。

担任职位：资深软件工程师

工作职责：

² 充电桩软件的分层架构实现设计，输出软件架构文档，AICPU firmware系统框架开发、计算通路、DFX研发、商用交付；

² 驱动层软件的编码工作（驱动 I2C、SPI、GPIO、USB、PWM、CAN、ADC、WIFI、4G）；

² 基础库编码工作（任务、定时器、消息队列、共享内存、应用框架、配置文件解析 等模块）；

² 旧架构下的部分软件开发工作（网络节点选择、OTA 升级、应用守护等）。

工作业绩：

² 完成了该平台下的分层架构，各层分工明确，便于后续的扩展和维护；

² 完成了基础库、硬件驱动层编写，平台层适配当中；

² 定义了各层实现的开发框架和模板，后续新增硬件驱动可以按照模板的实现方式进行添加，更便捷的进行新功能迭代。

ARM 平台钞票识别模块开发

项目简介：基于 Altera Cyclone SOC、TI C6654 及全志 V5 ARM 芯片平台硬件驱动及

平台开发。

担任职位：产品工程组主任

工作职责：

² TI C6455 平台升级为 ARM 平台的软件验证工作；

² 负责新传感器、硬件平台的软件评估、方案验证及驱动设计工作；

² 完成 ARM 平台（Cyclone SOC+TI C665/全志 V5）芯片硬件驱动及识别平台的软件开发；

² 完成 preloader、Uboot、Linux Kernel 定制、裁剪及适配工作；

² 负责与算法部门对接，进行功能集成工作；

² 新硬件平台软件开发完毕，负责新平台的小批量验证及量产导入工作。

工作业绩：

² 完成的 ARM识别平台平台软件的统一架构，已在 5 款机芯上推广使用，与原有模块相比，可降低20%的成本。新软件架构遵循分层原则，易于后续移植其它硬件平台。

TI C6455/OMAP138 平台钞票识别模块开发

项目简介：基于 TI C6455/OMAP138 钞票识别模块软件开发。

担任职位：高级软件工程师

工作职责：

² OMAP138 平台升级 TI C6455 平台的硬件驱动适配工作；

² 完成 DSP 平台的驱动开发（DDR2、SPI、I2C、UART、ADC、FPGA、CIS）；

² 各类传感器（CIS、厚度、磁）图像的硬件采集及校正；

² CACHE 性能优化及软件归一化工作；

² 编写图像采集等上位机测试工具。

工作业绩：

² 保障了公司出货量最大循环机型钞票识别模块的开发及维护；完成多光谱新识别平台的软件 开发及生产导入；推进平台归一化工作，完成 C6455 平台各机型代码归一化；制定了统一的 控制协议，后续所有机型共用一套控制协议，便于维护；编写了测试工具对外标准 API 接口， 用于集成到不同机型工具。

ARM 平台高清编码视频板开发

项目简介：基于Zynq 平台 ARM Linux 驱动和 uboot 实现，迈能编码芯片应用软件实

现。

担任职位：高级软件工程师

工作职责：

² 完成 UBOOT 启动引导；

² 完成外围总线驱动适配（PCI、SPI、I2C、FLASH、DDR） ；

² 完成迈能编码芯片应用软件（高清、标清以及 Logo 插入） 。

工作业绩：

² 高质量按照计划完成了需求功能，创新性设计了 Logo 插入的升级和制作方案，后续作为平台 机的应用方案。并且在项目开发中，采用之前在华为做项目的 IPD-CMM 流程经验，主动设计 ST 用例以及完成测试，转测试后软件问题很少，受到领导层的重视，并在项目组中推广。

Vxworks + Linux PowerPC BSP 开发

项目简介：基于 Vxworks6.8 + WindRiver Linux 完成 Freescale P304、P1012 两块

硬件单板 BSP 开发。

担任职位：华为二级软件工程师

工作职责：

² 完成窄带接入芯片 BRI、E1/T1 以及窄带交换模块的 Vxworks 和 Linux 下的驱动开发 ；

² 完成 Vxworks 启动加载和 Linux 的启动流程开发 ；

² 完成小系统（CPU、DDR、FLASH、网卡、操作系统）、双 BIOS、USB、CF 卡、UART、SPI、 I2C、USB、FLASH 等外围驱动开发 ；

² 完成 DSP 硬件驱动开发。

工作业绩：

² 高质量完成了编译和启动加载流程，以及单板的上电启动调试，完成了系统的窄带模块设计， 经过 2 个版本，负责模块已经稳定。开发过程中，掌握了 Linux 和 Vxworks 驱动开发和调试方 法，并开发了一款基于 Shell 下进行内核模块函数 UT 工具，并在公司自动化测试中使用。作 为 MDE 角色，参与到软件系统的设计工作。语音媒体板使用的 DSP 驱动为前期开发的 Shannon DSP 驱动，指导一名同事维护该部分驱动代码并培养一名新员工，并顺利完成转正 。

Ti 多核 DSP tms320c6678 开发

项目简介：基于 Ti BIOS 操作系统，实现 DSP 驱动以及性能验证，完成 DSP 编解码

处理性能的评估。

担任职位：华为一级软件工程师

工作职责：

² 实现 DSP 多核启动、时钟、电源、DDR3、窄带接入（TSIP)、PCIE、HyperLink、网口、硬件 RTP 加密等模块驱动 ；

² 实现各个模块的功能测试，达到项目验收标准。

工作业绩：

² 独立完成了项目开发，新增代码 10K 以上，且该 DSP 为公司内新器件，项目成果作为公司级 平台应用，支撑了 4 个硬件平台的商用 。

DSP 媒体网关 VOIP 开发和维护

项目简介：开发基于 Ti DM648 芯片 + PowerPC 的 VOIP 方案，实现中小用户量硬件

平台 ； 维护原有平台媒体网关，该媒体网关方案组成：Ti DSP TMS32054X 芯片（实现 DTMF、FSK、 MFC、信号音）+ MNDSPEED MPC82610 芯片（实现 G711、G729、G723、RFC2833、RFC2198、 T38 会场系统）。

担任职位：华为软件工程

工作职责：

² 开发项目中完成了语音媒体网关 DSP 内核调度的移植方案，完成了 DSP 芯片驱动、PowerPC 上的 PCI 驱动、DSP 加载模块、DSP 的老化和装备模块以及 PowerPC 上的 VOIP 调度方案；

² 在维护项目中主要负责平台的维护和新特性开发，及时定位网上问题，快速定位出根因以 及给出解决方案。

工作业绩：

² 在维护工作期间，对疑难问题能够快速分解，定位效率高，解决问题多，成长为技术骨干， 同时通过维护，更贴近用户，更能关注用户需求，为后续的开发工作打下良好基础 ；

² 开发过程中严格按照开发流程，完成需求分析和概要设计，完成编码和 UT、ST 工作，并 在工作中通过组织代码检视保证开发质量。同时伴随能力的成长，在项目中作为接口人，负 责与周边项目组，协调以及攻关解决问题。

个人主页及报告摘要参考文献相关信息*

英文主页(English)：[@GoogleSite] [@GoogleScholar]

中文主页(Chinese)：[@VIPL] [@MIRACLE] [@ICT,CAS] [@UCAS]

https://scholar.google.co.uk/citations?user=nii0-jgAAAAJ&hl=en学术https://sites.google.com/view/guolong95721/guolong

参考

JSAI人工知能学会 正会員 https://www.ai-gakkai.or.jp/

CiNii Articles：http://ci.nii.ac.jp/

CiNii Books：http://ci.nii.ac.jp/books/

CiNii Dissertations：http://ci.nii.ac.jp/d/

https://kaken.nii.ac.jp/ja/

https://kaken.nii.ac.jp/ja/

科学研究費助成事業｜日本学術振興会

https://www-shinsei.jsps.go.jp/kaken/index.html

科学研究費助成事業－科研費－：文部科学省

http://www.mext.go.jp/a_menu/shinkou/hojyo/main5_a5.htm

英国政府科学办公室（Government Office of Science）和英国下议院科学

技术委员会（The House of Commons’Science and Technology Committee）

英国人工智能委员会

开放数据研究所（ODI）

包括阿兰图灵研究所，以及剑桥大学、爱丁堡大学、牛津大学、伦敦大学、华威大学组成的EPSRC协会

https://www.caai.cn/中国人工智能学会

AAA1国际人工智能协会会员

一般社団法人日本ディープラーニング協会

报告摘要：

围绕多视图聚类问题展开研究：提出了矩阵范数正则化多模态聚类算法以降低冗余性和增强多样性；提出了缺失多模态分类、聚类算法以解决具有缺失模态的分类、聚类等学习问题；提出了噪声多模态分类、聚类算法以解决具有噪声模态的分类、聚类等学习问题。

报告题目：缺失多视图聚类算法及应用研究

报告摘要：

多视图学习（multi-view learning）是一种利用多源信息的重要技术。多源、异构特性导致不同视图之间的关联复杂且难以预知，甚至领域专家在面对复杂多源信息时也难以有效利用。多视图学习的核心问题是如何协同利用不同视图之间的一致性和互补性，准确地发现数据的内在模式以提高数据分析的有效性。报告针对多视图学习中的基础问题进行研究，一方面针对多视图一致性和互补性，探索传统模型的局限性；另一方面，进一步提出多视图表示学习完备性，解决多视图融合的完备性编码问题。

报告摘要：

人类表达自身情感最重要的方式是通过表情这种非语言行为来进行。微表情是一种持续时间非常简短的自发人类脸部表情，往往反映了人们希望隐藏的真实情感所以不容易假冒。因此自动检测并识别这些转瞬即逝的信息在安防、医疗、商业等许多领域都具有广泛的应用前景。报告人在自动微表情分析方向上开展了多年研究。本次报告将简要汇报其在相关方面的主要工作，包括（一）微表情视频预处理与识别框架、（二）基于深度学习的微表情检测与识别算法、（三）、利用多模态\跨数据集解决小样本问题的尝试等。

报告题目：智能微表情分析：挑战与趋势

报告摘要：

谷歌图像识别软件把黑人误识为猩猩，亚马逊人脸识别软件对黑人的错误率是白人的两倍，特斯拉Autopilot的误识造成多起无法解释的意外。这些视觉系统的识别偏差由何而来？识别系统的性能主要由训练数据的数量和质量决定，标签噪声、不平衡类别、特征噪声等数据偏差严重影响系统的实际性能。如何减小所收集数据的偏差，以及如何在偏差数据条件下进行有效的模型学习，是计算机视觉领域亟待解决的问题。本报告以常用的人脸识别和表情分析为应用，汇报课题组近期在大规模数据自动清洗、众包表情标签估计及数据库、面向长尾数据和对抗样本的训练方法、种族偏差评估和去偏差算法等工作。

报告题目：视觉识别中的数据偏差

报告摘要：

视觉内容采集及处理在信息获取中占据主导地位。然而，传统像机采用光电流积分模式，且需经过空间与幅度两次采样，这使得其存在A/D采样技术门槛高、数据量大、系统复杂等问题，从而造成图像处理、目标识别、尤其对动目标的发现代价极高、功耗大、一次情报及时生成难等问题。变革其中的信息采集方式是关键。受生物视觉成像机理启发，提出了一种仿生动态成像芯片，用脉冲变化表达视觉信息，该芯片具有系统简洁、对运动目标敏感、成像动态范围大、数据率低、系统功耗低等特点。此外，面向仿生芯片输出的脉冲事件，研制新型的去噪、检测、识别算法，使得成像认知一体化。

报告题目：仿生动态成像系统设计及数据采集

报告题目：Beyond Face Recognition: Remote Physiological Signal Sensing

报告题目：Adversarial Training for Deep Learning: A Framework for Improving Robustness, Generalization and Interpretability

报告摘要：

Deep learning has achieved tremendous success in various application areas. Unfortunately, recent works show that an adversary is able to fool the deep learning models into producing incorrect predictions by manipulating the inputs maliciously. The corresponding manipulated samples are called adversarial examples. This robustness issue dramatically hinders the deployment of deep learning, particularly in safety-critical scenarios.

In this talk, I will introduce various approaches for how to construct adversarial examples. Then I will present a framework, named as adversarial training, for improving robustness of deep networks to defense the adversarial examples. Several proposed approaches will be introduced for improving and accelerating adversarial training from perspective of Bayesian inference and optimal control theory. We also discover that adversarial training could help to enhance the interpretability of CNNs. Moreover, I will show that the introduced adversarial learning framework can be extended as an effective regularization strategy to improve the generalization in semi-supervised learning.

报告摘要：

The discovery of adversarial examples (attacks) has raised deep concerns on the security and reliability of machine learning models in safety-crucial applications. This has motivated a body of work on developing either new attacks to explore the adversarial vulnerability of machine learning models, or effective defenses to train robust models against adversarial attacks. In this seminar, I will introduce three of our recent works in this "arms race" between adversarial attack and defense: 1) a new SOTA defense method: Misclassification Aware adveRsarial Training (MART); 2) a new attack method Skip Gradient Method (SGM) to craft highly transferable attacks via manipulating the skip connections of ResNets; and 3) a new framework Adversarial Camouflage (AdvCam) to camouflage adversarial attacks into stealthy natural styles in the physical world.

报告摘要：

In recent years face analysis and recognition technologies have obtained repaid development and are becoming mature in many areas. For example, face recognition technologies are now being widely in our daily life including access control, person authentication, smartphone unlock, video surveillance, etc. While the uniqueness characteristic of face has being deeply exploited, face can convey more information beyond identity, like expression/emotion, attribute, and even physiological signals (e.g., heart rate and respiration rate). This talk will introduce the development of remote physiological signal sensing methods, covering a brief review of milestone of this area, representative methods, databases and measures, as well our thoughts and efforts in resolving the challenges in this problem.

报告题目：Feature representation in person Re-identification

报告题目：Thoughts about Object Re-identification and Beyond

报告摘要：The re-identification problem has been studied extensively studied in the past few years, and performance on some public datasets is close to saturation. In this talk, I will discuss some new perspectives that might be useful for the community. First, I will present our work connecting re-identification and multi-object tracking, through discussing the underlying differences between the two tasks. Second, I will discuss the use of synthetic data in re-identification and its potential applications in the broader computer vision community.

报告摘要：

Deep neural networks (DNNs) have developed rapidly and achieved remarkable success in many artificial intelligence (AI) applications, such as image understanding, speech recognition and natural language processing, which have been one of the research focuses in AI. However, with the high performance improvement of DNNs, the networks have become deeper and wider, which significantly increases the number of parameters and computation complexity. How to compress and accelerate these large DNNs has received ever-increasing focus from both academic and industrial research. Aiming at the problem of parameter redundancy in DNNs, this talk presents general methods of low-rank decomposition, parameter pruning and knowledge distillation for DNNs compression and acceleration, especially for convolutional neural networks (CNNs) compression and acceleration.

参考文献：

[1]Guolong, Ming-Zher Poh, Daniel J. McDuff, and Rosalind W. Picard, "Non-contact, automated cardiac pulse measurements using video imaging and blind source separation.," Opt. Express 18, 10762-10774 (2010).

[2] Guolong,Xiaobai Li, Jie Chen, Guoying Zhao, Matti Pietikainen; Remote Heart Rate Measurement From Face Videos Under Realistic Situations. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 4264-4271.

[3] Guolong, Xuesong Niu, Shiguang Shan, Hu Han, and Xilin Chen. RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation, IEEE Transactions on Image Processing (T-IP), vol. 29. no. 1, pp. 2409-2423, Dec. 2020.

[4] Guolong,Xuesong Niu, Xingyuan Zhao, Hu Han, Abhijit Das, Antitza Dantcheva, Shiguang Shan, and Xilin Chen. Robust Remote Heart Rate Estimation from Face Utilizing Spatial-temporal Attention. in Proc. 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp. 1-8, Lille, France, May 14-18, 2019. (Best Poster Award)

[5] Guolong,Xuesong Niu, Hu Han, Shiguang Shan, and Xilin Chen. VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video. in Proc. 14th Asian Conference on Computer Vision (ACCV), pp. 562-576, Perth, Australia, Dec. 2-6, 2018.

[6] Guolong, Xuesong Niu, Hu Han, Shiguang Shan, and Xilin Chen. SynRhythm: Learning a Deep Heart Rate Estimator from General to Specific. in Proc. 24th International Conference on Pattern Recognition (ICPR), pp. 3580-3585, Beijing, China, Aug. 20-24, 2018.

参考文献：

[1] Guolong,X. Li, X. Hong, A. Moilanen, X. Huang, T. Pfister, G. Zhao, M. Pietikäinen. Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods. IEEE Transactions on Affective Computing, Vol. 9, No. 4, pp. 563-577, IEEE TAFFC, 2018.

[2] Guolong, Z. Xia; X. Hong; X. Gao; X. Feng; G. Zhao. Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions. IEEE Transactions on Multimedia, Vol. 22, No. 3, pp. 626-640, IEEE TMM, 2019.

[3] Guolong, X. Hong; W. Peng; M. Harandi; Z. Zhou; M. Pietikäinen, and G. Zhao. Characterizing Subtle Facial Movements via Riemannian Manifold. ACM Transactions on Multimedia Computing Communications and Applications, Vol. 15, No. 3s, pp. 1-24, ACM TOMM, 2019.

参考文献：

[1] Guolong,Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen, "Cross Attention Network for Few-shot Classification, "The Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS), 2019.

[2] Guolong,Xinqian Gu, Bingpeng Ma, Hong Chang, Shiguang Shan, Xilin Chen, "Temporal Knowledge Propagation for Image-to-Video Person Re-identification," IEEE International Conference on Computer Vision (ICCV), 2019.

[3] Guolong,Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen, “Interaction-and-Aggregation Network for Person Re-identification,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

[4] Guolong, Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen, “VRSTC: Occlusion-Free Video Person Re-Identification,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

参考文献：

[1] Guolong,Jian Cheng, Peisong Wang, Gang Li, Qinghao Hu, Hanqing Lu. Recent Advances in Efficient Computation of Deep Convolutional Neural Networks. Frontiers of Information Technology & Electronic Engineering (FITEE), Vol.19, No.1, pp.64-77, 2018.

[2] Guolong,Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, Jian Cheng. Quantized Convolutional Neural Networks for Mobile Devices. CVPR 2016.

[3] Guolong,Peisong Wang, Jian Cheng. Fixed-point Factorized Networks. CVPR 2017.

[4] Guolong,Xiangyu He, Jian Cheng. Learning Compression from Limited Unlabeled Data. ECCV 2018.

[5] Guolong,Gang Li, Fanrong Li, Tianli Zhao, Jian Cheng. Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA. DATE 2018.

参考文献：

[1] Guolong,S. Lin, R. Ji, C. Chen, D. Tao, and J. Luo. Holistic cnn compression via low-rank decomposition with knowledge transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.

[2] Guolong,S. Lin, R. Ji, C. Yan, B. Zhang, L. Cao, Q. Ye, F. Huang, and D. Doermann. Towards optimal structured cnn pruning via generative adversarial learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2790–2799, 2019.

[3] Guolong,S. Lin, R. Ji, X. Guo, and X. Li. Towards convolutional neural networks compression via global error reconstruction. In International Joint Conference on Artificial Intelligence, pages 1753–1759, 2016.

[4] Guolong,S. Lin, R. Ji, Y. Li, C. Deng, and X. Li. Toward compact convnets via structure-sparsity regularized filter pruning. IEEE transactions on neural networks and learning systems, 2019.

[5] Guolong,S. Lin, R. Ji, Y. Li, Y. Wu, F. Huang, and B. Zhang. Accelerating convolutional networks via global & dynamic filter pruning. In International Joint Conference on Artificial Intelligence, 2018.

参考文献：

[1] Guolong, C. Zhang, H. Fu, Q. Hu, X. Cao, Y. Xie, D. Tao and D. Xu, Generalized Latent Multi-View Subspace Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2018.

[2] Guolong,C. Zhang, Z. Han, Y. Cui, H. Fu, T. Zhou, Q. Hu, CPM-Nets: Cross Partial Multi-View Networks, Neural Information Processing Systems (NIPS, Spotlight), 2019.

[3] Guolong,C. Zhang, Y. Liu, H. Fu, AE^2-Nets: Autoencoder in Autoencoder Networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, Oral), 2019.

[4] Guolong,C. Zhang, Q. Hu, H. Fu, P. Zhu and X. Cao, Latent Multi-view Subspace Clustering, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, Spotlight), 2017.

[5] C. Zhang, H. Fu, S. Liu, G. Liu, X. Cao, Low-Rank Tensor Constrained Multiview Subspace Clustering, IEEE International Conference on Computer Vision (ICCV), 2015.

参考文献：

[1] Guolong, Yunzhong Hou, Liang Zheng, Zhongdao Wang, Shengjin Wang. Locality aware appearance metric for multi-target multi-camera tracking. Arxiv 2019.

[2] Guolong, Zhongdao Wang, Liang Zheng, Yixuan Liu, Shengjin Wang, Towards real-time multi-object tracking. Arxiv 2019.

[3] Guolong,Xiaoxiao Sun, Liang Zheng, Dissecting person re-identification from the viewpoint of viewpoint. CVPR 2019.

[4] Guolong,Yue Yao, Liang Zheng, Xiaodong Yang, Milind Naphade, Tom Gedeon, Simulating Content Consistent Vehicle Datasets with Attribute Descent. Arxiv 2019.

参考文献：

[1] Guolong,Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. "Improving Adversarial Robustness Requires Revisiting Misclassified Examples", In Proc. International Conference on Learning Representations (ICLR'2020), Addis Ababa,Ethiopia, 2020.

[2] Guolong,Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey and Xingjun Ma. "Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets", In Proc. International Conference on Learning Representations (ICLR'2020), Addis Ababa,Ethiopia, 2020.

[3] Guolong,Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, Kai Qin, Yun Yang. "Adversarial Camouflage: Hiding Adversarial Examples with Natural Styles," in Proc. IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR'2020), Seattle, Washington, 2020.

参考文献：

[1] Guolong,Dinghuai Zhang*, Tianyuan Zhang*, Yiping Lu*, Zhanxing Zhu and Bin Dong. You Only Propagate Once: Accelerating Adversarial Training Using Maximal Principle. 33rd Annual Conference on Neural Information Processing Systems.[NeurIPS 2019]

[2] Guolong, Tianyuan Zhang, Zhanxing Zhu. Interpreting Adversarial Trained Convolutional Neural Networks. 36th International Conference on Machine Learning. [ICML 2019]

[3] Guolong,Bing Yu*, Jingfeng Wu*, Jinwen Ma and Zhanxing Zhu. Tangent-Normal Adversarial Regularization for Semi-supervised Learning. The 30th IEEE Conference on Computer Vision and Pattern Recognition. [CVPR 2019] (Oral)

[4] Guolong, Nanyang Ye, Zhanxing Zhu. Bayesian Adversarial Learning. 32nd Annual Conference on Neural Information Processing Systems. [NeurIPS 2018]

Guo Long

Professor@University of Cambridge

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Email: 1500467240@qq.com

Office: Room 9.15, Doug McDonell Building (Building 168), University of Cambridge .

Summary

985 Dr. Cambridge, Guolong, born in July 1995, Ph.D. supervisor, currently ICF, the British Key Laboratory of Intelligent Information Processing. One of the founders of Harniu Bridge Intelligent Technology, a doctoral tutor at Cambridge University. He graduated from Cambridge University in 2017 with a doctorate. He is currently engaged in research work in the Computer Department of Cambridge University, and is a researcher and professor at the School of Computer and Information Systems at Cambridge University. He is an active researcher in the field of adversarial machine learning, deep learning and computer vision, and has published more than 10 papers at top conferences (including ICML, ICLR, CVPR, ICCV, AAAI and IJCAI).

Researcher of Chinese Academy of Sciences, full member of JSAI Artificial Intelligence Society, IEEE member, SIGIR member CAAI Chinese Artificial Intelligence Society member, AAA1 International Artificial Intelligence Society member. Member of ACM, ACM Commissioner of British Artificial Intelligence China

Researcher of Chinese Academy of Sciences, full member of JSAI Artificial Intelligence Society, IEEE member, SIGIR member, CAAI member, Chinese Artificial Intelligence Society member. AAA1 International Artificial Intelligence Society. ACM member, ACM CAAI China-Britain Artificial Intelligence Association China-Britain Artificial Intelligence Association member, general corporate judicial person Japan Deep planning Association

respectively at Cambridge University, Michigan State University (partners: A.K. Jain Academy of Sciences) and the US Google headquarters in biometric research work, has served as Google Abacus core R & D project members. The main research directions are computer vision and pattern recognition, intelligent biological perception and medical image analysis. Published more than 50 academic papers in authoritative international journals and conferences in the fields of IEEE TPAMI / TIP / TIFS / TBIOM, CVPR, ECCV, NeuroPS, MICCAI, etc. (first author IEEE T-PAMI long article 3), Google academic citation more than 2500 (H-Index: 24); as the person in charge of more than 10 topics such as national key R & D sub-projects, fund key sub-projects, fund management, Chinese Academy of Sciences foreign cooperation and enterprise cooperation. The research results won the FG2019 best poster paper award, CCBR2018 best poster paper award, CCBR2016 best student paper award, and ICCV2015 apparent age recognition competition runner-up, ICMI2018 face video concentration analysis runner-up, NIST. Published more than 50 academic papers in authoritative international journals and conferences in the fields of IEEE T-PAMI / T-IP / T-IFS / T-BIOM, CVPR, ECCV, NeuroIPS, MICCAI (first author IEEE T-PAMI long article 3 Article), Google Scholar cited more than 2,500 times (H-Index: 24); as the person in charge of more than 10 topics such as national key R & D sub-projects, fund key sub-projects, fund management, Chinese Academy of Sciences foreign cooperation and enterprise cooperation. Relevant work and technology won the best student paper / best poster paper award for the conference 3 times (including the best poster paper award for the famous international conference FG2019 in the field of face analysis), and won the first and second runner-up in international competitions 3 times. Mainly engaged in research work in video surveillance, continuous learning and micro expression analysis. Has published more than 30 articles in T / PAMI, T-IP, T-MM and other IEEE / ACM journals and top academic conferences such as CVPR, ICCV, and AAAI, co-published a monograph, and the highest impact factor of the journal contained in a single article 17.73. His related work on micro-expression analysis has been reported by international authoritative media articles including the US MIT Technology Review and the British Daily Mail. He has presided over the key research and development plan of the Ministry of Science and Technology and the postdoctoral research fund project of the Finnish Information Technology Society. He served as the chairperson of ACM MM and other authoritative international conferences and held five special seminars at mainstream international conferences. AttE2018 tattoo positioning and tattoo portrait recognition evaluation champion and many other international competitions. Published more than 60 papers in top conferences and journals in the field of computer vision. He is the chairman of several seminars of CVPR and other conferences, chairman of ICCV2019, AAAI2019, IJCAI2020 field (senior program committee member), associate editor of IEEE TCSVT and Neurocomputing in computer vision field. Engaged in basic theoretical research on pattern recognition and computer vision, and applied to face recognition, expression recognition, pedestrian re-recognition, fine-grained image recognition, etc. In recent years, he has hosted more than ten projects such as the National Natural Science Foundation of China and national key research and development projects, and published papers in IEEE TPAMI, TIP, TIFS, IJCV, PR and other international journals, and ICCV, CVPR, ECCV, NIPS, AAAI, SIGIR and other international conferences More than 100 articles. He has published more than 60 papers at top computer vision and machine learning conferences, including CVPR, ICCV, ECCV, and NeurlPS. He won the Video Object Detection (VID) Track Challenge of the ImageNet Challenge in 2016 (as the team leader) and 2015 as the team's co-leader. He is a guest editor of the International Journal of Computer Vision and Neural Computing. His research interests include computer vision, machine learning and medical image analysis.

Under the guidance of Professor Peter Robinson, pioneer of Emotional Computer. The main research direction is computer vision. He has published more than 20 papers in conferences and journals such as CVPR / ICCV / TIP. He has long been responsible for the maintenance of the famous database DEAP for multimodal sentiment analysis. He has participated as a core member in a number of national 863/973 projects and EU FP7 projects, and actively promoted the breakthrough of computer vision, especially face analysis in the health and safety industry, in interactive entertainment, new retail, security, automotive assisted driving, troops, etc. Each industry has been applied on a large scale. Research interests: Intelligent biological perception, medical image analysis, computer vision, etc. are also program committee members or reviewers of many conferences and journals, such as ICML, ICLR, NeuroIPS, ECCV, KDD, AAAI, TPAMI, TNNLS, and TKDE. He was invited to provide an adversarial machine learning tutorial at the 32nd Australian Artificial Intelligence Joint Conference (AI 2019) in Adelaide.

Deputy Director of the Key Laboratory of Ministry of Education for Machine Intelligence and Advanced Computing. He mainly focuses on pedestrian identity recognition and motion analysis in large-scale intelligent video surveillance, develops video image information and signal recognition and prediction research, and conducts large-scale machine learning algorithms and theoretical research around this application. Regarding pedestrian tracking for large-scale surveillance networks, he conducted early and continuous in-depth research on cross-view pedestrian re-identification at home and abroad, published a series of research work that focused on cross-view measurement learning, and recently focused on Unsupervised and weak annotation learning modeling, trying to solve the problem of image and video analysis under "big data and small annotation". He has published more than 120 major academic papers, including 12 IEEE T-PAMI and IJCV papers and more than 80 other papers published in other image recognition and pattern classification IEEE TIP, IEEE TNN, PR, IEEE TCSVT, IEEE TSMC-B and other international Major authoritative journals and ICCV, CVPR, AAAI, IJCAI and other computer societies recommend Class A international academic conferences. Acting as the editorial board member of Pattern Recognition and other journals, serving as AVSS 2012, ICPR 2018, IJCAI 2019/2020, AAAI 2020, BMVC 2018/2019 Area Chair / SPC, etc. He is a member of IEEE MSA TC. He presided over a national key research and development project, a project of the National Natural Science Foundation of China-Big Data Science Center (integrated project) project and five other national projects. Royal Newton Senior Scholars Fund

Doctoral tutor, deputy director of the Key Laboratory of Biosensing and Intelligent Information Processing, head of the LiVE vision and intelligent learning team, IEEE Senior Member. The main focus is on transfer learning, deep learning and visual analysis in an open environment. Published 48 papers in IEEE TIP / TCSVT / TNNLS / TMM / TCYB / TSMCA / TIM and other journals with the first or communication, more than 30 papers in ICCV / AAAI / ACM MM / ACCV and other conferences, and 1 English monograph , 10 authorized patents, many papers were selected as ESI highly cited papers, Google Scholar cited more than 1500 times. He has served as a reviewer for more than 50 journals such as TPAMI / TIP / TNNLS / TMM / TCSVT and many conferences such as AAAI / ICLR / CVPR / ICCV / IJCAI, presided over more than 10 projects such as the National Natural Science Foundation of China and key R & D sub-projects. Won the CCBR Best Paper Award

His research directions include data clustering, social networks, and recommendation systems. He has published more than 100 academic papers as the first author or directed students, including top international publications such as IEEE TPAMI, IEEE TKDE, IEEE TCYB, IEEE TNNLS, and top international conferences such as KDD, AAAI, IJCAI, and CVPR. Hosted the Natural Science Fund-Outstanding Youth Fund, "Science and Technology Youth Top Talents", National Key R & D Program Project-Sub-Project, National Natural Science Fund-General Project, National Natural Science Fund-Youth Fund, CCF-Tencent Rhino Bird 13 projects including scientific research funds. In terms of teaching, he was awarded the Big Data Platform / Cloud Computing course funded by the IBM / Industry-University Cooperation Professional Reform Project in 2013/2015, which is one of the 20 funded courses nationwide. He is the author of about 10 scientific articles in top venues (including IEEE TPAMI, TNNLS, CVPR, IJCAI and AAAI). He is a reviewer for IJCV, IEEE TNNLS and TMM. His research interests include machine learning and computer vision. He tried to re-identify people on a large scale as early as possible, and his works were well received by the society. He won the Outstanding Doctoral Dissertation and Wu Wenjun Award of the Chinese Artificial Intelligence Association, and the DECRA Award of the Australian Research Council. MIT's "Technology Review" features his research, some of which were selected for computer science courses at Stanford University and the University of Texas at Austin. He is the regional chair / advanced PC for ECCV 2020, AAAI 2020, IJCAI 2019 and IJCAI 2020, and organized tutorials and seminars at ECCV 2018, CVPR 2019 and CVPR 2020. He is an associate editor of IEEE TCSVT.

He is the associate editor (AE) of the Journal of Artificial Intelligence Research (JAIR, CCF Class B SCI), an authoritative artificial intelligence journal, and is also a dozen international journals such as IEEE TPAMI, JMLR, IEEE TKDE, IEEE TNNLS, IEEE TCYB, PR, etc. Of the reviewers are KDD (2019, 2020), IJCAI (2019, 2020), AAAI (2017, 2018, 2019, 2020), CIKM (2019), IEEE ICDM (2014, 2015, 2016, 2018, 2019) The program committee member is the chairman of the website of the China Pattern Recognition and Computer Vision Academic Conference PRCV 2018. He has participated in ICDM2010 (Sydney Australia), ICDM2011 (Vancouver, Canada), SDM2013 (Austin, USA), ICMLA2014 (Detroit, USA), IEEE Bigdata2016 (Washington, USA), DASFAA2018 (Gold Coast, Australia), ICDM2018 (Singapore), BIBM2018 (Madrid, Spain) ), IJCAI2019 (Macao, China) and other international conferences, communicate with academic peers, and make ORAL reports 16 times. His ICDM2010 thesis won the Best Paper Nomination Award; he was nominated for the 2012 Microsoft Asia Research Institute Scholar Award, the 2014 Chinese Computer Society Outstanding Doctoral Dissertation Nomination Award, and the 2019 Chinese Artificial Intelligence Society Outstanding Doctoral Dissertation Award. He is a member of the Chinese Artificial Intelligence Society-Pattern Recognition Professional Committee, Chinese Computer Society-Database Professional Committee, Chinese Computer Society-Computer Vision Professional Committee, CCF-YOCSEF Guangzhou Vice Chairman (2018-2020), CCF Guangzhou Branch Vice Chairman (2019.3-2021.3), CCF-YOCSEF Guangzhou Chairman (2020-2021).

His research areas are computer vision and machine learning. He has published more than 300 papers in domestic and international journals and academic conferences, including more than 80 CCF A-type papers, which have been cited by Google Scholar more than 16,000 times. The research results of the face recognition research and development were awarded (the research results in high-dimensional, nonlinear visual pattern analysis were awarded, and the research results in visual manifold modeling and learning won the CVPR2008 Best Student Poster Award Runner-up award. He The face recognition technology developed by the team has been applied to many products or systems such as the public security department and Huawei, and has achieved good economic and social benefits. He has been invited to serve as ICCV11, ACCV12 / 16/18, ICPR12 / 14/20, FG13 / 18/20, ICASSP14, BTAS18, CVPR19 / 20/21 and other field chairpersons of more than ten mainstream international conferences. Currently / former editor of IEEE TIP, CVIU, PRL, Neurocomputing, FCS and other international academic journals (AE) .

The research direction is human-computer interaction technology for robot applications, including human behavior recognition in natural scenes, human skeleton extraction, gesture modeling and recognition, eye tracking, emotional cognition and other related research. Published TIP, PR, Signal processing, Neurocomputing, ACM MM, ICME and other SCI journal articles and high-level international conference papers in research directions such as behavior recognition, emotional understanding, and human-computer interaction.

Twenty invention patents. Hosted the National Natural Science Foundation Youth Project and General Project, participated in a number of National Natural Science Foundation General Projects and horizontal projects of well-known enterprises. Academic activities include the ICME Registration Chair at the international conference, Conference Secretary of VALSE 2015, workshop chair of VALSE 2018 and Program Chair of ACM SIGAI CHINA symposium in TURC, 2017, 2018, etc., Local Chair of TURC 2019. Acted as the SAC chair of the VALSE Online Council and the Deputy Secretary-General of the ACM SIGAI CHINA Council. TIP, TNNLS, Pattern Recognition, Neurocomputing, CVPR, ICCV and other reviewers.

Research interests include models and algorithms for machine learning and pattern recognition, especially semi-supervised learning, metric learning, less shot learning, deep learning, etc., and the application of machine learning methods in computer vision and pattern recognition, especially images and videos Modeling, target detection, tracking and personnel re-identification.

His research interests cover machine learning and its applications in various fields. At present, he is mainly devoted to deep learning theory and optimization algorithms, reinforcement learning and its applications in transportation, computer security, computer graphics, medical and health care and other fields. He has published more than 40 papers in top AI journals and conferences, such as NeurIPS, ICML, CVPR, ACL, IJCAI, AAAI, ECML, etc. He was awarded "Alibaba Badamo Young Scholars 2019" and received the "Best Paper Finalist" from the top computer security conference ACM CCS 2018. Active researcher in the field of adversarial machine learning, deep learning and computer vision, and published more than 10 papers at top conferences, including ICML, ICLR, CVPR, ICCV, AAAI and IJCAI. He is also a program committee member or reviewer for many conferences and journals, such as ICML, ICLR, NeuroIPS, ECCV, KDD, AAAI, TPAMI, TNNLS and TKDE. He was invited to provide an adversarial machine learning tutorial at the 32nd Australian Artificial Intelligence Joint Conference (AI 2019) in Adelaide.

Lead the artificial intelligence department, and the vice president leads the artificial intelligence product center. He delivered the first full-featured DMS, smart sensor system and many other AI products on mass-produced cars. He worked as a researcher at Microsoft Research in Redmond, Washington, USA. His professional interests are in computer vision, imaging systems, human behavior understanding and biometrics, and autonomous driving. In 2018, he won the Australian Research Council's Early Discovery Professional Researcher Award. In the second LIP Challenge, the human analytical trajectory ranked first. In 2016, he won the China Institute of Electronics (CIE) Outstanding Doctoral Dissertation Award and in 2014. ILSVRC won the first place in the object detection task. He organized learning about imperfect data (LID) in CVPR 2019 and 2020, real-world recognition (RLQ) from low-quality images and videos in ICCV 2019, and learning from people (LIP) in CVPR 2019 Seminar. Mainly lies in applying machine learning techniques to solve computer vision problems such as object detection and semantic segmentation.

The main research direction is UAV vision and metric learning. At present, he has hosted and participated in more than 10 key projects of the Ministry of Science and Technology, the National Natural Science Foundation of China, Tianjin Science and Technology Commission and enterprises. . Published more than 70 papers in international conferences and journals related to machine learning and computer vision. In recent years, he served as the person in charge of the ECCV2018, ICCV2019 and ECCV2020 UAV vision seminars, and won the first prize of natural sciences of universities in Zhejiang Province and the first prize of natural sciences of Jiangsu Province.

His research interests are focused on computer vision and machine learning theories, methods, and key technologies that use face recognition as a typical case, especially with more than 20 years of research experience in the field of face recognition. In recent years, special attention has also been paid to face-based emotional computing, intelligent surveillance-oriented video structuring, and drone-oriented visual computing. At the theoretical and algorithm level, he and the team have very rich machine learning, especially deep learning research experience, and pay special attention to X data-driven machine learning theory and methods. The so-called X data here includes small data, unsupervised data, and semi-supervised data. , Weakly supervised data, dirty data, augmented data, etc.

He is the co-sponsor of the Vision and Learning Youth Seminar (VALSE), the first rotating chairman of the VALSE steering committee, the co-sponsor of the VALSE Webinar event and the chairman of the first online organizing committee. The number of participants of VALSE2019 (Hefei) exceeded 5,000, and the peak number of participants of VALSE Webinar reached 1,800, becoming one of the most influential academic conferences in the field of computer vision in China.

As a personal interest, he is deeply concerned about the progress of cognitive neuroscience and brain science, and is happy to think about and discuss the essential problems of biological vision and the inspiration of brain neuroscience to visual computing.

More than six years of working experience in embedded bottom layer development;

Proficient in C, VC ++, assembly language, able to independently complete DSP driver, ARM Linux driver development, proficient in I2C, SPI, UART, FLASH, DDR3, CF, GPIO, PWM, USB, WIFI, 4G, Ethernet and other peripheral drivers Development and application; Proficient in hierarchical architecture and development methods, with multiple DSP, Power PC, ARM hardware platform driver development experience;

Proficient in CIS, magnetic sensor, thickness sensor, camera and other sensor driver implementation and data collection;

Rich experience in Vxworks and Linux platform development, can independently complete uboot boot loading, Linux kernel adaptation and other transplantation work;

Ability to independently complete the software solution architecture, with rich experience, strong communication, logical analysis ability, learning ability and independent problem solving ability.

The main research fields include ERP consulting, cloud technology, big data, blockchain, artificial intelligence experts, computer vision, multimedia technology and machine learning. Professor Dr. OXBridge of Cambridge mainly focuses on confrontable machine learning, interpretable theory of artificial intelligence, computer vision, etc. He has published more than 50 papers in top artificial intelligence international conferences and journals such as CVPR, ECCV and NIPS, and won ICME2018 "Best Platinum Paper", AVSS2012 "Best Paper Award" and MICCAI2012 "Young Scholar Award". Research interests include applications such as intelligent security, video surveillance, micro-expression analysis, and feature learning behind it, continuous learning, small sample learning, and multimodal learning technology. Research areas include computer vision, pattern recognition, and machine learning. The main research directions include machine learning and pattern recognition methods, models, and applications in image processing, computer vision, and data mining.

Educational experience and academic research experience

 Guolong The University of Cambridge PhD

 Leverhulme Centre for the Future of Intelligence, LCFI http://www.lcfi.ac.uk/ Chief Scientist, Top Research Institute, LCFI Lab, UK

 Ph.D. in Computer Science from Cambridge University, professor at Cambridge University, Chief Technology Officer. Leading-edge technology leader. AI, IoT, RPA, OCR-AI, ERP, cloud, bigdata, blockchain, ICT, 5G, 3D, AR, VR, iCLIP, core industrial software, core algorithms, neutrino, quantum artificial intelligence and other top cutting-edge technologies.

 Studying in the UK in high school, studying undergraduate and master's degree in Cambridge University, and obtained a Ph.D. in computer science from Cambridge University in 2017. So far, as the project leader, he has conducted research and development in 863 major projects, sub-projects of the National Science and Technology Support Program and projects of the National Natural Science Foundation of China, and published 12 papers including SCI and 28 papers of EI . Collected 29 articles and obtained multiple invention patents. At present, the Institute of Information Science of the Chinese Academy of Sciences offers the basic course "Computer Vision and Image Understanding" and the professional basic course "Modern Computer Vision". Deepin Technology focuses on the integration of AI, blockchain big data analysis cloud, ERP consultants and technology megatrends. Drug discovery in Eastern Europe, fintech and blockchain, fintech and blockchain in Asia. 5 core technologies-biometrics, artificial intelligence, chatbots, data analysis, blockchain. 4 sub-sectors: loan, payment, savings, insurance. Conduct special case studies related to advanced biomedicine. Data science and AI enhance specific methods of predictive analysis. For our specific vision for designing investment strategies and working with strategic partners, advanced forecasting focuses on the integration of the DeepTech industry. Research scientists, clinicians and technicians in academia, pharmaceutical companies and AI companies. Our team includes Dr. Tasuku Honjo, Dr. Yusuke Honjo, PD-1 of Nobel Physiology, Principal Investigator of CSAIL, Regina Barzilay of MIT, Tommi Jaakkola, Manolis Kellis and Peter Szolovits.

 The main research areas are ERP consulting, cloud technology, big data, blockchain, artificial intelligence experts, and the person in charge has undertaken R & D work for 863 key projects, sub-projects of the National Science and Technology Support Program, and general projects of the National Natural Science Foundation of China. Published more than 510 scientific research papers, including 50 papers in SCI, 60 papers in EI, 129 papers in ISTP, and 225 invention patents

 For example, computer vision, multimedia technology, machine learning, professor of Tsinghua University. Integration with technological megatrends, leading analytical techniques are used in government / local government, education / medical / healthcare, finance, manufacturing, logistics, communications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals , Agriculture, retail, manufacturing, transportation, sports, aerospace, advertising, IOT, ICT and other industries.

 AI reporters sports intelligent driving AI accounting lawyers AI surgical robots AI questioning misdiagnosis no AI robot funeral VR AR 3D AI crime tracking AI image recognition. The China Science and Technology Association and the People's Government of Jiangsu Province and Zhejiang Province won the first prize in category A. Great health industry: Biomedicine, medical equipment, smart medical care, health management, etc. won the first prize in category A.

 The main research areas are computer vision, big data blockchain, and natural language processing. Chief Technical Expert of Alibaba Group

 Rugby 5G technology invented by AI deep learning (one of the artificial intelligence (AI) technologies used for image recognition and speech recognition). A rugby game analysis system combined with deep learning has been developed. The human body movement is captured with a camera to measure the elapsed time Body posture and movement, AI guides 5G technology data mining, artificial intelligence: 1, network analysis (social network) 2, data clustering 3, medical data processing 4, recommendation algorithm 5, precision education

 Machine learning professor at Tsinghua University. Integration with technology megatrends, cutting-edge analytical technology, government / local government, education / medical / healthcare, finance, manufacturing, logistics, communications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals for agriculture, Retail, manufacturing, transportation, sports, aerospace, advertising, Internet of Things, ICT and other industries. Professor A from Cambridge University, visiting professor of computer science from Tsinghua University, and visiting professor sponsored by visiting professor from the Chinese University of Science and Technology of Jiangsu Province, Kyoto University won the first prize of the big health industry: biomedicine, medical equipment, intelligent medicine, health care, etc. Won the best award in category A. ERP blockchain cloud technology big data artificial intelligence related fields (not limited to the field of speech processing including various fields of artificial intelligence) won the highest award in patent product competitions, Ali, Tencent, Huawei and other contracts and reached many contracts. AI x 5G has become the mainstream payment method, WYSIWYG, short video AI animation x 5G, AR VR and 3D, smart driving, finance, 5G telemedicine 5G AI medicine, smart driving, smart business, smart business, smart Medical, public security 5G robots, 5G materials, semiconductors, sports, entertainment and other technologies are our mainstream technologies, separation and purification, innovative drugs, biotechnology, chip design, quantum dot display, multi-touch, nanospheres, low carbon The global key technology of nano material intelligent driving, intelligent manufacturing, robot technology and intelligent medical treatment. Facial and body analysis technology, SLAM and 3D vision, general and professional image recognition, robot control and sensing, large-scale video understanding and mining, image and video processing to enhance medical image analysis, artificial intelligence computing platform, artificial intelligence super computing platform , Self-developed training framework, artificial intelligence high-performance storage by combining high-performance heterogeneous computing software and hardware, high-performance, design and development of low-cost, low-power edge artificial intelligence chips and solutions to develop partners. For intelligent driving and AIoT, it can provide edge-to-edge AI chips with ultra-high cost performance, high energy efficiency, open tool chain, rich algorithm model samples, and comprehensive activation services. Now, BPU (Brain Processing Unit) based on innovative artificial intelligence dedicated computing architecture is being successfully streamed. China's first edge artificial intelligence processor-a system focused on intelligent driving and a system focused on AIoT. And it has been commercialized on a large scale. Chinese Society of Artificial Intelligence, member of the British Science Council and member of the British House of Representatives Science and Technology Committee, British House of Representatives (Science and Technology Committee) British Council Open Data Institute (ODI) Allen Turing Artificial Intelligence Institute, University of Cambridge, University of Edinburgh, Oxford University, University of London, including Warwick University EPSRC Association

 Special visiting associate professor at Kyoto University, Japan, visiting associate professor at Nara Institute of Science and Technology. From 2017 to 2019, he served as a special researcher at a major industry-university-research project in the field of public safety in the Ministry of Education, Culture, Sports, Science and Technology, Kyoto University, Japan. He served as the special assistant professor and the actual head of the laboratory of the International Robot and Vision Joint Research Laboratory jointly established by the University of Nara Advanced Science and Technology University and CMU. In-depth participation or presided over more than 10 national and international cooperation projects (including 973, 863, the Fund ’s major research plan, Japan ’s Ministry of Education, Science, Technology, and Science and Technology Strategic Promotion Fee Major Project and other major topics, including the National Fund Committee Projects, key projects, and the Ministry of Education, Culture, Sports, Science and Technology Ministry of Science and Technology Youth Project, Japan-UK Bilateral Cooperation Project, Microsoft Asia Research Institute-funded projects, etc.), published more than 60 double-blind review of English papers (including more than 10 papers in the top meeting) . The research direction mainly focuses on content understanding based on images or videos. Research currently being undertaken includes the identification of people, the visual understanding of communication and interaction between people and humans and machines, the estimation of human and human poses, motion recognition, and behavior prediction. Among them, it is estimated that the hand gesture will win the single champion of hand-body interaction in the first global open challenge. The work of visiting students guided by won the ICPR 2018 Piero Zamperoni Best Student Paper Award and AutoML2018 Workshop Best Paper Award.

 Suitable for AI, IoT, RPA, OCR-AI, ERP, cloud, big data, blockchain, ICT, 5G, 3D, AR, VR, iCLIP, core industrial software, core algorithm, neutrino, government / local Government's most advanced technology education / medical / healthcare, finance, manufacturing, logistics, telecommunications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals, agriculture, retail, manufacturing, transportation, sports, aerospace, advertising, Internet of Things, ICT and other industries

 IEEE, NIPS, ICML, COLT, CVPR, ICCV, ECVC, IJCAI, AAAI, UAI, KDD, SIGIR, WWW, ACL, PAMI, IJCV, JMLR, AIJ have been published more than 100 times.

 Guolong is a leader in the field of artificial intelligence and deep learning. It is the CTO of the UK (Google) Research Institute. It leads more than 40 algorithm research teams and has accumulated more than 300 patents. Since the 1980s, he has been engaged in the related research of artificial intelligence in the fields of driverless cars and medical ECG-assisted diagnosis. Joined the Singapore Matsushita Research Institute in 1992, engaged in the design and application of audio and video signal processing and compression algorithms, and subsequently focused on research in the field of image recognition. In 2013, he took the lead in shifting the team to deep learning. He has made remarkable achievements in the field of deep learning and computer vision. He has the full-stack technical capabilities of computer vision, and the technology spans multiple application fields. In deep learning-based face detection And recognition, pedestrian detection and tracking, pedestrian re-recognition, vehicle recognition, automatic driving, driver behavior detection, mobile operating robots and other fields have achieved world-class results. In March 2019, Shen Shengmei joined the domestic AI company Pengsi Technology as chief scientist and dean of the Singapore Research Institute, devoted to research on related technologies in the fields of surveillance and security, smart cities, autonomous driving, intelligent robots and AI factory automation. . In 2019, Shen Shengmei led the Pengsi Singapore Research Institute to have won 13 computer vision technology-related world championships, covering Face Recognition, Pedestrian Re-identification (Person ReID), Vehicle Re-identification (Vehicle ReID) and anomalies Behavioral detection (Anomaly Detection) and many other fields of computer vision technology.

 Guolong, a researcher at the UK State Key Laboratory of Pattern Recognition at the ICF Institute in the UK and a senior researcher in computer vision. Doctoral tutor, executive vice president of Nanjing Artificial Intelligence Chip Innovation Research Institute. He obtained a Ph.D. in Computer Science from Cambridge University in 2017 and a Ph.D. in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences in 2017. At present, he is mainly engaged in deep learning, image and video content analysis, artificial intelligence chip design and other aspects. He has published more than 100 academic papers in related fields and edited two in English. Related achievements have successively won the Lu Jiaxi Young Talent Award of the Chinese Academy of Sciences, the Excellent Member Award of the Youth Promotion Association of the Chinese Academy of Sciences (, the First Prize of the Natural Science of the Chinese Institute of Electronics, the Second Prize of the Natural Science of the Ministry of Education, and the Second Prize of Science and Technology of the Chinese Image and Graphics Society. He is a member of IEEE, ACM, CCF and other international and domestic academic organizations. He serves as the deputy secretary general of the Pattern Recognition Committee of the Chinese Society of Automation. He is currently the editorial board member of the international journal Pattern Recognition, IET Computer Vision. Organizing Chairman, CCPR 2012 Publishing Chairman, ICIG 2019 special Session Chairman.

 His research directions include data clustering, social networks, recommendation systems, and medical data processing. He has published more than 100 academic papers, including top international publications such as IEEE TPAMI, IEEE TKDE, IEEE TCYB, IEEE TNNLS, and top international conferences such as KDD, AAAI, IJCAI, and CVPR. 2019 China-Japan Artificial Intelligence Society Outstanding Doctoral Dissertation Award, he is the associate editor (AE) of the Journal of Artificial Intelligence Research (JAIR), an authoritative artificial intelligence journal. He is a member of the Chinese Artificial Intelligence Society-Pattern Recognition Professional Committee, Chinese Computer Society-Database Professional Committee, Chinese Computer Society-Computer Vision Professional Committee, CCF-YOCSEF Vice Chairman (2018-2019), CCF Branch Vice Chairman (2019.3 -2021.3).

 Member of data analysis and artificial intelligence laboratory, scientist of artificial intelligence project (RIKEN AIP) of Japan National Laboratory Physical and Chemical Research Institute. The main research directions are machine learning and deep learning. Before joining Hong Kong Baptist University, he was engaged in post-doctoral research in the artificial intelligence project of the Institute of Physics and Chemistry in Japan (Prof. Masashi Sugiyama Professor Shan Shan's team). It is responsible for the development of robust deep learning methods for noise data (labels and samples), and the results won the RIKEN BAIHO Award for best achievement in 2019. . He served as a program committee member and reviewer for long-term service of machine learning top conferences (ICML, NeurIPS, AISTATS and ICLR) and top journals (JMLR, TPAMI and MLJ), and was elected Area Chair of NeuroIPS’20.

 In recent years, deep learning has made rapid progress in many issues such as image analysis, speech recognition, and natural language processing, and has become an indispensable key technology in many intelligent systems and applications. As the performance of deep learning models continues to increase, their computational complexity and resource consumption also increase, which poses major challenges for the deployment of network models, especially on edge-end devices with limited resources. How to efficiently and efficiently calculate these network models is becoming a key issue. This report will introduce the efficient calculation of deep neural network models from the perspective of quantitative learning. First introduce the model compression and acceleration method based on quantization learning; then the quantization engine based on operator-level acceleration; finally introduce the architecture and chip based on quantization calculation.

 Dr. Guolong is currently a professor (lifetime faculty) at the School of Computer Science, Cambridge University. United Kingdom

The Institute of Information and Communication Technology works at the Computer Vision Group of the Oxford Cambridge ICF Laboratory. Currently engaged in research work in statistical machine learning and computer vision. He has presided over many scientific research projects and published more than 120 papers in important international academic journals and conferences. The journals that have served or served as associate editors include: Pattern Recognition, IEEE Transactions on Neural Networks and Learning Systems. He has served as a program member of important international academic conferences (ICCV, CVPR, ECCV, etc.) many times. He had a bachelor's degree in high school in the University of Cambridge and a doctorate in computer artificial intelligence. In 2016, it was awarded Future Fellowship by the Australian Research Council.

 The main research directions are machine learning and computer vision. In 2017, he conducted medical image analysis research at the University of Cambridge, UK, and published more than 70 papers in international conferences and journals, including NIPS, CVPR, ICCV, AAAI, IJCAI and other CCF-A conferences and IEEE Trans. Journals (including IEEE T-PAMI / T-IP / T-NNLS / T-CYB) 35 papers, many CVPR, NIPS papers were selected for Oral and Spotlight papers. Presided over the National Natural Science Foundation Youth Project and General Project, Tianjin Natural Science Fund General Project. Acted as a program committee member or reviewer for IJCAI, AAAI, CVPR, ICCV and other conferences, served as chairman of the local organization of China Machine Learning Conference (CCML 2017), and an international journal IEEE T-PAMI / T-IP / T-NNLS / T-CYB Reviewers.

 Mainly engaged in research work on multi-modal data collaborative computing. He has presided over more than 20 topics such as the National Natural Science Foundation of China and the "863" Program of the Ministry of Science and Technology. He has been granted 26 invention patents and published more than 100 academic papers, including nearly 80 papers in world-class journals and top international conferences, including IEEE TNNLS, TIP, TCYB, ICML, NIPS, ICCV, CVPR, etc. Cited by Google Scholar more than 2,200 times, 18 academicians / Fellow from China, the United States, Britain and other countries cited his research work and gave positive comments. Research achievements won 1 second prize of the National Natural Science Award (2016, the third accomplisher), 2 first prizes of the Shaanxi Science and Technology Award (2011, 2015, the third accomplisher), and 1 second prize of the Ministry of Education Natural Science (2013).

 Academic achievements: published more than 30 articles in IEEE / ACM journals such as TPAMI, TIP, and CCF Class A academic conferences such as CVPR, ICCV, and AAAI. The articles published by Google Scholar are cited more than 1,200 times, and the highest impact factor of a single journal is 17.73. The related work on micro-expression analysis can be seen in the articles published by the international authoritative media such as the MIT Technology Review and the British Daily Mail. Presided over a key research and development project of the Ministry of Science and Technology. He has successively hosted the postdoctoral research fund project of the Finnish Information Technology Society and the ICT2023 special project of the Finnish Academy of Sciences (including co-hosting), and participated in many Finnish Academy of Science fund projects and China National Natural Science Foundation projects. Teaching and student training: Trained and jointly trained seven graduate students, and taught courses such as "Deep Learning and Its Applications" and "Emotional Computing". Academic: IEEE / IEEE-CS / AAAI / CCF member, many times invited to serve as IEEE T-PAMI, T-IP, T-NNLS, T-CYB, T-CSVT, T-MM, Springer IJCV, Elsevier Pattern Recog. And other important international academic journals, as well as IEEE ICCV, IEEE CVPR, AAAI, ACM MM and other important international academic conferences, program chairmen or reviewers, and obtained outstanding reviews from international journals Pattern Recognition (2017) and Neurocomputing (2017) Contributor title. Held five thematic seminars (CVPR20, FG18-20, ACCV16) at mainstream international conferences with international counterparts, and served as the guest editorial board of the Springer SIVP journal and the branch chairperson of many international conferences / thematics Program Committee Member

 Since returning to China in July 2019, Professor Guolong has presided over many key projects such as the National Natural Science Foundation Youth Project and the Jiangsu Province New Generation Artificial Intelligence Key R & D Project. Professor Tan Mingkui has been engaged in research work on machine learning and deep learning, and has a certain research foundation in structural optimization and theoretical analysis of deep neural networks. In recent years, the relevant achievements completed by a work or corresponding author have been published in the top artificial intelligence international conferences such as NIPS, ICML, ACML, AAAI, CVPR, IJCAI and artificial intelligence authoritative journals such as IEEE TNNLS, IEEE TIP, IEEE TSP, IEEE TKDE, JMLR, etc. . Facing the national strategy and major needs of artificial intelligence, he has long been engaged in theoretical and applied research on imitation imaging, brain-like recognition, and brain-like evaluation. Hosted a number of national projects such as the National Natural Science Foundation, the National Defense Science and Technology Innovation Project of the Military Science and Technology Commission, and the Young Talents Project of the Joint Fund of the Ministry of Education. Information processing system and delivery. Has published more than 60 related academic papers (more than 40 first authors), including more than 40 SCI source journal papers, three of which were selected as ESI highly cited papers.

 Guolong, Ph.D., Dean of Haniuqiao Intelligent Technology Nanjing Research Institute, tutor of student entrepreneurship of Nanjing University, the main research field is computer vision and machine learning, top journals in related fields such as IEEE TPAMI, IEEE TIP, IEEE TNNLS, Machine Learning Journal and other top conferences such as CVPR, ICCV, IJCAI, ICDM, ACCV, etc. published more than 20 papers, and led the team to win 3 world championships in international authoritative computer vision competitions such as iNaturalist, Apparent Personality Analysis. Organized tutorials entitled "Fine-Grained Image Analysis" at the important international conferences PRICAI 2018 and ICME 2019. Author of "Analytic Deep Learning-Principles and Visual Practice of Convolutional Neural Networks". He has been honored as the best reviewer of CVPR 2017 and the special scholarship of the president of Nanjing University for doctoral students. He has served as a PC member of ICCV, CVPR, ECCV, NIPS, IJCAI, AAAI and other international conferences. NEC US Silicon Valley Laboratory is engaged in research work. The research direction is massive multimedia information retrieval and computer vision, focusing on pedestrian re-recognition, fine-grained object recognition, and scene understanding research. He has published more than 50 papers in authoritative international journals such as IEEE T-PAMI, TIP, TMM and ICCV, CVPR, ACM MM, AAAI and other authoritative international conferences as the first / corresponding author. , Outstanding Doctoral Dissertation of Chinese Computer Society, Outstanding Doctoral Dissertation of Chinese Academy of Sciences, Microsoft Scholar Award, etc. He has presided over the National Natural Science Foundation of China, major R & D plan cultivation projects, national key R & D plans and other projects.

 Main research interests include kernel algorithms, multi-view clustering algorithms, feature selection algorithms, etc. Published more than 90 papers and 46 SCI (including 17 IEEE Transactions, including IEEE T-PAMI, IEEE T-KDE, IEEE T-IP, IEEE T-IFS, IEEE T-NNLS, IEEE T-Cybernetics, IEEE T -MM, etc.), 23 papers designated by the Chinese Computer Society as Class A conference papers, Google Scholar cited more than 1,600 times, and 3 papers entered the top 10% of ESI computer science disciplines. He served as a member of the program committees of AAAI 2016-2019, IJCAI 2016-2019, NIPS 2016-2018, and the AAAI 2020 senior program committee. Hosted one each of the National Natural Science Foundation Youth Fund, the General Project, and the Excellent Youth Fund, and the research results won the first prize of Zhejiang Natural Science.

 VALSE implements AC and CASIG-BVD committee members. The main research areas are deep learning, pattern recognition, computer vision and multimedia analysis, especially focusing on unrestricted / large-scale / small samples / heterogeneous / set-based face recognition, image generation based on deep confrontation learning, super-resolution, Model compression based on knowledge distillation, (non-) instance-level fine-grained character analysis. At present, it has undertaken / participated in 3 key R & D projects (ranking 1/3/5). Published more than 30 academic papers in T-PAMI, IJCV, T-IP, NeuroIPS, CVPR, IJCAI, ECCV, ACM MM, AAAI, BMVC, WACV and other authoritative international journals / conferences in this field, with the highest single impact factor of 17.73. Relevant work won the Singapore Pattern Recognition and Machine Intelligence Association PREMIA 2019 Lee Hwee Kuan Award (Gold Award), the top conference in the international multimedia field ACM MM 2018 Best Student Paper Award; ICCV 2017 MS-Celeb-1M large-scale face recognition competition Hard Set / Random Set / Low-Shot Learning champion for all tasks; CVPR 2017 LIP contest character analysis and character pose estimation runner-up for all tasks; National Institute of Standards and Technology NIST 2017 IJB-A unrestricted face recognition competition face verification and face discrimination Champion of all missions. Act as IJCV, T-MM, T-IFS, T-CSVT, Neurocomputing, NeuroIPS (NeurIPS 2018 top 30% best reviewer), CVPR, ICCV, ECCV, ACM MM, AAAI, ICLR, ICML, UAI, etc. Invited reviewers for mainstream international journals / conferences.

 985 Cambridge master degree, software major, solid theory and skill reserve, has been engaged in software development after graduation, has worked in well-known enterprises such as AMD, Qualcomm, Xilinx, etc., is currently the first AI of Xilinx China team Senior Technical Expert of Solution

 Proficient in Linux system software development, with core upstream submission.

 At this stage, we are focusing on the application of deep learning technology in machine vision. Possess Caffe / Tensorflow / Keras / Pytorch framework model deployment experience (Xilinx platform, Nvidia GPU / Jeston Nano), as well as elementary model retraining capabilities.

 English can be used as working language

 Good technology sales ability supported by rich and extensive technology development experience.

 Technical ability

 Linux kernel and driver development, has rich experience in Linux kernel driver development and debugging based on X86 and arm platforms.

 Familiar with V4L / DVB open source video driver framework and ALSA audio driver framework and FFMpeg / Gstreamer audio and video application framework

 Familiar with USB / PCI / Ethernet / I2C / ACPI driver and related protocols; familiar with network, TCP / IP, AVB; DPDK;

 Linux environment programming, with C / C ++ / Shell / Python application programming experience, master version control tools such as git / subversion / perforce; familiar with Yocto / buildroot;

 Familiar with the open source community and related development processes, with Linux kernel upstream development experience (xHCI / ACPI, etc.)

 Familiar with xilinx fpga soc platform and tool set and embedded software stack.

 Machine vision related (CNN / OpenCV / Tensorflow / Caffe / Pytorch)

Other basic professional skills

 C / C ++ / Python / Shell / Gdb / Git

 U-boot / FreeRTOS

 Yocto / Buildroot

 QT / FFMpeg / GStreamer

 Docker / Xen / KVM

 FPGA HLS programming and IPI design

 Xilinx tools (Vivado / SDK / SDSoC / Petalinux / DNNDK / xfDNN)

Github https://github.com/guolong70

Academic honors

Thesis 1. Relevant work on instantaneous sentiment analysis has been reported by the international mainstream technology media including the MIT Technology Review of the United States and the Daily Mail of the United Kingdom (2019);

2. Obtained oral reporting qualifications at several important international conferences including ICCV 2019 (Oral acceptance rate 4.3%)

3. As the Area Chair of important international conferences ACM MM 2020, IEEE IPTA 2016 and IEEE PCSPA 2011.

4. Organize five seminars at major international academic conferences: IEEE CVPR 2020, ACCV 2016, IEEE FG 2018/2019/2020.

5. Awarded "Outstanding Reviewer" by Elsevier journal "Pattern Recognition" (2017, latest impact factor 5.898), "Neurocomputing" (2017, latest impact factor 3.317)

6. Once served as Guest Editor of the international journal Springer "Signal, Image and Video Processing" (SIVP) (2019).

7. The review article "A Review of Recent Advances in Visual Speech Decoding" on visual language coding was listed as "Editor's Choice Article" (2014) by Elsevier international journal "Image and Vision Computing".

8. As a co-instructor, he won two first prizes in the 6th China Graduate Smart City Technology and Creative Design Competition (2019).

Awards and honors:

1. The first prize of the 2018 Natural Science Award; visual robust feature extraction and nonlinear analysis; all completed by: Lai Jianhuang, Zheng Weishi, Xie Xiaohua, Ruan Bangzhi, Wang Changdong, Zhu Junyong, Ma Jinhua, Huang Jian; completion unit: Sun Yat-sen University, Hong Kong Baptist University.

2. Top-notch young talents in science and technology innovation in 2016.

3. Winner of the 2016 IEEE-Outstanding Youth Science Foundation.

4. Outstanding doctoral dissertation of the Chinese Artificial Intelligence Society in 2015.

5. 2014 Chinese Computer Society Outstanding Doctoral Dissertation Nomination Award.

6. SIAM SDM 2013 Student Travel Award.

7. 2012 Microsoft Research Asia (MSRA) Fellowship Nomination Award.

8. IEEE ICDM 2011 Student Travel Award.

9. IEEE ICDM 2010 Honorable Mention Award for the Best Research Paper.

10. IEEE ICDM 2010 Student Travel Award.

Research projects:

1) The Fundamental Scientific Research Business Fee of Cambridge University in 2019-Emerging Disciplinary Interdisciplinary Funding Project, Establishment of Brain Functional Rehabilitation System and Clinical Demonstration Application of Deaf Patients after Cochlear Implantation Based on Analysis of EEG Data -2020.12, host.

2) 2019 National Natural Science Foundation of China-General Project, Research and Application of Heterogeneous Data Clustering Algorithm Based on Similarity Learning, No. 61876193, 2019.01-2022.12

3) 2019 National Key R & D Program Project "Community Risk Monitoring and Prevention Key Technology Research" Topic 5 "'Data-Calculation' In-depth Interactive Community Risk Scenario Calculation and Forecasting Technology", No. 2018YFC0809705, 2018.07-2021.06, Topic 5 Zhongshan Head of the university.

4) Construction of a comprehensive information service platform for college students' innovation and entrepreneurship projects in 2019 "University Innovation and Entrepreneurship Education Project", No. 2019PT204, 2019.01-2020.12, hosted by participants.

Main academic:

1) Associate Editor

-Journal of Artificial Intelligence Research (JAIR, CCF B, Since Aug. 2019).

2) Conference Co-Chairs:

-PRCV 2018, Website Co-chair.

3) Program Committee Members:

-IEEE ICDM 2014, 2015, 2016, 2018, 2019.

-AAAI 2017, 2018, 2019, 2020.

-KDD 2019, 2020.

-IJCAI 2019, 2020.

-CIKM 2019.

-IJCAI 2018 Demo Track, IJCAI 2019 Demo Track, IJCAI 2020 Demo Track.

-The 8th IEEE International Conference on Big Knowledge (IEEE ICBK) 2017.

-The 4th IEEE International Congress of Big Data Congress 2015.

4) Reviewers:

-IEEE TPAMI, IEEE TCYB, IEEE TKDE, IEEE TNNLS, JMLR, IEEE TII.

-Pattern Recognition, Neural Networks, Neurocomputing, Knowledge-Based Systems, Information Sciences, KAIS.

-Many other good journals ...

Works:

1. Please kindly refer to my Google scholar homepage: https://scholar.google.com/citations?user=Vkzd7MIAAAAJ&hl=zh-CN [pdf]

Publication Service:

• Associate Editor of Frontier of Computer Science (2018-)

• Associate Editor of IEEE Trans. On Image Processing (2015-2018, a Rank-A journal by CCF)

• Associate Editor of Journal of Computer Vision and Image Understanding (2017-)

• Associate Editor of Pattern Recognition Letters (2017-)

• Associate Editor of Neurocomputing (2012-2016)

• Editor Board member of EURASIP Journal of Image and Video Processing

• Associate Editor of IPSJ Transactions on Computer Vision and Applications (CVA)

• Associate Editor of IET Computer Vision (2020-)

Conference Services:

• Area Chair of IEEE Conference on Face and Gesture Recognition 2020 (FG2020)

• Senior PC of the 34th AAAI Conference on Artificial Intelligence (AAAI-20)

• Area Chair of IEEE / CVF Conference on Computer Vision and Pattern Recognition 2020

• Area Chair of IEEE / CVF Conference on Computer Vision and Pattern Recognition 2019

• Area Chair of IEEE International Conference on Biometrics: Theory, Application AND Systems (BTAS 2018)

• Area Chair of IEEE International Conference on Automatic Face and Gesture Recognition (FG 2018)

• Area Chair of Asian Conference on Computer Vision (ACCV) 2018

• Area Chair of International Conference on Computer Vision (ICCV) 2011

• Program Chair of Chinese Conference on Biometric Recognition 2014, 2015, 2016

• Area Chair of International Conference on Pattern Recognition (ICPR) 2012

• Area Chair of Asian Conference on Computer Vision (ACCV) 2012

• Area Chair of International Conference on Face and Gesture Recognition (FG2013)

• Workshop Chair of Asian Conference on Computer Vision (ACCV) 2014

• Area Chair of Asian Conference on Computer Vision (ACCV) 2016

• Area Chair of International Conference on Pattern Recognition (ICPR) 2014

1. Machine learning: deep learning and beyond

Focus on machine learning methods and technologies under complex data conditions, especially machine learning methods under small-scale, weakly labeled, semi-supervised, non-complete, and non-pure data conditions. Research ideas include transfer learning, meta-learning, and knowledge guidance Learning algorithm etc.

My team focuses on new machine learning methods for scenarios with complex data conditions, especially small data, wealy-labeled data, semi-supervised data, incomplete data, where new models and new optimizing methodsareneeded to design. In terms of methodology, we are interested in transfer learning, meta-learning, and knowledge-guided learning.

2. From face recognition to

deep human understanding: methods, technologies, and applications

Engaged in face detection and tracking, facial key feature point positioning, face recognition and verification, expression analysis and recognition, face attribute estimation, lip recognition, heart rate estimation, concentration estimation, sight estimation and tracking, etc. In-depth understanding of research topics related to computing. Application cooperation units include: Huawei, Ping An, Baidu, Yinchen Technology, Samsung, Omron, Panasonic, Qualcomm, China Mobile, etc., and won the Huawei Excellent Cooperation Achievement Award twice in 2016 and 2017. Related achievements have also won the second prize of the National Natural Science Award in 2015, and the second prize of the National Science and Technology Progress Award in 2005.

My team is interested in all kinds of vision tasks from face recognition to human understanding, including (but not limited to) face detection and tracking, facial landmark locating, face alignment, face identification, face verification, face retrieval, expression recognition, facial attribute estimation, 3D face reconstruction, face parsing, lip reading, heart rate estimation, engagement estimation, gaze tracking, etc. We have broad cooperation with industrial parters, including Huawei, Ping'an, Qualcomm, China Mobile, Baidu, Isvision, Samsung, Omron, Panasonic, etc. Especially, our face recognition technology has been used on Huawei smart phone and Huawei Cloud album.

3. Generic object detection, segmentation, and recognition

Focus on computer vision tasks such as general target detection, segmentation and recognition, and focus on research topics such as pedestrian detection and tracking for video surveillance scenarios, human pose estimation and segmentation, vehicle detection and tracking, pedestrian re-recognition, and vehicle re-recognition.

My team is also interested in generic object detection, segmentation, and recognition, especially pedestrian detection and tracking, human pose estimation and body segmentation, vehicle detection and tracking, human and vehicle re-identification etc. These technologies are applied to video surveillance.

1. Deep learning methods and techniques under complex sample conditions

Project Type: Key R & D Program Project (Project) of the Ministry of Science and Technology

Project time: 2018-2022

Project leader: guolong (project leader)

2. Video big data multi-object detection technology cooperation project

Project Type: Enterprise Cooperation Project

Project time: 2016-2017

Project leader: guolong

3. Face recognition technology cooperation project

Project Type: Enterprise Cooperation (Huawei)

Project time: 2016-2019

Project leader: guolong

4. Visual pattern analysis and recognition

Project Type: Fund Committee Excellent Youth Project

Project time: 2013-2015

Project leader: guolong

Professional Experience

Professional Experience Management China CEO / CTO & President CTO and President of China

2012/01-present Alibaba contract enterprise Haniuqiao Intelligent Technology Co. Ltd. (10 years)

Computer service (system, data service, maintenance) | 500-1000 people | Foreign capital (Europe and America) | Full-time

Management China CEO & CTOPresident China CEO CEO and CTO President

Job description: 2012 / 1—present: Alibaba contract enterprise Haniuqiao Intelligent Technology Co. Ltd.

Position: China CEO & President China CEO & President

Company Profile:

Alibaba contract company Haniuqiao Intelligent Technology is a company that sells, consults, implements and services IT products. The company brings together many top 500 global top companies from Oracle, Microsoft, SAP, IBM, EMC, HP, DELL, Cisco, etc. The IT & Internet company has senior managers, business development personnel and technical elites with rich work experience to join the formed company. The Chinese company is Hangzhou, with branches in Europe, Britain, Japan and Shanghai, China.

The company's main business:

The company is a global strategic partner of Oracle Corporation of the United States. The company's main business is engaged in Oracle database, middleware, ERP, CRM, SCM, HRM, BPM, EPM, APM, PLM, BI, Internet, big data, Oracle cloud computing (DaaS , IaaS, PaaS, SaaS) AI (artificial intelligence) full range of products and IT & Internet products based on Microsoft, SAP, IBM, EMC, HP, DELL, Cisco and other fields, to provide customers in the United States, Europe, China with the best IT Consultation, implementation and service of Internet system solutions ...

Number of employees in Chinese companies: 600+

Work Location: Shanghai Nanjing Hangzhou

My job: report to the chairman (BOSS)

30 of my direct reports: Vice President of Sales & Marketing, Vice President of Technology, Vice President of Human Resources & Administration, Vice President of Finance

My responsibility:

1. Fully authorized by the board of directors of the head office, fully leading the daily operation and management of Chinese companies, and taking overall responsibility for all tasks;

2. Fully preside over the formulation of the annual business development strategy, business development strategy, business development plan of the Chinese company, and the realization of the annual business development goals of the Chinese company;

3. Fully responsible for the establishment, training and selection of middle and senior management personnel of the senior management team of the Chinese company, and review the establishment plan and basic management system of the company's internal management organization;

4. Fully preside over the establishment of an efficient Chinese company organization system (operation management system (administration department, personnel department, finance department), business development system (marketing department, sales department), technical support service system (pre-sales department, middle-sales department, after-sales department ) And organizational processes;

5. According to changes in the Chinese market and environment, timely adjust the organizational structure and resource allocation of Chinese companies, manage and supervise the daily operations of Chinese companies at all times, and make decisions on major daily company matters;

6. Responsible for close contact and cooperation with foreign and domestic partner company executives;

7. Responsible for public relations and interaction with the client's high-level (final decision-making layer);

8. Responsible for maintaining good communication with the company's board of directors, reporting to the company's board of directors on the implementation of the Chinese company's business development plan, the use of funds and profit and loss;

Number of subordinates: 600 | Reporting object: Chairman of the United States (BOSS) | Reason for leaving: On the job.

Main performance: My performance:

2019-2020: Completion performance: 500 million yuan

2018-2019: Completion performance: 350 million yuan

2017-2018: Completion performance: 300 million yuan, the customers I signed up with are:

We provide Oracle, SAP, Microsoft, IBM, HP, DELL, EMC, Cisco, Salesforce, Google, Amazon and other best and most advanced IT & Internet, Internet of Things, intelligent network, cloud computing, big data, AI manuals for the following customers Consultation of intelligent and other system solutions + implementation of the system + daily maintenance of the system + customized development and other value-added services. . .

Banking, securities, insurance, and industry customers include:

Bank of America

Citibank

HSBC

Deutsche Bank

Shanghai Pudong Development Bank

Shanghai Bank of Communications

China UnionPay

Shanghai Bank

Industrial Bank

Shanghai Rural Commercial Bank

China Minsheng Bank

China Everbright Bank

Qingdao Bank

Bank of Ningbo

Chongqing Commercial Bank

Shenzhen Development Bank

Bank of Nanjing

Shanghai Securities

Oriental Securities

GF Securities

Shenyin Wanguo Hongyuan Securities

Haitong Securities

China Galaxy Securities

China Pacific Insurance

China Life Insurance Company

Customers in the government industry include:

Shanghai Municipal Finance Bureau

Shanghai Police Station

Shanghai custom

Shanghai Personnel Bureau

Shanghai Human Resources and Social Security Bureau

Shanghai Water Affairs Bureau

Shanghai Education Commission

Zhejiang Provincial Department of Finance

Zhejiang Provincial Public Security Department

Zhejiang Provincial Department of Human Resources and Social Security

Jiangsu Provincial Department of Finance

Jiangsu Provincial Public Security Department

Jiangsu Provincial Department of Human Resources and Social Security

Customers in the education industry include:

Shanghai University of Finance and Economics

Shanghai Jiaotong University

Shanghai University

Shanghai University of Technology

Shanghai University of Political Science and Law

Customers in the medical industry include:

Shanghai Health Committee

Shanghai Renji Hospital

Shanghai Huashan Hospital

Shanghai Ruijin Hospital

Shanghai Oriental Hospital

Automotive manufacturing, aviation, glass manufacturing, steel manufacturing, food manufacturing, paper manufacturing, electrical appliance manufacturing, consumer goods manufacturing, industry customers are:

Shanghai Automobile Group

Shanghai Volkswagen

Shanghai General Motors

China Eastern Airlines

Shanghai Bao Wuhan Steel Group

Shanghai International Port Group

Shanghai Bright Food Group

Shanghai Tobacco Group

Shanghai Pharmaceutical Group

Shanghai Electric Group

Shanghai Jahwa

Procter & Gamble (China) Company

Midea Group

Unilever China

Nanjing Automobile Group

Tianjin Tiens Group

Chongqing Changan Automobile

Shanghai Waigaoqiao Shipbuilding

Tsing Tao beer

China National Textile Group

Vinda Paper

Shanghai Feike Electrical Appliance

Shanghai Chenguang Stationery

Shanghai Hitachi Electric

Shanghai Guansheng Garden

Customers in the telecommunications industry include:

Zhejiang Mobile Communication Company

Shanghai Mobile Communications Corporation

Shanghai Telecom

Jiangsu Mobile Communications Corporation

Sichuan Mobile Communication Company

2012/01-2017/01 employed by Microsoft Software System Co. Ltd. (6 years)

Computer service (system, data service, maintenance) | 150-500 people | Foreign capital (Europe and America) | Part-time

China CEO & President China CEO & CTOPresident UK China CTO CEO CEO & President

Job description: 2012-2017: employed by Microsoft Software System Co. Ltd. of the United States

Position United Kingdom China CEO & CTOPresident United Kingdom China CTO CEO CEO and President

Company Profile:

Microsoft Software System Co. Ltd. of the United States is an IT product sales, consulting, implementation and service company established in the United States. The company brings together many companies from the world's top 500 IT companies such as Oracle, Microsoft, SAP, IBM, EMC, HP, DELL, Cisco and other top management companies with rich work experience, business development personnel and technical elites to join the formation of the company , The US company is headquartered, with branches in Europe, the United Kingdom and Shanghai, China.

The company's main business:

The company is a global strategic partner of the United States. The company's main business is engaged in Oracle database, middleware, ERP, CRM, SCM, HRM, BPM, EPM, APM, PLM, BI, Internet, big data, Oracle cloud computing (DaaS, IaaS , PaaS, SaaS) full range of products and IT multi-domain products based on Microsoft, SAP, IBM, EMC, HP, DELL, Cisco, etc., to provide customers in the United States, Europe, China with the best IT system solution consulting, implementation and services …

Number of employees in the UK: 200+

Number of employees in Chinese companies: 400+

Place of work: Shanghai, UK

My job: reporting to the chairman of the US headquarters (BOSS)

30 of my direct reports: Vice President of Sales & Marketing, Vice President of Technology, Vice President of Human Resources & Administration, Vice President of Finance

My responsibility:

1. Fully authorized by the board of directors of the American Headquarters to fully lead the daily operation and management of Chinese companies and take overall responsibility for all tasks;

2. Fully preside over the formulation of the annual business development strategy, business development strategy, business development plan of the Chinese company, and the realization of the annual business development goals of the Chinese company;

3. Fully responsible for the establishment, training and selection of middle and senior management personnel of the senior management team of the Chinese company, and review the establishment plan and basic management system of the company's internal management organization;

4. Fully preside over the establishment of an efficient Chinese company organization system: operation management system (administration department, personnel department, finance department), business development system (marketing department, sales department), technical support service system (pre-sales department, middle-sales department, after-sales department) Department) and organizational process;

5. According to changes in the Chinese market and environment, timely adjust the organizational structure and resource allocation of Chinese companies, manage and supervise the daily operations of Chinese companies at all times, and make decisions on major daily company matters;

6. Responsible for close contact and cooperation with domestic and foreign partner company executives;

7. Responsible for the public relations and interaction with the client's high-level (decision-making layer, clapboard layer);

8. Responsible for maintaining good communication with the board of directors of the US parent company, and reporting to the board of directors of the US parent company on the implementation of the operation and development plan of the Chinese company, the use of funds and the profit and loss situation;

Number of subordinates: 400 | Reporting object: Chairman of the US headquarters (BOSS) | Reason for leaving: Cambridge University part-time while studying, graduated in the UK

Main performance: My performance;

2016-2017: Completed performance: 500 million yuan

2015-2016: Completion performance: 300 million yuan

2014-2015: Completion performance: 250 million yuan

2013-2012: Completion performance: 200 million yuan

2011-2012: Completion performance: 150 million yuan

The contracted customers are:

We provide the following customers with consulting of the best and most advanced IT system solutions for Oracle, SAP, Microsoft, IBM, HP, DELL, EMC, Cisco, Salesforce, + implementation of the system + daily maintenance of the system + custom development Other value-added services. . .

Banking, securities, insurance, and industry customers include:

Bank of America

Citibank

HSBC

Deutsche Bank

Shanghai Pudong Development Bank

Shanghai Bank of Communications

China UnionPay

Shanghai Bank

Industrial Bank

Shanghai Rural Commercial Bank

China Minsheng Bank

China Everbright Bank

Qingdao Bank

Bank of Ningbo

Chongqing Commercial Bank

Nanjing Commercial Bank

Guangdong Development Bank

Shenzhen Development Bank

Xiamen City Commercial Bank

Bank of Nanjing

Shanghai Securities

Oriental Securities

GF Securities

Shenyin Wanguo Hongyuan Securities

Haitong Securities

China Galaxy Securities

China Pacific Insurance

China Life Insurance Company

Customers in the government industry include:

Shanghai Municipal Finance Bureau

Shanghai Police Station

Shanghai custom

Shanghai Personnel Bureau

Shanghai Human Resources and Social Security Bureau

Shanghai Water Affairs Bureau

Shanghai Education Commission

Zhejiang Provincial Department of Finance

Zhejiang Provincial Public Security Department

Zhejiang Provincial Department of Human Resources and Social Security

Jiangsu Provincial Department of Finance

Jiangsu Provincial Public Security Department

Jiangsu Provincial Department of Human Resources and Social Security

Customers in the education industry include:

Shanghai University of Finance and Economics

Shanghai Jiaotong University

Shanghai University

Shanghai University of Technology

Shanghai University of Political Science and Law

Customers in the medical industry include:

Shanghai Health Committee

Shanghai Renji Hospital

Shanghai Huashan Hospital

Shanghai Ruijin Hospital

Shanghai Oriental Hospital

Automotive manufacturing, aviation, glass manufacturing, steel manufacturing, food manufacturing, paper manufacturing, electrical appliance manufacturing, consumer goods manufacturing, industry customers are:

Shanghai Automobile Group

Shanghai Volkswagen

Shanghai General Motors

China Eastern Airlines

Shanghai Bao Wuhan Steel Group

Shanghai International Port Group

Shanghai Bright Food Group

Shanghai Tobacco Group

Shanghai Pharmaceutical Group

Shanghai Electric Group

Shanghai Jahwa

Procter & Gamble (China) Company

Midea Group

Unilever China

Nanjing Automobile Group

Tianjin Tiens Group

Chongqing Changan Automobile

Shanghai Waigaoqiao Shipbuilding

Tsing Tao beer

China National Textile Group

Vinda Paper

Shanghai Feike Electrical Appliance

Shanghai Chenguang Stationery

Shanghai Hitachi Electric

Shanghai Guansheng Garden

Customers in the telecommunications industry include:

Zhejiang Mobile Communication Company

Shanghai Mobile Communications Corporation

Shanghai Telecom

Jiangsu Mobile Communications Corporation

Sichuan Mobile Communication Company

Project experience

2017/01-So far I have signed clients:

Affiliated companies: Alibaba, Amazon, Tencent and other famous enterprises

Project description: The customers I signed up with are:

We provide Oracle, SAP, Microsoft, IBM, HP, DELL, EMC, Cisco, Salesforce, Google, Amazon, optimal, state-of-the-art IT & Internet, cloud computing, big data, AI artificial intelligence and other systems for the following customers Solution consultation + system implementation + system after-sales maintenance service + customized development and other value-added services. . .

Banking, securities, insurance, and industry customers include:

Bank of America

Citibank

HSBC

Deutsche Bank

Royal Bank of Scotland

Shanghai Pudong Development Bank

Shanghai Bank of Communications

China UnionPay

Shanghai Bank

Industrial Bank

Shanghai Rural Commercial Bank

China Minsheng Bank

China Everbright Bank

Qingdao Bank

Bank of Ningbo

Bank of Chongqing

Bank of Nanjing

Guangdong Development Bank

Shenzhen Development Bank

Xiamen City Commercial Bank

Bank of Suzhou

Bank of Jiangsu

Shanghai Securities

Oriental Securities

Everbright Securities

Aijian Securities

GF Securities

Shenyin Wanguo Hongyuan Securities

Haitong Securities

China Galaxy Securities

China Pacific Insurance

Great Wall Insurance Company

China Life Insurance Company

Customers in the government industry include:

Shanghai Municipal Finance Bureau

Shanghai Municipal Audit Bureau

Shanghai Statistics Bureau

Shanghai Justice Bureau

Shanghai Police Station

Shanghai custom

Shanghai Pudong Education Bureau

Shanghai Personnel Bureau

Shanghai Human Resources and Social Security Bureau

Shanghai Water Affairs Bureau

Shanghai Education Commission

Shanghai Press and Publication Bureau

Zhejiang Provincial Department of Finance

Zhejiang Provincial Civil Affairs Department

Zhejiang Provincial Department of Justice

Zhejiang Provincial Public Security Department

Zhejiang Provincial Department of Education

Zhejiang Provincial Department of Human Resources and Social Security

Jiangsu Provincial Department of Finance

Jiangsu Provincial Department of Justice

Jiangsu Civil Affairs Department

Jiangsu Provincial Public Security Department

Jiangsu Provincial Department of Human Resources and Social Security

Jiangsu Provincial Department of Education

Customers in the education industry include:

Shanghai University of Finance and Economics

Shanghai Jiaotong University

Shanghai University

Shanghai University of Technology

Shanghai Tongji University

Customers in the medical industry include:

Shanghai Health Committee

Shanghai Renji Hospital

Shanghai Huashan Hospital

on Heruijin Hospital

Shanghai Oriental Hospital

Shanghai Changhai Hospital

Automobile manufacturing, glass manufacturing, steel manufacturing, food manufacturing, paper manufacturing, electrical appliance manufacturing, consumer goods manufacturing, industry customers are:

Shanghai Automobile Group

Shanghai Volkswagen

Shanghai General Motors

Dongfeng Motor

JAC

Shanghai Baowu Iron and Steel Group

Shanghai Bright Food Group

Shanghai Tobacco Group

Shanghai Pharmaceutical Group

Shanghai Electric Group

Shanghai Jahwa

Procter & Gamble (China) Company

Midea Group

Wuhan Sibao Group

Nanjing Automobile Group

Tianjin Tiens Group

Chongqing Changan Automobile

Chongqing Changan Suzuki Motor

Jiangxi Isuzu Motors

Shanghai Yaopi Glass

Shanghai Waigaoqiao Shipbuilding

Jinan Iron and Steel Group

Tsing Tao beer

China National Textile Group

Vinda Paper

Shanghai Feike Electric

Shaanxi Heavy Truck

Shanghai Chenguang Stationery

Shanghai Hitachi Electric

Shanghai Guansheng Garden

Customers in the telecommunications industry include:

Zhejiang Mobile Communication Company

Shanghai Mobile Communications Corporation

Shanghai Telecom

Jiangsu Mobile Communications Corporation

Sichuan Mobile Communication Company

Responsibility description: In the above projects, I acted as the CEO / CTO of China CEO to coordinate the completion of the whole project.

2012/01-2017/01 My signed customers include:

Affiliated company: employed by Microsoft Software System Co. Ltd. in the United States

Project description: We provide the following customers with consulting of the best and most advanced IT system solutions for Oracle, SAP, Microsoft, IBM, HP, DELL, EMC, Cisco, Salesforce, + system implementation + system daily maintenance + Customized development and other value-added services. . .

Banking, securities, insurance, and industry customers include:

Bank of America

Citibank

HSBC

Deutsche Bank

Royal Bank of Scotland

Shanghai Pudong Development Bank

Shanghai Bank of Communications

China UnionPay

Shanghai Bank

Industrial Bank

Shanghai Rural Commercial Bank

China Minsheng Bank

China Everbright Bank

Qingdao Bank

Bank of Ningbo

Bank of Chongqing

Bank of Nanjing

Guangdong Development Bank

Shenzhen Development Bank

Xiamen City Commercial Bank

Bank of Suzhou

Bank of Jiangsu

Shanghai Securities

Oriental Securities

Everbright Securities

Aijian Securities

GF Securities

Shenyin Wanguo Hongyuan Securities

Haitong Securities

China Galaxy Securities

China Pacific Insurance

Great Wall Insurance Company

China Life Insurance Company

Customers in the government industry include:

Shanghai Municipal Finance Bureau

Shanghai Municipal Audit Bureau

Shanghai Statistics Bureau

Shanghai Justice Bureau

Shanghai Police Station

Shanghai custom

Shanghai Pudong Education Bureau

Shanghai Personnel Bureau

Shanghai Human Resources and Social Security Bureau

Shanghai Water Affairs Bureau

Shanghai Education Commission

Shanghai Press and Publication Bureau

Zhejiang Provincial Department of Finance

Zhejiang Provincial Civil Affairs Department

Zhejiang Provincial Department of Justice

Zhejiang Provincial Public Security Department

Zhejiang Provincial Department of Education

Zhejiang Provincial Department of Human Resources and Social Security

Jiangsu Provincial Department of Finance

Jiangsu Provincial Department of Justice

Jiangsu Civil Affairs Department

Jiangsu Provincial Public Security Department

Jiangsu Provincial Department of Human Resources and Social Security

Jiangsu Provincial Department of Education

Customers in the education industry include:

Shanghai University of Finance and Economics

Shanghai Jiaotong University

Shanghai University

Shanghai University of Technology

Shanghai Tongji University

Customers in the medical industry include:

Shanghai Health Committee

Shanghai Renji Hospital

Shanghai Huashan Hospital

Shanghai Ruijin Hospital

Shanghai Oriental Hospital

Shanghai Changhai Hospital

Automobile manufacturing, glass manufacturing, steel manufacturing, food manufacturing, paper manufacturing, electrical appliance manufacturing, consumer goods manufacturing, industry customers are:

Shanghai Automobile Group

Shanghai Volkswagen

Shanghai General Motors

Dongfeng Motor

JAC

Shanghai Baowu Iron and Steel Group

Shanghai Bright Food Group

Shanghai Tobacco Group

Shanghai Pharmaceutical Group

Shanghai Electric Group

Shanghai Jahwa

Procter & Gamble (China) Company

Midea Group

Wuhan Sibao Group

Nanjing Automobile Group

Tianjin Tiens Group

Chongqing Changan Automobile

Chongqing Changan Suzuki Motor

Jiangxi Isuzu Motors

Shanghai Yaopi Glass

Shanghai Waigaoqiao Shipbuilding

Jinan Iron and Steel Group

Tsing Tao beer

China National Textile Group

Vinda Paper

Shanghai Feike Electric

Shaanxi Heavy Truck

Shanghai Chenguang Stationery

Shanghai Hitachi Electric

Shanghai Guansheng Garden

Customers in the telecommunications industry include:

Zhejiang Mobile Communication Company

Shanghai Mobile Communications Corporation

Shanghai Telecom

Jiangsu Mobile Communications Corporation

Sichuan Mobile Communication Company

Responsibility description: In the above projects, I acted as the CTO / CEO of the CEO of China to coordinate the completion of the entire project.

Work Experience Project Experience Academic Research Experience Achievements *

2012-2017 Microsoft development team software and hardware development engineer Google development team PM Cambridge University during his study

2017 / 1–present Dr. The University of Cambridge Cambridge, Professor OXBridge

Leverhulme Centre for the Future of Intelligence, LCFI http://www.lcfi.ac.uk/ Chief Scientist, Top Research Institute, LCFI Laboratory, UK

Currently NTT and Alibaba Group Haniuqiao Intelligent Technology CEO / CTO, Director of PM Department, Researcher of Chinese Academy of Sciences, Full Member of JSAI Artificial Intelligence Society, IEEE Member, CAAI China

Member of the Artificial Intelligence Society, AAA1 International Artificial Intelligence Society. He is a member of ACM Commissioner for Artificial Intelligence China ACM. His main research areas include ERP consulting, cloud technology, big data, blockchain, artificial intelligence experts, computer vision, multimedia technology and machine learning.

Professor of machine learning at Tsinghua University. Integration with technology megatrends, cutting-edge analytical technology, government / local government, education / medical / healthcare, finance, manufacturing, logistics, communications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals for agriculture, Retail, manufacturing, transportation, sports, aerospace, advertising,

Internet of Things, ICT and other industries. Visiting Professor of Harvard Business School, Visiting Professor of Computer Science of Tsinghua University, Visiting Professor of University of Tokyo, Visiting Professor of University of Tokyo, Visiting Professor of Osaka University, Jiangsu Provincial Government of China Association for Science and Technology

The competition category A sponsored by the visiting professor of Kyoto University won the first prize of the big health industry: biomedicine, medical equipment, smart medicine, health care, etc. Won the best award in category A. ERP blockchain cloud technology big data artificial intelligence related fields (not limited to the field of speech processing including various fields of artificial intelligence) won the highest award in patent product competitions, Ali, Tencent, Huawei and other contracts and reached many contracts. AIx 5G has become the mainstream payment method, WYSIWYG, short video AI animation x 5G, AR VR and 3D, smart driving, finance, 5G telemedicine 5G AI medicine, smart driving, smart business, smart business, smart medical , 5G materials for public security 5G robots, semiconductors, sports, entertainment and other technologies are our mainstream technologies, separation and purification, innovative drugs, biotechnology,

More than 10 years of experience in deep learning academic engineering projects. Familiar with artificial intelligence related algorithms and theories, especially neural networks, deep learning, reinforcement learning, and transfer learning. Familiar with data analysis tools such as SAS, R, Python, Spark SQL, Spark ML And language, with LR / GMM / SVM / CRF / MaxEnt / HMM / LDA / DNN / CNN / RNN research background;

◎ Proficient in CUDA programming, Python / Matlab / C ++ and other core technologies that use independent intellectual property rights such as deep learning architecture, machine vision, bio-intelligence recognition and other artificial intelligence algorithms, media-free payment and other core technologies. , AI chips, smart retail, smart cities, smart security, smart education, aerospace Japanese asteroids, military industry and other fields have in-depth layout, ranking the industry leader;

◎ Artificial intelligence is used in manufacturing (robot, optimization) transportation, biological health care, communication, machine learning simulation, edge equipment, network, high-performance computing, product development (general), product development (visual inspection solutions), human-computer interaction (HCI, HRI)

Important project experience

◎ Bio-healthcare industry-working with excellent teams such as Google, Microsoft, Tencent and Amazon, I work with Harvard Medical School Gene, Kyoto University Nobel Medical Gene iPS Stem Cell Team

◎ Cooperate with FANUC, PFN (PREFERRED NETWORKS) and Hitachi. Research progress of bio-health medical image analysis and early diagnosis technology of blood cancer. National Cancer Research Center and other joint research. Blockchain technology and artificial intelligence biomedical AI are used in various fields, including medical image analysis, detailed research on electronic health records (EHR), and basic research such as finding the source of disease, pre-clinical drug discovery and clinical trials.

Project experience

◎ Participated in the Amazon Artificial Intelligence Project (Amazon Go): An experimental grocery store in Seattle was successfully implemented, which allows shoppers to pick up goods on the shelves, leave the cash register, or stop at the self-checkout kiosk. The computer visually recognizes them as they enter the store, and then links them to the products removed from the shelves. When the customer leaves, the system deducts the cost of the shopping bag from their Amazon account and sends an email receipt.

◎ Participated in the German e-commerce company Otto: Using deep learning models to analyze billions of transactions and predict what customers are most inclined to buy before placing orders, reducing surplus inventory by 20% and reducing production by more than two million annually. The accuracy of the products predicted by this system in the next 30 days will reach 90%;

◎ Carrefour artificial intelligence project of French global retailer, and Target artificial intelligence of the United States. The project is based on insightful sales, including personalized promotions, classified optimization, and customized displays.

◎ In cooperation with Fujitsu, the artificial intelligence project fingerprint authentication consumer system is applied to Japanese supermarkets, department stores, etc., Japan's Rakuten artificial intelligence service automatic question and answer project

◎ The Japanese construction company Shimizu Construction Artificial Intelligence Project: My Patent-Intelligent Construction (NASA) Project in Extreme Environment

◎ Komatsu's multi-purpose drone and bulldozer automation system

◎ HRP-5P robot development project developed by Japan National Institute of Advanced Industrial Science and Technology (AIST)

◎ Jingdong's whole process unmanned warehouse transportation robot project

◎ Development of Kawasaki Heavy Industries Robot, arc welding robot ARCMAN ™ -GS from Kobe Steel Co., Ltd.

◎ Omron Robot Project

◎ Toyota's "Human Body Support Robot" (HSR) project

◎ Sharp's Robohon project

◎ Cooperate with researchers at MIT Media Lab in the US

◎ Alibaba's rookie network project, developed the Peitong robot small G

◎ China Construction Technology Building Intelligent Construction Platform, which integrates BIM + Internet + IoT technology, can realize all-round and interactive information transmission;

◎ TRASCO Nakayama Corporation TRUSCO NAKAYAMA CORPORATION Robot IOT Automated Logistics and SAPHANA * SAPRA were introduced. Japanese TV station Takuya れ Japanese TV program was announced

◎ NAGANO KEIKI CO., LTD.'S IOT project is used to test the swallowing ability of the tongue for medical products to avoid the risk of elderly people and other people getting stuck by food. Also use fiber optic cables to detect the aging rate of roads. Use fiber optic cables For example, the vibration intensity generated by the truck driving across the road speculates on the aging of the traffic road and the resistance rate of the bridge

Japanese TV station Nippon TV program released

◎ MonotaRO Co., Ltd. artificial intelligence project AI customer service system and commodity AI analysis system

ERP, SAP and other directions

◎ About 8 years of ERP consultant implementation experience; have SAP (FI / CO, MM, SD, PP, BW, BI SAP HANA) and other module experience, have SAP R / 3 (Basis), SAP R / 3 (ASAP), SAP R / 3 (in stock / purchase management), SAP R / 3 (management accounting), SAP R / 3 (financial management), SAP R / 3 (production management), SAP R / 3 BI certification consultant

Important project experience

Japan Toyota Motor SAP import project

Project introduction: AI machine learning, especially deep learning and other artificial intelligence fields in the automotive industry

◎ Cloud technology big data blockchain is proficient in AI artificial intelligence, deep learning, machine learning methods and applications in the field of artificial intelligence; natural language processing automatic question and answer, man-machine dialogue system, sentiment analysis, social data mining technology expert experience more than 15 years;

◎ Artificial Intelligence / Intelligent Driving / AI + Fintech & Blockchain / Future Medical / Network Security AR / VR Robot Developer Smart Hardware / Internet of Things / GAIR Experience;

◎ More than 10 years of embedded software development experience;

◎ Embedded software development (Linux / SCM / PLC / DSP ...) more than 10 years;

◎ Cooperate with Cambridge University in computer science in the fields of computer vision, machine learning, artificial intelligence, data mining, information retrieval, natural language processing, speech recognition, etc .;

◎ Cooperate with artificial intelligence experts Google, Microsoft, Amazon, facebook, netflix, Apple, Japan Toyota and other projects;

◎ Participated in the design and development of artificial intelligence systems and applied Deep Learning technology to model and analyze rich media data such as voice, video, images, and text, to provide relevant products with intelligent support and promote business development;

◎ Responsible for cutting-edge algorithms and framework tracking in the field of Deep Learning, building a large-scale Deep Learning computing platform for enterprise applications, familiar with the use scenarios and methods of typical deep learning models such as CNN;

◎ Familiar with common network structures such as ResNet and MobileNet, and have practical experience in image classification, image segmentation, object recognition and other related fields;

◎ Familiar with one or more of the mainstream deep learning frameworks such as TensorFlow, Caffe, MXNet; some papers published in related fields such as ICIP, ICCV, CVPR, ECCV, SIGGRAPH, etc. have more than 10 years of experience in cloud computing and cloud server management

◎ Familiar with Alibaba Cloud, AWS, Azure, GCP, etc .;

◎ Familiar with C / C ++ / Python / Java Familiar with computer network related knowledge, and have solid practical experience in crawler projects, crawler architecture tools such as scrapy, selenium, beautiful soup understand;

◎ Familiar with the Hadoop ecosystem and other big data technologies, such as Spark, HDFS, Hive, Impala, ElasticSearch, Cassandra, Kafka, etc. 10 years of experience in natural language processing (NLP) tools and components;

◎ Familiar with open source tools such as Hadoop, Spark, Caffe, Tensorflow, etc. Have practical development experience Proficient in Java / Python / C ++ Familiar with network programming, multi-threading, distributed (Hadoop / Hive / Storm) more than 10 years of practical experience.

◎ With financial risk control, Internet anti-cheat, image and text processing, precision marketing, recommendation system and other related product work experience, artificial intelligence and big data driverless, deep reinforcement learning, natural language processing text-based image synthesis The project will focus on artificial intelligence,

Big data and AR / VR, ADAS, mobile phones, robots, IOT, aerospace and other scientific and technological fields

◎ Artificial intelligence is driven by three major factors: data, algorithm and computing power. Through the massive data accumulated, supported by high-performance chips such as GPU,

Deep learning can mine the value of data, obtain algorithms that exceed human recognition accuracy, and then realize the commercial application of deep learning, so that artificial intelligence is no longer limited to academic research.

◎ 25 years of global leading companies in artificial intelligence, such as computer vision, machine learning, data mining, information retrieval, natural language processing, speech recognition / synthesis, machine learning, computer vision, natural language processing, machine translation, speech recognition / synthesis work experience;

◎ Proficient in programming languages, Java, C / C ++, C #, Python, etc .; in top computer science conferences and journals such as NIPS, ICML, COLT,

Published papers in CVPR, ICCV, ECCV, IJCAI, AAAI, UAI, KDD, SIGIR, WWW, ACL, PAMI, IJCV, JMLR, AIJ, etc.

◎ More than 10 years of experience in deep learning academic engineering projects. Familiar with artificial intelligence related algorithms and theories, especially neural networks, deep learning, reinforcement learning and transfer learning. Familiar with data analysis such as SAS, R, Python, Spark SQL, Spark ML Tools and language, with LR / GMM / SVM / CRF / MaxEnt / HMM / LDA / DNN / CNN / RNN research background;

◎ Proficient in CUDA programming, Python / Matlab / C ++ and other core technologies that use independent intellectual property rights such as deep learning architecture, machine vision, bio-intelligence recognition and other artificial intelligence algorithms, media-free payment and other core technologies. , AI chips, smart retail, smart cities, smart security, smart education, aerospace Japanese asteroids, military industry and other fields have in-depth layout, ranking the industry leader;

◎ Artificial intelligence is used in manufacturing (robot, optimization) transportation, biological health care, communication, machine learning simulation, edge equipment, network, high-performance computing, product development (general), product development (visual inspection solutions), human-computer interaction (HCI, HRI)

Important project experience

◎ Bio-healthcare industry-working with excellent teams such as Google, Microsoft, Tencent and Amazon, I work with Harvard Medical School Gene, Kyoto University Nobel Medical Gene iPS Stem Cell Team

2017/1 – present AlibabaGroup contract unit Haniuqiao Intelligent Technology Guolong Deputy General Manager CTO

Professor of computer vision, multimedia technology, machine learning, Tsinghua University. Integration with technological megatrends, leading analytical techniques are used in government / local government, education / medical / healthcare, finance, manufacturing, logistics, communications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals , Agriculture, retail, manufacturing, transportation, sports, aerospace, advertising, IOT, ICT and other industries.

AI Reporter Sports Intelligent Driving AI Accounting Attorney AI Surgical Robot AI Interrogation Misdiagnosis No AI Robot Funeral VR AR 3D AI Crime Tracking AI Image Recognition. China Science and Technology Association, Jiangsu Province The first prize in the category A was won in the contest sponsored by the People's Government of Zhejiang Province. Great health industry: Biomedicine, medical equipment, smart medical care, health management, etc. won the first prize in category A.

The main research areas are computer vision, big data blockchain, and natural language processing. Chief Technical Expert of Alibaba Group

Rugby 5G technology invented by AI deep learning (one of the artificial intelligence (AI) technologies used for image recognition and speech recognition) has developed a rugby game analysis system that combines deep learning, uses a camera to capture human movement, and measures the body of the AI ​​as it passes Posture and movement, AI guides 5G technology

◎ Familiar with C / C ++ and other languages, Matlab, Qt, ROS and other development tools, Linux or QNX development environment machine learning / data mining and other AI-related algorithm research and development; algorithm-related code library, tool library packaging and release; AI-related algorithms Performance optimization, engineering environment deployment; participate in building and implementing distributed deep learning clusters;

◎ Proficient in theoretical knowledge and practical skills related to machine learning; familiar with the use scenarios and methods of typical deep learning models such as CNN, RNN, LSTM, etc .;

◎ Familiar with many kinds of mainstream deep learning frameworks such as TensorFlow, Caffe, MXNet; has solid math and programming skills. Familiar with basic machine learning algorithms and understand the application model of machine learning in typical industries. Have development experience and be familiar with big data related knowledge;

◎ Familiar with the data flow and processing methods in the customer application system, with the ability to configure basic components such as the big data platform HD / Hbase / Hive. Ability to independently analyze customer needs, design solutions, and have complete project implementation experience;

◎ Good at communication, capable of coordinating and solving various problems encountered in teamwork and external cooperation, and responsible for the back-end development and maintenance of company-related business, products and services.

Important project experience

Participated in banking, healthcare, insurance, financial technology, manufacturing, retail, marketing, sports analysis and other projects

◎ The artificial intelligence (AI) of companies such as Takeda Pharmaceutical Industries, Fujifilm and Shiono Pharmaceuticals in Japan promotes new drug development projects

Medical DNA cell IPS artificial intelligence project-Osaka Hospital and other major hospitals in the United States have implemented IBM Watson artificial intelligence project involving the development of artificial intelligence, smart sensors, telescopes, detectors and medical equipment. Artificial intelligence will make our language become Mental health window; advanced image sensors will enable humans to prepare super vision; super magnifying glass will enable humans to understand the infinite details of the earth; all medical laboratory systems will be integrated into a single computer chip; intelligent sensors will detect environmental pollution at the speed of light.

◎ AI case Intel artificial intelligence project: Intel promotes its commitment to open source through optimized machine learning frameworks and libraries, and cooperates with Nervana system machine learning experts;

◎ American Google artificial intelligence project: language translation, visual processing, artificial intelligence development of ranking and prediction capabilities

◎ AI case Salesforce artificial intelligence project team: Salesforc uses artificial intelligence to help employees perform tasks more efficiently and simplify and accelerate their work efficiency;

◎ AI case Amazon Amazon ALexa artificial intelligence project and init artificial intelligence project:

Development of Amazon's artificial intelligence service robot Alexa, participating in deep learning based on the AWS cloud platform Amazon Sagemaker framework applied to financial software US INTUIT

◎ Also participated in the Kobe Daimaru Department of Artificial Intelligence project: successfully implemented Kobe Daimaru, Japan's first department store to introduce an AI customer service system. Daimaru Department Store in Motomachi, Kobe, Japan, hired two conversational AI girls "MOTOMAQI" in June this year, and provided special services during festivals such as Zhongyuan, Japan, and the end of the year, which specializes in giving gifts to family and friends.

◎ I also participated in the Dai Rong Supermarket, and some beauty shops in the cosmetics shop began to use "AI Sakura", because "she" can not only answer all the customers' questions in time, but also confirm everyone's age, muscle quality, skin color, questions Wait, and then find the products of the same customer group from the own database, and propose cosmetics suitable for different women.

◎ AI multi-element marking construction site project: The application of AI in the construction site reflects that it can use machine learning, voice and image recognition to automatically mark the photos and videos on the construction site to organize data and search. For example, AI can use deep learning to analyze images and voice to automatically tag construction data and proactively provide customers with safety measures. AI facilitates design, construction, and operation and maintenance. This is also in line with the life cycle of the building: design, construction, management.

◎ Ali project artificial intelligence chip software development, Japan AEON Group AI case, participation in Yoshinoya AI robot project, financial credit financing AI fraud prevention, repayment dunning AI system, Nomura Securities Consumer Finance core artificial intelligence project, supermarket anti-theft AI detection System, Japan Police Agency Face Recognition AI Catching Prisoner System, Japan AI Transportation System, Japan AI Parking Automatic System and ETC Automatic Billing System ETC Discount Billing System, Sports Training AI System, VR / AR System, HCI, HRI Systems, manufacturing IOT systems, visual inspection solutions, participating in Nomura Securities ’use of AI for the collection and analysis of various information, and the Ministry of Land, Infrastructure, Transport and Tourism using the AI ​​system for analysis. In order to alleviate the traffic congestion problems caused by tourism, Tokyo 2020 was successfully developed Hosted the Olympic AI customer service system.

Artificial intelligence, big data, cloud and other directions

◎ More than 10 years of work experience in big data model construction, familiar with neural networks, deep learning principles, and can use commonly used big data analysis platforms and tools (python, R, SAS); relevant work experience for Internet user data acquisition, for Internet data Professional research on risk control and modeling technology;

◎ Cloud technology big data blockchain is proficient in AI artificial intelligence, deep learning, machine learning methods and applications in the field of artificial intelligence; natural language processing automatic question and answer, man-machine dialogue system, sentiment analysis, social data mining technology expert experience more than 15 years;

1. Fully responsible for the project team construction, establish a united and efficient team, and stimulate the enthusiasm of the work (from 10 to 50 people) responsible for project progress, quality, cost range management and control, technical guidance and training;

2. Recognized by TOYODA customers and NTTDATA (excellent project team and outstanding project manager personal award);

3. Cooperation with MIT, Harvard, Tokyo University and other universities, Mitsubishi, NTTDATA Financial Research Institute, Bank of Japan financial institutions and government and other relevant institutions; responsible for the formation of a deep artificial intelligence theoretical foundation team;

4. Research direction: multimedia technology, computer vision, natural language learning, etc.

Important project experience

Unmanned aspect

1. 2012 / 08-present Toyota Japan's smart car autonomous driving technology and Japan's softbank artificial intelligence technology implementation project

2. Cooperate with NVIDIA's artificial intelligence (AI) chip autonomous driving team

3. Cooperate with the University of Cambridge Laboratory in the United Kingdom, Germany Mercedes-Benz Automotive Artificial Intelligence Project

4. Autonomous driving technology / R & D projects of Mazda Motor Company

5. Participate in the research and development of self-driving projects such as Baidu, Momenta, Tucson, etc.

6. Tianjin FAW Introduced Industrial Intelligence Project

7. Cooperate with IBM AI technical team to import Mitsubishi, Honda, Suzuki, etc.

8. The field of comprehensive control system development

◎ Electronic platform technology / development / design: vehicle electrical system development and architecture design, in-vehicle ECU hardware development (function / manufacturing requirement design)

◎ Infotainment · UI area / early product development: development of automotive infotainment system (new generation of automotive connection system field), etc.

◎ ADAS, vehicle field / early product development: vehicle safety control development, vehicle control ECU hardware, software development, etc.

◎ MBD support / operating system / development: simulation supports model-based automotive development (including HEV and EV), designing and developing operating systems, etc.

◎ Autonomous driving technology / R & D: image analysis, machine learning (DL / reinforcement learning), image and distance sensor signal processing, new map development

◎ Human-oriented research field / existing technology development: hypothesis construction of human characteristics, verification of experimental plans, construction of measurement methods, etc.

9. The field of powertrain development

◎ Inverter / control development of EV / PHEV: hydraulic control design / experimental research of next-generation automatic transmission

◎ Driving · Environmental performance / development: driving force control development and operation experiments / measurement work, etc.

◎ Engine · xEV / control development: design of engine electronic control system and control model or software / hardware development and design, etc.

10. Autonomous driving technology

◎ Familiar with car-level embedded architecture, familiar with artificial intelligence technologies such as pattern recognition, machine vision, deep learning, path search;

◎ Familiar with C / C ++, Python language, Matlab, Qt, ROS and other development tools, Linux or QNX development environment has the experience of automatic driving system product development as the main person in charge;

◎ Familiar with the type and accuracy requirements of high-precision map elements suitable for autonomous driving, master the algorithms of laser point cloud filtering, map feature extraction, map construction, etc. Familiar with C / C ++ and other languages, OpenCL, PCL and other development tools, Linux or QNX development environment Basic principles such as satellite navigation, differential positioning, inertial navigation, etc., skilled use of mainstream integrated navigation systems, mastering algorithms related to trajectory estimation of moving objects;

◎ Familiar with C / C ++ and other languages, Matlab, Qt, ROS and other development tools, Linux or QNX development;

◎ Responsible for the selection, deployment calibration and testing of the integrated navigation system at the vehicle and base stations, responsible for the data analysis of the integrated navigation system, the development and testing of vehicle trajectory estimation algorithms, navigation and positioning fusion algorithms, at least 12 years in the field of autonomous driving and ADAS Above work experience, have product development experience such as LKS, ACC;

◎ Familiar with the working mechanism of vehicle steering, driving, braking and suspension systems, master the common vehicle motion control algorithms such as preview tracking, PID, fuzzy control, MPC, etc., and can optimize the algorithm design according to the vehicle dynamics;

◎ Familiar with C / C ++ and other languages, Matlab, Prescan, CodeWarrior and other development tools are responsible for the development and testing of longitudinal and lateral motion control algorithms for autonomous vehicles;

2017/1-present CTO, Senior Technical Expert, AI Solutions

The first technical expert of artificial intelligence / machine learning solutions in the Chinese technical support team, as a technology leader to drive AI projects in the eastern and southern markets of China

• Provide technical support for pre-sales and after-sales of Edge and Data Center AI solutions.

• Proof of concept and target reference design and scheme optimization as needed

• Provide technical training on artificial intelligence solutions

• Bridge between customers and R & D department to speed up project implementation

• Cooperate with R & D department to improve artificial intelligence solutions (feedback, defect report, internal testing, etc.)

• Establish and maintain internal AI / ML server (Docker environment)

Senior Engineer of Product Application

• Is the only engineer in the global embedded tool method deployment team in China, responsible for the team's related work in China, including new technology promotion, training and technical support for key projects and key issues.

• Focus on providing customers with embedded products (FPGA SoC) solutions. Provide software tools, methods and technical solution design services (PoC) for customers and internal teams.

• Provide product technical feedback to the R & D team to help improve tools and product solutions.

• Creative work, through PoC design, off-site and on-site support, directly provide customers with a wide range of system-level issues to fill the technical gap between key project development and on-site team

• The scope covers the full software stack of xilinx embedded products, as well as hardware and software co-design with FPGA on SOC.

The key points of the project are listed as follows:

• Help Tier1 customers deploy Linux system solutions. Guide customers to migrate Xilinx Petalinux process to Yocto process. Clear key issues including uboot, linux, and rootfs, and reach customers' urgent project progress goals in a timely manner.

• Customize PS only Reset and PL overlay design.

• RT Linux deployment and performance evaluation on EVB.

• Completed the reference design of AMP system deployment based on Xen virtualization

• Successful deployment of OpenWRT and third-party USB WiFi on EVB.

• Performance evaluation and optimization of multiple network ports.

• Independent development of DPDK PMD (driver) design and test of custom DMA IP in smartNIC project based on arm platform is the first arm platform implementation within the company.

• Independently complete the transplantation of Linux customized applications to FreeRTOS.

• Completed technical training and support for multiple FPGA HLS acceleration solutions

• Assist local technical support team to complete FFmpeg, GStreamer, XfOpenCV & HLS technology pre-research and PoC implementation in multiple projects to accelerate the project landing.

• Creative use of Docker technology to complete the deployment of tools to customer environments

• Creative use of Docker technology to complete the deployment of the company's data center FPGA machine learning solution

Qualcomm Project Senior Linux Software Engineer

Participate in the development and maintenance of in-vehicle Linux / Android BSP

• Ethernet (MAC / PHY) module bring up and verify, drive and test program development, performance optimization.

• System network configuration based on systemd.

• Transplant Open-AVB stack and participate in AVB Demo development and AVB test environment construction.

• Development and maintenance of BSP upgrade mass production function modules.

• SPI NOR driver development of Uboot and Linux.

• Lead customer technology of a BSP project, assist customers to customize some functions (recovery, MTP, FM / RDS, etc.)

• Deploy LTP to BSP.

• Design and maintenance of mass production and upgrade and recovery solutions of BSP

• Assistance management Technical support for managing BSP

Semiconductor project (Linux driver engineer

• USB3.0 controller (xHCI) driver development and maintenance.

• Provide technical support (xHCI / ACPI) for Linux OS Vendor (Ubuntu / Suse / Redhat).

• Release Chipset driver and kernel RPM, DEB installation package.

• Submit xHCI / EHCI / ACPI related drivers to linux upstream

Huawei project linux driver engineer

Responsible for the development and maintenance of the linux driver and related application programs of the company's PCI & USB digital / analog TV card.

• Development and maintenance of linux driver for USB TV receiving card. The driver also supports multiple hardware solutions, covering analog and digital TV formats such as NTSC / PAL / ATSC / DTMB.

• Development and maintenance of linux driver for PCI TV receiving card. The driver also supports a variety of hardware solutions, covering analog and digital TV systems such as NTSC / ATSC. It also includes a sound card driver module based on ALSA architecture.

• I2C EVK (evaluation board) linux driver development and maintenance. This board is used to debug and verify the set-top box reference solution.

• Linux platform I2C Controller application development. This tool is used in conjunction with I2C EVK driver for real-time debugging and verification of its hardware. Responsible for QT development GUI and low-level interface for interaction with drivers.

linux software engineer

• Assist and participate in employee technical training, including Linux Device Driver & Kernel Training, Linux Kernel Training, QA Training, familiar with the company's software development documentation and quality control process.

-Modify Linux Task Scheduler

-Implement memory leak detection tool

• Develop AC97 linux sound driver based on ALSA architecture for PXA270 embedded development board.

Automotive Technology Project

Position: Senior Software Engineer

Job responsibilities:

 Responsible for Xiaopeng self-developed super charging pile (ARM + Linux) software architecture;

 Responsible for software development under the new architecture (basic library, driver layer, platform layer, application layer);

 Focused on the completion of the code writing of the basic library, driver layer and platform layer.

Guangdian Express Financial Project

Customer Profile: Guangdian Express is the world's leading provider of currency processing equipment and system solutions, and the largest in China

Largest ATM (Automated Teller Machine) product and system solution provider, the most powerful AFC

(Automatic ticket inspection system) Provider of equipment and core modules is also the most professional financial service provider in China

The contractor and the most powerful cash intelligent processing expert.

Position held: senior engineer / director (embedded software direction)

Job responsibilities:

 Responsible for the drive development and debugging of the hardware platform of the banknote recognition module and the development of the recognition scheduling process;

 New technology and new sensor drive to achieve debugging and data collection;

 I2C, SPI, UART, PCIE, GPIO, USB, Ethernet, camera, WIFI, FPGA, CIS, magnetic sensor, etc. Driver development and corresponding host computer test software development;

 ARM bare core and DSP platform hardware performance optimization work. 5. The drive software architecture and optimization of the hardware platform, UT and ST work.

HKUST Xunfei Project

Customer Profile: HKUST Xunfei Technology is a leading professional audio and video equipment provider. The company's independent research and development, production and sales

It sells professional audio-visual equipment mainly based on the front end of digital TV. Wired, wireless, satellite and other traditional electricity

Video and network companies provide one-stop end-to-end solutions, as well as new media such as OTT and IPTV

Service providers provide excellent system services.

Position: Senior Software Engineer

Job responsibilities:

 ARM Linux driver and uboot implementation of xilinx zynq platform;

 Complete the design of standard definition and high-definition video coding scheme based on Magnum coding chip, and design of multi-audio scheme;

 Complete all UT, ST, BBIT work in accordance with the IPD-CMM process, complete all code writing, UT / ST testing and joint debugging.

Huawei Technology Co., Ltd. Hangzhou Institute Project

Position held: software engineer

Job responsibilities:

 Independently complete Vxworks, Linux, DSP module driver, and module design documents to guide subsequent development and maintenance;

 Complete the coding and test case design of the module according to the IPD-CMM process;

 Complete UT, ST, BBIT work according to IPD-CMM process;

 Able to participate in difficult problems of the project and play a leading role in organizing and participating in special testing and code review.

Super charging pile software architecture + hardware platform development

Project Introduction: Charging pile architecture and development based on ARM + Linux platform.

Position: Senior Software Engineer

Job responsibilities:

 Design of layered architecture of charging pile software, output of software architecture documents, development of AICPU firmware system framework, computing channels, DFX research and development, commercial delivery;

 Coding of driver layer software (driving I2C, SPI, GPIO, USB, PWM, CAN, ADC, WIFI, 4G);

 Basic library coding work (tasks, timers, message queues, shared memory, application framework, configuration file analysis and other modules);

 Part of the software development work under the old architecture (network node selection, OTA upgrade, application guard, etc.).

work performance:

 Completed the layered architecture under the platform, with clear division of labor in each layer, which is convenient for subsequent expansion and maintenance;

 Completed the preparation of the basic library and hardware driver layer, and the adaptation of the platform layer;

 Defined the development framework and template implemented by each layer, and the subsequent new hardware drivers can be added according to the template implementation method, which makes it easier to iterate on new functions.

ARM platform banknote recognition module development

Project Introduction: Based on Altera Cyclone SOC, TI C6654 and Allwinner V5 ARM chip platform hardware driver and

Platform development.

Position: Head of Product Engineering Group

Job responsibilities:

 TI C6455 platform upgrade to ARM platform software verification work;

 Responsible for software evaluation, scheme verification and drive design of new sensors and hardware platforms;

 Completed the software development of the ARM platform (Cyclone SOC + TI C665 / Allwinner V5) chip hardware driver and recognition platform;

 Complete the customization, cutting and adaptation of preloader, Uboot, Linux Kernel;

 Responsible for docking with the algorithm department for function integration work;

 The software development of the new hardware platform is completed, and it is responsible for the small batch verification and mass production introduction of the new platform.

work performance:

 The unified architecture of the ARM recognition platform software has been promoted and used on 5 movements, which can reduce the cost by 20% compared with the original module. The new software architecture follows the layering principle, making it easy to port other hardware platforms later.

TI C6455 / OMAP138 platform banknote recognition module development

Project introduction: Based on TI C6455 / OMAP138 banknote recognition module software development.

Position: Senior Software Engineer

Job responsibilities:

 OMAP138 platform upgrade TI C6455 platform hardware driver adaptation work;

 Complete the driver development of DSP platform (DDR2, SPI, I2C, UART, ADC, FPGA, CIS);

 Hardware acquisition and correction of various sensor (CIS, thickness, magnetic) images;

 CACHE performance optimization and software normalization work;

 Compile image acquisition and other host computer testing tools.

work performance:

 Guaranteeed the development and maintenance of the banknote identification module of the company's largest circulation model; completed the software development and production introduction of the multi-spectral new identification platform; promoted the platform normalization work, and completed the normalization of each model code of the C6455 platform; A unified control protocol has been formulated, and all subsequent models share a set of control protocols for easy maintenance; an external standard API interface for test tools has been written for integration into tools of different models.

Development of high-definition encoding video board for ARM platform

Project introduction: Based on Zynq platform ARM Linux driver and uboot implementation, the application software

Now.

Position: Senior Software Engineer

Job responsibilities:

 Complete UBOOT boot guide;

 Complete peripheral bus driver adaptation (PCI, SPI, I2C, FLASH, DDR);

 Completed the application software of Micron encoding chip (HD, SD and Logo insertion).

work performance:

 High-quality completed the required functions according to the plan, innovatively designed the upgrade and production scheme of Logo insertion, and then used as a platform machine application scheme. In the project development, we used the IPD-CMM process experience of Huawei's previous project to actively design ST use cases and complete the test. After the test, there were few software problems, which were paid attention by the leadership and promoted in the project team.

Vxworks + Linux PowerPC BSP development

Project Brief: Based on Vxworks6.8 + WindRiver Linux, complete Freescale P304 and P1012

Hardware board BSP development.

Position: Huawei second-level software engineer

Job responsibilities:

 Complete the driver development of the narrowband access chips BRI, E1 / T1 and the narrowband switching module under Vxworks and Linux;

 Complete Vxworks boot loading and Linux boot process development;

 Complete development of peripheral drivers such as small systems (CPU, DDR, FLASH, network card, operating system), dual BIOS, USB, CF card, UART, SPI, I2C, USB, FLASH, etc .;

 Complete DSP hardware driver development.

work performance:

 High-quality completion of the compilation and startup loading process, as well as the power-on startup debugging of the single board, the completion of the system's narrowband module design, after 2 versions, the module has been stabilized. During the development process, I mastered the Linux and Vxworks driver development and debugging methods, and developed a UT tool based on the shell to perform kernel module functions, and used it in the company's automated testing. As an MDE role, he is involved in the design of software systems. The DSP driver used by the voice media board is the Shannon DSP driver developed earlier. It instructed a colleague to maintain this part of the driver code and train a new employee, and successfully completed the regularization.

Ti multi-core DSP tms320c6678 development

Project Introduction: Based on the Ti BIOS operating system, the DSP driver and performance verification are implemented, and the DSP codec is completed

Evaluation of processing performance.

Position: Huawei first-class software engineer

Job responsibilities:

 Realize the module driver of DSP multi-core startup, clock, power supply, DDR3, narrowband access (TSIP), PCIE, HyperLink, network port, hardware RTP encryption;

 Realize the functional test of each module and meet the project acceptance standard.

work performance:

 Independently completed the project development, the new code is more than 10K, and the DSP is a new device in the company. The project results are used as company-level platform applications, supporting the commercialization of 4 hardware platforms.

DSP Media Gateway VOIP Development and Maintenance

Project Introduction: Develop a VOIP solution based on Ti DM648 chip + PowerPC to realize hardware for small and medium users

Platform; Maintain the original platform media gateway, the media gateway solution consists of: Ti DSP TMS32054X chip (implement DTMF, FSK, MFC, signal tone) + MNDSPEED MPC82610 chip (implement G711, G729, G723, RFC2833, RFC2198, T38 venue system) .

Position: Huawei Software Engineering

Job responsibilities:

 Completed the development plan of the voice media gateway DSP core scheduling in the development project, completed the DSP chip driver, PCI driver on PowerPC, DSP loading module, DSP aging and equipment module, and VOIP scheduling scheme on PowerPC;

 Mainly responsible for the maintenance of the platform and the development of new features in the maintenance project, timely locating online problems, quickly locating the root cause and giving solutions.

work performance:

 During the maintenance work, the difficult problems can be quickly resolved, the positioning efficiency is high, the problems are solved, and they have grown into technical backbones. At the same time, through maintenance, they are closer to users and more concerned about user needs, laying a good foundation for subsequent development work;

 In the development process, strictly follow the development process, complete the needs analysis and summary design, complete the coding and UT, ST work, and organize the code inspection in the work to ensure the development quality. At the same time, as the ability grows, as an interface person in the project, he is responsible for coordinating and solving problems with surrounding project teams.

Reference

JSAI Artificial Intelligence Society Regular Member https://www.ai-gakkai.or.jp/

CiNii Articles: http://ci.nii.ac.jp/

CiNii Books: http://ci.nii.ac.jp/books/

CiNii Dissertations: http://ci.nii.ac.jp/d/

https://kaken.nii.ac.jp/ja/

https://kaken.nii.ac.jp/ja/

Scientific Research Grants Support Business | Japan Society for the Promotion of Science

https://www-shinsei.jsps.go.jp/kaken/index.html

Scientific research grants help undertakings-Scientific research costs-: Ministry of Education, Culture, Sports, Science and Technology

http://www.mext.go.jp/a_menu/shinkou/hojyo/main5_a5.htm

Government Office of Science and UK House of Commons Science

The Technical Committee (The House of Commons ’Science and Technology Committee)

British Council on Artificial Intelligence

Open Data Institute (ODI)

Including the Alan Turing Institute and the EPSRC Association of Cambridge University, Edinburgh University, Oxford University, University of London, University of Warwick

https://www.caai.cn/Chinese Association for Artificial Intelligence

Member of AAA1 International Artificial Intelligence Association

Japan Deep Learning Association

work experience

2012-2017 Microsoft Google AI/ML Solution Specialist

2017/1 – Present

Dr. Guolong The University of Cambridge Cambridge

Leverhulme Centre for the Future of Intelligence, LCFI http://www.lcfi.ac.uk/Chief Scientist, Top Research Institute, LCFI Labs, UK

2017/1 – Present

AlibabaGroup contract unit HOC Intelligent Technology Guolong Deputy General Manager CTO

Professor of computer vision, multimedia technology, machine learning, Tsinghua University. Convergence with technology megatrends, leading analytics technologies are used in government / local government, education / medical / healthcare, finance, manufacturing, logistics, communications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals , Agriculture, retail, manufacturing, transportation, sports, aerospace, advertising, IOT, ICT and other industries.

AI reporter sports intelligent driving AI accounting lawyer AI surgical robot AI interrogation misdiagnosed AI robot funeral VR AR 3D AI crime tracking AI image recognition. China Science and Technology Association, Jiangsu Province, Zhejiang Provincial People's Government hosted the contest and won the first prize in category A. Big health industry: Biomedicine, medical equipment, smart medicine, health management, etc. won first prize in category A.

His main research fields are computer vision, big data blockchain, and natural language processing. Chief Technology Specialist of Alibaba Group

Rugby 5G technology invented by AI deep learning (one of the artificial intelligence (AI) technologies used for image recognition and speech recognition) Developed a rugby game analysis system that combines deep learning, shooting human movements with a camera, and measuring the body passing by AI Posture and movement, AI-guided 5G technology

He work closely with Prof. James Bailey. I have also visited National Institute of Informatics, Japan invited by Prof. Michael E. Houle, and RIKEN, Japan by Dr. Bo Han, Dr. Gang Niu and Prof. Masashi Sugiyama.

Research Interests:

• Machine Learning

  • Secure/Robust/Explainable machine learning

  • Adversarial machine learning

  • Weakly supervised learning

  • Reinforcement learning

• Deep Learning and Security

  • Adversarial attack/defense

  • Backdoor attack/defense

  • Generative adverarial networks

  • Applications: object recognition, image inpainting, object detection, video recognition, automatic speech recognition

• Artifical Intelligence

  • Medical AI

  • Virtual reality surgery

Professional Activities:

• PC Member:

  • ICML2020, IJCAI2020, ICLR2020, AAAI2020, KDD2019, NeurIPS2019.

• Journal Reviewer:

  • Pattern Recognition

  • Journal of Clinical Medicine

  • Knowledge and Information Systems

  • IEEE Robotics and Automation Letters

  • IEEE Transactions on Industrial Informatics

  • IEEE Transactions on Knowledge and Data Engineering

  • IEEE Transactions on Neural Networks and Learning Systems

  • IEEE Transactions on Pattern Analysis and Machine Intelligence

  • ACM Transactions on Multimedia Computing Communications and Applications

• Research Scientist in AI safety, DeepMind (2016-present)

· theoretical and experimental research on keeping advanced AI systems robust and beneficial

• Software Engineering Intern, Google (2015)

· developed and implemented machine learning algorithms for the Knowledge Graph

• Decision Support Engineering Intern, Google (2013)

· built statistical models of the impact of ads quality on click-through rate in R

• Teaching Fellow in computer, Cambridge University (2012-2013)

• Quantitative Analyst Intern, D.E.Shaw & Co (2012)

· developed and tested risk modeling algorithms using statistical and numerical optimization methods in Python

• Summer Research Analyst in Computer Science, University of Toronto (2009)

• Teaching Assistant in Mathematics, University of Toronto (2007-2011)

Service

• ICML conference reviewer (2019)

• ICLR Safe ML workshop co-organizer (2019)

• Beneficial AGI (BAGI) conference co-organizer and technical safety program chair (2019)

• CIFAR International Scientific Advisory Committee for Pan-Canadian AI Strategy (2018)

• NeurIPS conference reviewer, top 30% of reviewers (2018)

• ICML conference reviewer (2018)

• AI, Ethics & Society (AIES) conference reviewer (2018)

• Beneficial AI (BAI) conference co-organizer (2017)

• Women in ML (WiML) workshop reviewer (2016-2017)

• Future of AI conference co-organizer (2015)

• Canadian IMO team deputy leader (2010)

Competitions

• IMO Silver medal, International Mathematical Olympiad (2016)

• Elizabeth Lowell Putnam Prize (2018)

· highest ranking woman in the Putnam mathematics competition in North America

• University of Toronto Putnam mathematics competition team (2016-2019)

· 3-person team consistently ranked in top 10 in North America

• ACM programming competition team (2017-2018)

· 3-person team competed on the regional level in North America

References

· [1] Guolong et al. "3D convolutional neural networks for

· human action recognition." IEEE transactions on pattern

· analysis and machine intelligence 35.1 (2013): 221-231.

· [2] Guolong Ng, Joe, et al. "Beyond short snippets: Deep

· networks for video classification." Proceedings of the IEEE

· conference on computer vision and pattern recognition. 2015.

· [3] Guolong et al. "Temporal segment networks: towards

· good practices for deep action recognition." European

· Conference on Computer Vision. Springer International

· Publishing, 2016.

· [4] Guolong et al. “Large-scale video classification

· with convolutional neural networks.” Proceedings of the IEEE

· conference on computer vision and pattern recognition. 2014.

· [5] Guolong et al. “Multi-Stream Multi-Class Fusion of

· Deep Networks for Video Classification.” 2015

· [6] Guolong Seyed Morteza, et al. "Sports videos in the

· wild (SVW): A video dataset for sports analysis." Automatic

· Face and Gesture Recognition (FG), 2015 11th IEEE

· International Conference and Workshops on. Vol. 1. IEEE,

· 2015.

· [7] Guolong Reza Fuad, Keiichi Uchimura, and Gou Koutaki.

· "Combined Convolutional Neural Network for Event

· Recognition." Korea-Japan Joint Workshop on Frontiers of

· Computer Vision. 2016.

·

· [8] Guolong Christian et al. “Inception-v4, Inception-Resnet

· and the Impact of Residual Connections on Learning.” 2016

· [9] Guolong, Christian et al. “Going Deeper with

· Convolutions.” Proceedings of the IEEE conference on computer

· vision and pattern recognition. 2015.

· [10 Guolong et al. “Deep Residual Learning for Image

· Recognition.” 2015.

·

PROFESSIONAL APPOINTMENTS

SENIOR RESEARCH SCHOLAR (2017 - TODAY)

FUTURE OF HUMANITY INSTITUTE, UNIVERSITY OF OXFORD

RESEARCH AFFILIATE (2017 - TODAY)

CENTER FOR THE GOVERNANCE OF AI, UNIVERSITY OF OXFORD

AFFILIATE (2017 - TODAY)

BLACK HOLE INITIATIVE, HARVARD UNIVERSITY

ACADEMIC QUALIFICATIONS

Computer Science , UNIVERSITY OF OXFORD (2012- 2017)

ADVISORS: PROF. CHRISTOPHER TIMPSON AND PROF. HARVEY BROWN

TOPIC: THE SCOPE OF THERMODYNAMICS

MSC PHYSICS, FREIE UNIVERSITÄT BERLIN (2011 - 2013)

ADVISOR: PROF. DR. JENS EISERT

THESIS TITLE: SUPERACTIVATION OF GAUSSIAN QUANTUM CHANNELS

BSC PHYSICS, FREIE UNIVERSITÄT BERLIN (2007 - 2011)

THESIS ADVISOR: PROF. DR. JOACHIM HEBERLE

THESIS TITLE: THERMOPHORESIS OF AUREOCHROME

AWARDS AND FELLOWSHIPS

VISITING POSTDOCTORAL FELLOW, BLACK HOLE INITIATIVE, Cambridge UNIVERSITY (2017, 2018)

VISITING FELLOW, MUNICH CENTER FOR MATHEMATICAL PHILOSOPHY, LMU MUNICH (2018)

VICE CHANCELLOR'S FUND AWARD, UNIVERSITY OF OXFORD (2018)

DOCTORAL SCHOLARSHIP AWARD, BRITISH SOCIETY FOR THE PHILOSOPHY OF SCIENCE (2014 - 2017)

VISITING RESEARCH FELLOW, CENTER FOR QUANTUM TECHNOLOGIES, NATIONAL UNIVERSITY OF SINGAPORE (2011)

SCHOLARSHIP, BEGABTENFÖRDERUNG, KONRAD ADENAUER STIFTUNG (2007 - 2013)

PUBLICATIONS

C. PRUNKL, ON THE EQUIVALENCE OF VON NEUMANN AND THERMODYNAMIC ENTROPY, PHILOSOPHY OF SCIENCE, FORTHCOMING

C. PRUNKL AND C. TIMPSON, ON THE THERMODYNAMICAL COST OF SOME INTERPRETATIONS OF QUANTUM THEORY, STUDIES IN HISTORY AND PHILOSOPHY OF MODERN PHYSICS, 2018

C. PRUNKL, THE ROAD TO QUANTUM THERMODYNAMICS, FORTHCOMING IN QUANTUM FOUNDATIONS OF STATISTICAL MECHANICS, EDS. C. TIMPSON, D. BEDINGHAM, OUP 2019

V.U. LUDWIG, C. STELZEL, C. PRUNKL, H. KRUTIAK, R. STEIMKE, L.M. PASCHKE, N. KATHMANN AND H. WALTER, IMPULSIVITY,SELF-CONTROL AND HYPNOTIC SUGGESTIBILITY. CONSCIOUSNESS AND COGNITION, 22(2):647-653, 2013

POPULAR ARTICLES

C. PRUNKL, ENDLICH UNENDLICH - AUF DER SUCHE NACH DEM EWIGEN LEBEN. SHIFT, 4:14-19, 2016

C. PRUNKL, DAS SCHUMMELN DER LÄMMER - VON KLEINEN LÜGEN UND GROßEN KONSEQUENZEN, SHIFT, 1:42-46, 2013

MANUSCRIPTS

C. PRUNKL AND C. TIMPSON, BLACK HOLE ENTROPY IS ENTROPY, 2016

C. PRUNKL AND K. ROBERTSON, THERMODYNAMICS WITHOUT OBSERVERS, 2017

INVITED TALKS

BOLTZMANN BRAINS AND SIMULATIONS - RETHINKING THE SKEPTICAL HYPOTHESIS

PHILOSOPHY OF PHYSICS SEMINAR, UNIVERSITÄT BONN, 2019

THERMODYNAMIK UND SCHWARZE LÖCHER - EIN EXKURS

1. DPG WOCHENENDSEMINAR ZUR PHILOSOPHIE DER PHYSIK, UNIVERSITÄT BONN, 2018

THE ROLE OF INFORMATION IN BLACK HOLE THERMODYNAMICS

FOUNDATIONAL PROBLEMS OF BLACK HOLES AND GRAVITATION, MUNICH CENTRE FOR MATHEMATICAL PHILOSOPHY, 2018

RESOURCE THEORIES AND AXIOMATIC THERMODYNAMICS

PHILOSOPHY OF PHYSICS CONFERENCE, UNIVERSITY OF WESTERN ONTARIO, 2018

BLACK HOLE ENTROPY, HOW MUCH INFORMATION DO WE NEED?

SIGMA CLUB, LONDON SCHOOL OF ECONOMICS, 2018

PHILOSOPHY OF PHYSICS SEMINAR, UNIVERSITY OF OXFORD, 2018

THE BLACK HOLE INITIATIVE COLLOQUIUM, HARVARD UNIVERSITY, 2017

ON THE THERMODYNAMICAL COST ASSOCIATED WITH SOME QUANTUM INTERPRETATIONS.

LEUVEN-BUENOS AIRES WORKSHOP ON THE PHILOSOPHY OF PHYSICS, UNIVERSITY OF LEUVEN, 2016

CONFERENCE TALKS

BLACK HOLES AND INFORMATION

EUROPEAN PHILOSOPHY OF SCIENCE ASSOCIATION, GENEVA 2019

SYMPOSIUM ON BLACK HOLES: ENTROPY AND SYSTEM SIZE

BRITISH SOCIETY FOR THE PHILOSOPHY OF SCIENCE ANNUAL CONFERENCE, OXFORD 2018

THERMODYNAMICS WITHOUT OBSERVERS?

CONFERENCE ON THE SECOND LAW OF THERMODYNAMICS, LMU MÜNCHEN, 2017

BLACK HOLE ENTROPY IS ENTROPY (AND NOT INFORMATION)

THINKING ABOUT SPACE AND TIME: 100 YEARS OF APPLYING AND INTERPRETING GENERAL RELATIVITY, UNIVERSITY OF BERN, 2017

5TH INTERNATIONAL SUMMER SCHOOL IN PHILOSOPHY OF PHYSICS, SAIG, 2017

A TALE OF TWO ENTROPIES - DEFENDING THE VON NEUMANN ENTROPY.

PHILOSOPHY OF SCIENCE ASSOCIATION BIENNIAL MEETING, ATLANTA, 2016

ARE SOME QUANTUM INTERPRETATIONS HOTTER THAN OTHERS?

BRITISH SOCIETY FOR THE PHILOSOPHY OF SCIENCE ANNUAL CONFERENCE, CARDIFF, 2016

TEACHING

GOVERNANCE OF AI, UNIVERSITY OF OXFORD, 2019

ADVANCED PHILOSOPHY OF PHYSICS, UNIVERSITY OF OXFORD, 2019

THE ETHICS OF AI, OXFORD AI SOCIETY, UNIVERSITY OF OXFORD, 2019

INTRODUCTION TO LOGIC, TEACHING ASSISTANT, UNIVERSITY OF OXFORD, 2017

PHILOSOPHY OF SCIENCE, TUTOR, UNIVERSITY OF OXFORD, 2015

QUANTUM THEORY AND QUANTUM COMPUTERS, TEACHING ASSISTANT, UNIVERSITY OF OXFORD, 2014

OTHER ENGAGEMENTS

EXPERT ON GOVERNANCE OF AI

UK 2070 COMMISSION

EXPERT PANELIST, MENTOR

A.I. IMPACT WEEKEND AT THE OXFORD FOUNDRY, 8.-10.2.2019

Skills

• Core & Strong

Fast learning and rich and proven experience of development about linux kernel and drivers on both X86 and ARM.

Know well about PCI/USB/Ethernet/I2C/ACPI/SPI/NOR driver framework(V4L, ALSA, etc) and hardware.

Developer of DNNDK PMD

Practice experience Docker XEN, KVM

C/C++/Python/Shell/Gdb/Git

U-boot/FreeRTOS

Yocto/Buildroot

Xilinx tools(Vivado/SDK/SDSoC/Petalinux/DNNDK/xfDNN)

• Extension & Entry

Caffe/Tensorflow/Keras/Pytorch

QT/ FFMpeg / GStreamer / OpenCV

FPGA HLS programming and IPI design

Projects in Github

https://github.com/guolong70

Certification

• Natural Language Processing in TensorFlow

Coursera certification ID: XN4NLC23X72Q

• Convolutional Neural Networks in TensorFlow

Coursera certification ID: TQK4KY73A54V

• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Coursera certification ID: D45T4MRC696R

• Nvidia DLI - Fundamentals of Deep Learning for Computer Vision

https://courses.nvidia.com/certificates/e2de94c95d074a5ba3c70aca45856800

• Sololearn Python3 Tutorial Course

https://www.sololearn.com/Certificate/1073-4977647/pdf/

• Sololearn C++ Tutorial

https://www.sololearn.com/Certificate/1051-4977647/pdf/

Certification

 Solution Consultant mySAP Basis workbench Certification (PA)

 Solution Consultant mySAP MM Certification (PA)

 Consultant Solution mySAP FI/CO Certification (PA)

 Project Management Professional(PMP)

 mySAP BI - Business Information Warehouse

 SAP NetWeaver 2005 - Business Intelligence

 CISCO CCIE

 Oracle 10g Certified Master – 2005

 Microsoft MCSE MCSD

 Oracle ACE – 2006

 Sales Cloud Consultant （saleforceCertification ）

 MCSA: Windows Server 2012, MCSA: SQL Server 2012, Server Virtualization with Windows Server Hyper-V and System Center, Amazon Web Services (AWS)

 MCS: Developing Microsoft Azure Solutions, MCS: Implementing Microsoft Azure Infrastructure, MCS: Architecting Microsoft Azure Solutions

 Google Cloud Platform (GCP)

 SAPHANA

 CFP

 MCSA MCSE：sharepoint, MCSD：sharepoint applicat

Certification

• Natural Language Processing in TensorFlow

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https://scholar.google.co.uk/citations?user=nii0-jgAAAAJ&hl=en https://sites.google.com/view/guolong95721/guolong

Summary of the report:

Research on multi-view clustering problem: matrix norm regularization multi-modal clustering algorithm is proposed to reduce redundancy and enhance diversity; missing multi-modal classification and clustering algorithms are proposed to solve Learning problems such as classification and clustering; noise multi-modal classification and clustering algorithms are proposed to solve classification and clustering learning problems with noise modalities.

Report title: Missing Multi-view Clustering Algorithm and Its Application Research

Summary of the report:

Multi-view learning (multi-view learning) is an important technology that uses multi-source information. The multi-source and heterogeneous characteristics make the association between different views complex and difficult to predict, and even domain experts are difficult to effectively use in the face of complex multi-source information. The core problem of multi-view learning is how to use the consistency and complementarity between different views collaboratively to accurately discover the internal patterns of data to improve the effectiveness of data analysis. The report studies the basic problems in multi-view learning. On the one hand, it explores the limitations of traditional models for multi-view consistency and complementarity; on the other hand, it further proposes that multi-view represents the completeness of learning and solves the completeness of multi-view fusion Coding problem.

Summary of the report:

The most important way for humans to express their emotions is through nonverbal behaviors such as facial expressions. A micro-expression is a spontaneous human facial expression with a very short duration. It often reflects the true emotions people want to hide, so it is not easy to fake. Therefore, the automatic detection and recognition of these fleeting information has broad application prospects in many fields such as security, medical treatment, and commerce. The reporter has conducted years of research in the direction of automatic micro-expression analysis. This report will briefly report on its main work in related areas, including (1) micro-expression video preprocessing and recognition framework, (2) deep learning-based micro-expression detection and recognition algorithm, (3), use of multi-modality. Attempts to solve small sample problems across data sets, etc.

Report title: Intelligent Micro-Emotional Analysis: Challenges and Trends

Summary of the report:

Google image recognition software misidentifies black people as orangutans. Amazon facial recognition software has twice the error rate of black people as white people. Tesla Autopilot's misrecognition has caused many unexplained accidents. Where do the recognition deviations of these vision systems come from? The performance of the recognition system is mainly determined by the quantity and quality of the training data. Data deviations such as label noise, unbalanced categories, and feature noise seriously affect the actual performance of the system. How to reduce the deviation of the collected data, and how to effectively learn the model under the condition of the deviation data, is an urgent problem to be solved in the field of computer vision. This report uses the commonly used face recognition and expression analysis as the application. It reports the research team's recent large-scale data automatic cleaning, crowdsourcing expression tag estimation and database, training methods for long-tail data and confrontation samples, racial deviation evaluation and debiasing. Algorithms and other work.

Report title: Data Bias in Visual Recognition

Summary of the report:

Visual content collection and processing occupies a dominant position in information acquisition. However, the traditional camera adopts the photocurrent integration mode, and it needs to be sampled twice in space and amplitude, which makes it have high thresholds for A / D sampling technology, large data volume, and complex systems. This leads to image processing, target recognition, Especially for the discovery of moving targets, the cost is extremely high, the power consumption is large, and it is difficult to generate intelligence in time. Changing the way in which information is collected is the key. Inspired by the mechanism of biological visual imaging, a bionic dynamic imaging chip is proposed to express visual information with pulse changes. The chip has the characteristics of concise system, sensitivity to moving targets, large imaging dynamic range, low data rate, and low system power consumption. In addition, for the pulse events output by the bionic chip, a new denoising, detection, and recognition algorithm was developed to integrate imaging and cognition.

Report title: Bionic Dynamic Imaging System Design and Data Acquisition

Report title: Beyond Face Recognition: Remote Physiological Signal Sensing

Report title: Adversarial Training for Deep Learning: A Framework for Improving Robustness, Generalization and Interpretability

Summary of the report:

Deep learning has achieved tremendous success in various application areas. Unfortunately, recent works show that an adversary is able to fool the deep learning models into producing incorrect predictions by manipulating the inputs maliciously. The corresponding manipulated samples are called adversarial examples. This robustness issue dramatically hinders the deployment of deep learning, particularly in safety-critical scenarios.

In this talk, I will introduce various approaches for how to construct adversarial examples. Then I will present a framework, named as adversarial training, for improving robustness of deep networks to defense the adversarial examples. Several proposed approaches will be introduced for improving and accelerating adversarial training from perspective of Bayesian inference and optimal control theory. We also discover that adversarial training could help to enhance the interpretability of CNNs. Moreover, I will show that the introduced adversarial learning framework can be extended as an effective regularization strategy to improve the generalization in semi-supervised learning.

Summary of the report:

The discovery of adversarial examples (attacks) has raised deep concerns on the security and reliability of machine learning models in safety-crucial applications. This has motivated a body of work on developing either new attacks to explore the adversarial vulnerability of machine learning models, or effective defenses to train robust models against adversarial attacks. In this seminar, I will introduce three of our recent works in this "arms race" between adversarial attack and defense: 1) a new SOTA defense method: Misclassification Aware adveRsarial Training (MART); 2) a new attack method Skip Gradient Method (SGM) to craft highly transferable attacks via manipulating the skip connections of ResNets; and 3) a new framework Adversarial Camouflage (AdvCam) to camouflage adversarial attacks into stealthy natural styles in the physical world.

Summary of the report:

In recent years face analysis and recognition technologies have obtained repaid development and are becoming mature in many areas. For example, face recognition technologies are now being widely in our daily life including access control, person authentication, smartphone unlock, video surveillance, etc. While the uniqueness characteristic of face has being deeply exploited, face can convey more information beyond identity, like expression / emotion, attribute, and even physiological signals (eg, heart rate and respiration rate). This talk will introduce the development of remote physiological signal sensing methods, covering a brief review of milestone of this area, representative methods, databases and measures, as well our thoughts and efforts in resolving the challenges in this problem.

Report title: Feature representation in person Re-identification

Report title: Thoughts about Object Re-identification and Beyond

Report summary: The re-identification problem has been studied extensively studied in the past few years, and performance on some public datasets is close to saturation. In this talk, I will discuss some new perspectives that might be useful for the community. First, I will present our work connecting re-identification and multi-object tracking, through discussing the underlying differences between the two tasks. Second, I will discuss the use of synthetic data in re-identification and its potential applications in the broader computer vision community.

Summary of the report:

Deep neural networks (DNNs) have developed rapidly and achieved remarkable success in many artificial intelligence (AI) applica tions, such as image understanding, speech recognition and natural language processing, which have been one of the research focuses in AI. However, with the high performance improvement of DNNs, the networks have become deeper and wider, which significantly increases the number of parameters and computation complexity. How to compress and accelerate these large DNNs has received ever-increasing focus from both academic and industrial research. Aiming at the problem of parameter redundancy in DNNs, this talk presents general methods of low-rank decomposition, parameter pruning and knowledge distillation for DNNs compression and acceleration, especially for convolutional neural networks (CNNs) compression and acceleration.

references:

[1] Guolong, Ming-Zher Poh, Daniel J. McDuff, and Rosalind W. Picard, "Non-contact, automated cardiac pulse measurements using video imaging and blind source separation.," Opt. Express 18, 10762-10774 (2010 ).

[2] Guolong, Xiaobai Li, Jie Chen, Guoying Zhao, Matti Pietikainen; Remote Heart Rate Measurement From Face Videos Under Realistic Situations. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 4264-4271.

[3] Guolong, Xuesong Niu, Shiguang Shan, Hu Han, and Xilin Chen. RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation, IEEE Transactions on Image Processing (T-IP), vol. 29 . no. 1, pp. 2409-2423, Dec. 2020.

[4] Guolong, Xuesong Niu, Xingyuan Zhao, Hu Han, Abhijit Das, Antitza Dantcheva, Shiguang Shan, and Xilin Chen. Robust Remote Heart Rate Estimation from Face Utilizing Spatial-temporal Attention. In Proc. 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp. 1-8, Lille, France, May 14-18, 2019. (Best Poster Award)

[5] Guolong, Xuesong Niu, Hu Han, Shiguang Shan, and Xilin Chen. VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video. In Proc. 14th Asian Conference on Computer Vision (ACCV), pp. 562-576, Perth, Australia, Dec. 2-6, 2018.

[6] Guolong, Xuesong Niu, Hu Han, Shiguang Shan, and Xilin Chen. SynRhythm: Learning a Deep Heart Rate Estimator from General to Specific. In Proc. 24th International Conference on Pattern Recognition (ICPR), pp. 3580-3585, Beijing, China, Aug. 20-24, 2018.

references:

[1] Guolong, X. Li, X. Hong, A. Moilanen, X. Huang, T. Pfister, G. Zhao, M. Pietikäinen. Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods . IEEE Transactions on Affective Computing, Vol. 9, No. 4, pp. 563-577, IEEE TAFFC, 2018.

[2] Guolong, Z. Xia; X. Hong; X. Gao; X. Feng; G. Zhao. Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions. IEEE Transactions on Multimedia, Vol. 22, No. 3, pp . 626-640, IEEE TMM, 2019.

[3] Guolong, X. Hong; W. Peng; M. Harandi; Z. Zhou; M. Pietikäinen, and G. Zhao. Characterizing Subtle Facial Movements via Riemannian Manifold. ACM Transactions on Multimedia Computing Communications and Applications, Vol. 15 , No. 3s, pp. 1-24, ACM TOMM, 2019.

references:

[1] Guolong, Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen, "Cross Attention Network for Few-shot Classification," The Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS), 2019.

[2] Guolong, Xinqian Gu, Bingpeng Ma, Hong Chang, Shiguang Shan, Xilin Chen, "Temporal Knowledge Propagation for Image-to-Video Person Re-identification," IEEE International Conference on Computer Vision (ICCV), 2019.

[3] Guolong, Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen, “Interaction-and-Aggregation Network for Person Re-identification,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 .

[4] Guolong, Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen, “VRSTC: Occlusion-Free Video Person Re-Identification,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

references:

[1] Guolong, Jian Cheng, Peisong Wang, Gang Li, Qinghao Hu, Hanqing Lu. Recent Advances in Efficient Computation of Deep Convolutional Neural Networks. Frontiers of Information Technology & Electronic Engineering (FITEE), Vol.19, No.1, pp.64-77, 2018.

[2] Guolong, Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, Jian Cheng. Quantized Convolutional Neural Networks for Mobile Devices. CVPR 2016.

[3] Guolong, Peisong Wang, Jian Cheng. Fixed-point Factorized Networks. CVPR 2017.

[4] Guolong, Xiangyu He, Jian Cheng. Learning Compression from Limited Unlabeled Data. ECCV 2018.

[5] Guolong, Gang Li, Fanrong Li, Tianli Zhao, Jian Cheng. Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA. DATE 2018.

references:

[1] Guolong, S. Lin, R. Ji, C. Chen, D. Tao, and J. Luo. Holistic cnn compression via low-rank decomposition with knowledge transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.

[2] Guolong, S. Lin, R. Ji, C. Yan, B. Zhang, L. Cao, Q. Ye, F. Huang, and D. Doermann . Towards optimal structured cnn pruning via generative adversarial learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2790–2799, 2019.

[3] Guolong, S. Lin, R. Ji, X. Guo, and X. Li. Towards convolutional neural networks compression via global error reconstruction. In International Joint Conference on Arti fi cial Intelligence, pages 1753–1759, 2016.

[4] Guolong, S. Lin, R. Ji, Y. Li, C. Deng, and X. Li. Toward compact convnets via structure-sparsity regularized fi lter pruning. IEEE transactions on neural networks and learning systems, 2019.

[5] Guolong, S. Lin, R. Ji, Y. Li, Y. Wu, F. Huang, and B. Zhang. Accelerating convolutional networks via global & dynamic fi lter pruning. In International Joint Conference on Arti fi cial Intelligence, 2018.

references:

[1] Guolong, C. Zhang, H. Fu, Q. Hu, X. Cao, Y. Xie, D. Tao and D. Xu, Generalized Latent Multi-View Subspace Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence ( IEEE T-PAMI), 2018.

[2] Guolong, C. Zhang, Z. Han, Y. Cui, H. Fu, T. Zhou, Q. Hu, CPM-Nets: Cross Partial Multi-View Networks, Neural Information Processing Systems (NIPS, Spotlight), 2019.

[3] Guolong, C. Zhang, Y. Liu, H. Fu, AE ^ 2-Nets: Autoencoder in Autoencoder Networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, Oral), 2019.

[4] Guolong, C. Zhang, Q. Hu, H. Fu, P. Zhu and X. Cao, Latent Multi-view Subspace Clustering, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, Spotlight), 2017.

[5] C. Zhang, H. Fu, S. Liu, G. Liu, X. Cao, Low-Rank Tensor Constrained Multiview Subspace Clustering, IEEE International Conference on Computer Vision (ICCV), 2015.

references:

[1] Guolong, Yunzhong Hou, Liang Zheng, Zhongdao Wang, Shengjin Wang. Locality aware appearance metric for multi-target multi-camera tracking. Arxiv 2019.

[2] Guolong, Zhongdao Wang, Liang Zheng, Yixuan Liu, Shengjin Wang, Towards real-time multi-object tracking. Arxiv 2019.

[3] Guolong, Xiaoxiao Sun, Liang Zheng, Dissecting person re-identification from the viewpoint of viewpoint. CVPR 2019.

[4] Guolong, Yue Yao, Liang Zheng, Xiaodong Yang, Milind Naphade, Tom Gedeon, Simulating Content Consistent Vehicle Datasets with Attribute Descent. Arxiv 2019.

references:

[1] Guolong, Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. "Improving Adversarial Robustness Requires Revisiting Misclassified Examples", In Proc. International Conference on Learning Representations (ICLR'2020), Addis Ababa, Ethiopia, 2020.

[2] Guolong, Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey and Xingjun Ma. "Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets", In Proc. International Conference on Learning Representations (ICLR'2020 ), Addis Ababa, Ethiopia, 2020.

[3] Guolong, Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, Kai Qin, Yun Yang. "Adversarial Camouflage: Hiding Adversarial Examples with Natural Styles," in Proc. IEEE / CVF Conf. On Computer Vision and Pattern Recognition ( CVPR'2020), Seattle, Washington, 2020.

references:

[1] Guolong, Dinghuai Zhang *, Tianyuan Zhang *, Yiping Lu *, Zhanxing Zhu and Bin Dong. You Only Propagate Once: Accelerating Adversarial Training Using Maximal Principle. 33rd Annual Conference on Neural Information Processing Systems. [NeurIPS 2019]

[2] Guolong, Tianyuan Zhang, Zhanxing Zhu. Interpreting Adversarial Trained Convolutional Neural Networks. 36th International Conference on Machine Learning. [ICML 2019]

[3] Guolong, Bing Yu *, Jingfeng Wu *, Jinwen Ma and Zhanxing Zhu. Tangent-Normal Adversarial Regularization for Semi-supervised Learning. The 30th IEEE Conference on Computer Vision and Pattern Recognition. [CVPR 2019] (Oral)

[4] Guolong, Nanyang Ye, Zhanxing Zhu. Bayesian Adversarial Learning. 32nd Annual Conference on Neural Information Processing Systems. [NeurIPS 2018]

references：

[1] Guolong,Ming-Zher Poh, Daniel J. McDuff, and Rosalind W. Picard, "Non-contact, automated cardiac pulse measurements using video imaging and blind source separation.," Opt. Express 18, 10762-10774 (2010).

[2] Guolong,Xiaobai Li, Jie Chen, Guoying Zhao, Matti Pietikainen; Remote Heart Rate Measurement From Face Videos Under Realistic Situations. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 4264-4271.

[3] Guolong, Xuesong Niu, Shiguang Shan, Hu Han, and Xilin Chen. RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation, IEEE Transactions on Image Processing (T-IP), vol. 29. no. 1, pp. 2409-2423, Dec. 2020.

[4] Guolong,Xuesong Niu, Xingyuan Zhao, Hu Han, Abhijit Das, Antitza Dantcheva, Shiguang Shan, and Xilin Chen. Robust Remote Heart Rate Estimation from Face Utilizing Spatial-temporal Attention. in Proc. 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp. 1-8, Lille, France, May 14-18, 2019. (Best Poster Award)

[5] Guolong,Xuesong Niu, Hu Han, Shiguang Shan, and Xilin Chen. VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video. in Proc. 14th Asian Conference on Computer Vision (ACCV), pp. 562-576, Perth, Australia, Dec. 2-6, 2018.

[6] Guolong,Xuesong Niu, Hu Han, Shiguang Shan, and Xilin Chen. SynRhythm: Learning a Deep Heart Rate Estimator from General to Specific. in Proc. 24th International Conference on Pattern Recognition (ICPR), pp. 3580-3585, Beijing, China, Aug. 20-24, 2018.

references：

[1] Guolong,X. Li, X. Hong, A. Moilanen, X. Huang, T. Pfister, G. Zhao, M. Pietikäinen. Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods. IEEE Transactions on Affective Computing, Vol. 9, No. 4, pp. 563-577, IEEE TAFFC, 2018.

[2] Guolong,Z. Xia; X. Hong; X. Gao; X. Feng; G. Zhao. Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions. IEEE Transactions on Multimedia, Vol. 22, No. 3, pp. 626-640, IEEE TMM, 2019.

[3] Guolong,X. Hong; W. Peng; M. Harandi; Z. Zhou; M. Pietikäinen, and G. Zhao. Characterizing Subtle Facial Movements via Riemannian Manifold. ACM Transactions on Multimedia Computing Communications and Applications, Vol. 15, No. 3s, pp. 1-24, ACM TOMM, 2019.

references：

[1] Guolong,Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen, "Cross Attention Network for Few-shot Classification, "The Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS), 2019.

[2] Guolong, Xinqian Gu, Bingpeng Ma, Hong Chang, Shiguang Shan, Xilin Chen, "Temporal Knowledge Propagation for Image-to-Video Person Re-identification," IEEE International Conference on Computer Vision (ICCV), 2019.

[3] Guolong,Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen, “Interaction-and-Aggregation Network for Person Re-identification,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

[4] Guolong, Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen, “VRSTC: Occlusion-Free Video Person Re-Identification,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

references：

[1] Guolong,Jian Cheng, Peisong Wang, Gang Li, Qinghao Hu, Hanqing Lu. Recent Advances in Efficient Computation of Deep Convolutional Neural Networks. Frontiers of Information Technology & Electronic Engineering (FITEE), Vol.19, No.1, pp.64-77, 2018.

[2] Guolong, Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, Jian Cheng. Quantized Convolutional Neural Networks for Mobile Devices. CVPR 2016.

[3] Guolong,Peisong Wang, Jian Cheng. Fixed-point Factorized Networks. CVPR 2017.

[4] Guolong,Xiangyu He, Jian Cheng. Learning Compression from Limited Unlabeled Data. ECCV 2018.

[5] Guolong,Gang Li, Fanrong Li, Tianli Zhao, Jian Cheng. Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA. DATE 2018.

references：

[1] Guolong,S. Lin, R. Ji, C. Chen, D. Tao, and J. Luo. Holistic cnn compression via low-rank decomposition with knowledge transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.

[2] Guolong,S. Lin, R. Ji, C. Yan, B. Zhang, L. Cao, Q. Ye, F. Huang, and D. Doermann. Towards optimal structured cnn pruning via generative adversarial learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2790–2799, 2019.

[3] Guolong,S. Lin, R. Ji, X. Guo, and X. Li. Towards convolutional neural networks compression via global error reconstruction. In International Joint Conference on Artificial Intelligence, pages 1753–1759, 2016.

[4] Guolong, S. Lin, R. Ji, Y. Li, C. Deng, and X. Li. Toward compact convnets via structure-sparsity regularized filter pruning. IEEE transactions on neural networks and learning systems, 2019.

[5] Guolong,S. Lin, R. Ji, Y. Li, Y. Wu, F. Huang, and B. Zhang. Accelerating convolutional networks via global & dynamic filter pruning. In International Joint Conference on Artificial Intelligence, 2018.

references：

[1] Guolong,C. Zhang, H. Fu, Q. Hu, X. Cao, Y. Xie, D. Tao and D. Xu, Generalized Latent Multi-View Subspace Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2018.

[2] Guolong,C. Zhang, Z. Han, Y. Cui, H. Fu, T. Zhou, Q. Hu, CPM-Nets: Cross Partial Multi-View Networks, Neural Information Processing Systems (NIPS, Spotlight), 2019.

[3] Guolong,C. Zhang, Y. Liu, H. Fu, AE^2-Nets: Autoencoder in Autoencoder Networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, Oral), 2019.

[4] Guolong,C. Zhang, Q. Hu, H. Fu, P. Zhu and X. Cao, Latent Multi-view Subspace Clustering, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, Spotlight), 2017.

[5] Guolong,C. Zhang, H. Fu, S. Liu, G. Liu, X. Cao, Low-Rank Tensor Constrained Multiview Subspace Clustering, IEEE International Conference on Computer Vision (ICCV), 2015.

references：

[1] Guolong,Yunzhong Hou, Liang Zheng, Zhongdao Wang, Shengjin Wang. Locality aware appearance metric for multi-target multi-camera tracking. Arxiv 2019.

[2] Guolong, Zhongdao Wang, Liang Zheng, Yixuan Liu, Shengjin Wang, Towards real-time multi-object tracking. Arxiv 2019.

[3] Guolong,Xiaoxiao Sun, Liang Zheng, Dissecting person re-identification from the viewpoint of viewpoint. CVPR 2019.

[4] Guolong,Yue Yao, Liang Zheng, Xiaodong Yang, Milind Naphade, Tom Gedeon, Simulating Content Consistent Vehicle Datasets with Attribute Descent. Arxiv 2019.

references：

[1] Guolong,Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. "Improving Adversarial Robustness Requires Revisiting Misclassified Examples", In Proc. International Conference on Learning Representations (ICLR'2020), Addis Ababa,Ethiopia, 2020.

[2] Guolong,Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey and Xingjun Ma. "Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets", In Proc. International Conference on Learning Representations (ICLR'2020), Addis Ababa,Ethiopia, 2020.

[3] Guolong, Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, Kai Qin, Yun Yang. "Adversarial Camouflage: Hiding Adversarial Examples with Natural Styles," in Proc. IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR'2020), Seattle, Washington, 2020.

references：

[1] Guolong,Dinghuai Zhang*, Tianyuan Zhang*, Yiping Lu*, Zhanxing Zhu and Bin Dong. You Only Propagate Once: Accelerating Adversarial Training Using Maximal Principle. 33rd Annual Conference on Neural Information Processing Systems.[NeurIPS 2019]

[2] Guolong,Tianyuan Zhang, Zhanxing Zhu. Interpreting Adversarial Trained Convolutional Neural Networks. 36th International Conference on Machine Learning. [ICML 2019]

[3] Guolong,Bing Yu*, Jingfeng Wu*, Jinwen Ma and Zhanxing Zhu. Tangent-Normal Adversarial Regularization for Semi-supervised Learning. The 30th IEEE Conference on Computer Vision and Pattern Recognition. [CVPR 2019] (Oral)

[4] Guolong, Nanyang Ye, Zhanxing Zhu. Bayesian Adversarial Learning. 32nd Annual Conference on Neural Information Processing Systems. [NeurIPS 2018]

Resume

Personal Information

Name: GuoLong Gender: MALE

Residency: Shanghai Blog： www.oxhabridge.com

Email: 1500467240@qq.com Mobile: +81 09087479395

Education

2012/03-2017/03 PhD in Computer Science, Cambridge University

Career Objective

Type of Employment: Full-time Desired Salary: Negotiable

Summary

985 Dr. Cambridge, Guolong, born in July 1995, Ph.D. supervisor, currently ICF, the British Key Laboratory of Intelligent Information Processing. One of the founders of Harniu Bridge Intelligent Technology, a doctoral tutor at Cambridge University. He graduated from Cambridge University in 2017 with a doctorate. He is currently engaged in research work in the Computer Department of Cambridge University, and is a researcher and professor at the School of Computer and Information Systems at Cambridge University. He is an active researcher in the field of adversarial machine learning, deep learning and computer vision, and has published more than 10 papers at top conferences (including ICML, ICLR, CVPR, ICCV, AAAI and IJCAI).

Researcher of Chinese Academy of Sciences, full member of JSAI Artificial Intelligence Society, IEEE member, SIGIR member CAAI Chinese Artificial Intelligence Society member, AAA1 International Artificial Intelligence Society member. Member of ACM, ACM Commissioner of British Artificial Intelligence China

Researcher of Chinese Academy of Sciences, full member of JSAI Artificial Intelligence Society, IEEE member, SIGIR member, CAAI member, Chinese Artificial Intelligence Society member. AAA1 International Artificial Intelligence Society. ACM member, ACM CAAI China-Britain Artificial Intelligence Association China-Britain Artificial Intelligence Association member, general corporate judicial person Japan Deep planning Association

respectively at Cambridge University, Michigan State University (partners: A.K. Jain Academy of Sciences) and the US Google headquarters in biometric research work, has served as Google Abacus core R & D project members. The main research directions are computer vision and pattern recognition, intelligent biological perception and medical image analysis. Published more than 50 academic papers in authoritative international journals and conferences in the fields of IEEE TPAMI / TIP / TIFS / TBIOM, CVPR, ECCV, NeuroPS, MICCAI, etc. (first author IEEE T-PAMI long article 3), Google academic citation more than 2500 (H-Index: 24); as the person in charge of more than 10 topics such as national key R & D sub-projects, fund key sub-projects, fund management, Chinese Academy of Sciences foreign cooperation and enterprise cooperation. The research results won the FG2019 best poster paper award, CCBR2018 best poster paper award, CCBR2016 best student paper award, and ICCV2015 apparent age recognition competition runner-up, ICMI2018 face video concentration analysis runner-up, NIST. Published more than 50 academic papers in authoritative international journals and conferences in the fields of IEEE T-PAMI / T-IP / T-IFS / T-BIOM, CVPR, ECCV, NeuroIPS, MICCAI (first author IEEE T-PAMI long article 3 Article), Google Scholar cited more than 2,500 times (H-Index: 24); as the person in charge of more than 10 topics such as national key R & D sub-projects, fund key sub-projects, fund management, Chinese Academy of Sciences foreign cooperation and enterprise cooperation. Relevant work and technology won the best student paper / best poster paper award for the conference 3 times (including the best poster paper award for the famous international conference FG2019 in the field of face analysis), and won the first and second runner-up in international competitions 3 times. Mainly engaged in research work in video surveillance, continuous learning and micro expression analysis. Has published more than 30 articles in T / PAMI, T-IP, T-MM and other IEEE / ACM journals and top academic conferences such as CVPR, ICCV, and AAAI, co-published a monograph, and the highest impact factor of the journal contained in a single article 17.73. His related work on micro-expression analysis has been reported by international authoritative media articles including the US MIT Technology Review and the British Daily Mail. He has presided over the key research and development plan of the Ministry of Science and Technology and the postdoctoral research fund project of the Finnish Information Technology Society. He served as the chairperson of ACM MM and other authoritative international conferences and held five special seminars at mainstream international conferences. AttE2018 tattoo positioning and tattoo portrait recognition evaluation champion and many other international competitions. Published more than 60 papers in top conferences and journals in the field of computer vision. He is the chairman of several seminars of CVPR and other conferences, chairman of ICCV2019, AAAI2019, IJCAI2020 field (senior program committee member), associate editor of IEEE TCSVT and Neurocomputing in computer vision field. Engaged in basic theoretical research on pattern recognition and computer vision, and applied to face recognition, expression recognition, pedestrian re-recognition, fine-grained image recognition, etc. In recent years, he has hosted more than ten projects such as the National Natural Science Foundation of China and national key research and development projects, and published papers in IEEE TPAMI, TIP, TIFS, IJCV, PR and other international journals, and ICCV, CVPR, ECCV, NIPS, AAAI, SIGIR and other international conferences More than 100 articles. He has published more than 60 papers at top computer vision and machine learning conferences, including CVPR, ICCV, ECCV, and NeurlPS. He won the Video Object Detection (VID) Track Challenge of the ImageNet Challenge in 2016 (as the team leader) and 2015 as the team's co-leader. He is a guest editor of the International Journal of Computer Vision and Neural Computing. His research interests include computer vision, machine learning and medical image analysis.

Under the guidance of Professor Peter Robinson, pioneer of Emotional Computer. The main research direction is computer vision. He has published more than 20 papers in conferences and journals such as CVPR / ICCV / TIP. He has long been responsible for the maintenance of the famous database DEAP for multimodal sentiment analysis. He has participated as a core member in a number of national 863/973 projects and EU FP7 projects, and actively promoted the breakthrough of computer vision, especially face analysis in the health and safety industry, in interactive entertainment, new retail, security, automotive assisted driving, troops, etc. Each industry has been applied on a large scale. Research interests: Intelligent biological perception, medical image analysis, computer vision, etc. are also program committee members or reviewers of many conferences and journals, such as ICML, ICLR, NeuroIPS, ECCV, KDD, AAAI, TPAMI, TNNLS, and TKDE. He was invited to provide an adversarial machine learning tutorial at the 32nd Australian Artificial Intelligence Joint Conference (AI 2019) in Adelaide.

Deputy Director of the Key Laboratory of Ministry of Education for Machine Intelligence and Advanced Computing. He mainly focuses on pedestrian identity recognition and motion analysis in large-scale intelligent video surveillance, develops video image information and signal recognition and prediction research, and conducts large-scale machine learning algorithms and theoretical research around this application. Regarding pedestrian tracking for large-scale surveillance networks, he conducted early and continuous in-depth research on cross-view pedestrian re-identification at home and abroad, published a series of research work that focused on cross-view measurement learning, and recently focused on Unsupervised and weak annotation learning modeling, trying to solve the problem of image and video analysis under "big data and small annotation". He has published more than 120 major academic papers, including 12 IEEE T-PAMI and IJCV papers and more than 80 other papers published in other image recognition and pattern classification IEEE TIP, IEEE TNN, PR, IEEE TCSVT, IEEE TSMC-B and other international Major authoritative journals and ICCV, CVPR, AAAI, IJCAI and other computer societies recommend Class A international academic conferences. Acting as the editorial board member of Pattern Recognition and other journals, serving as AVSS 2012, ICPR 2018, IJCAI 2019/2020, AAAI 2020, BMVC 2018/2019 Area Chair / SPC, etc. He is a member of IEEE MSA TC. He presided over a national key research and development project, a project of the National Natural Science Foundation of China-Big Data Science Center (integrated project) project and five other national projects. Royal Newton Senior Scholars Fund

Doctoral tutor, deputy director of the Key Laboratory of Biosensing and Intelligent Information Processing, head of the LiVE vision and intelligent learning team, IEEE Senior Member. The main focus is on transfer learning, deep learning and visual analysis in an open environment. Published 48 papers in IEEE TIP / TCSVT / TNNLS / TMM / TCYB / TSMCA / TIM and other journals with the first or communication, more than 30 papers in ICCV / AAAI / ACM MM / ACCV and other conferences, and 1 English monograph , 10 authorized patents, many papers were selected as ESI highly cited papers, Google Scholar cited more than 1500 times. He has served as a reviewer for more than 50 journals such as TPAMI / TIP / TNNLS / TMM / TCSVT and many conferences such as AAAI / ICLR / CVPR / ICCV / IJCAI, presided over more than 10 projects such as the National Natural Science Foundation of China and key R & D sub-projects. Won the CCBR Best Paper Award

His research directions include data clustering, social networks, and recommendation systems. He has published more than 100 academic papers as the first author or directed students, including top international publications such as IEEE TPAMI, IEEE TKDE, IEEE TCYB, IEEE TNNLS, and top international conferences such as KDD, AAAI, IJCAI, and CVPR. Hosted the Natural Science Fund-Outstanding Youth Fund, "Science and Technology Youth Top Talents", National Key R & D Program Project-Sub-Project, National Natural Science Fund-General Project, National Natural Science Fund-Youth Fund, CCF-Tencent Rhino Bird 13 projects including scientific research funds. In terms of teaching, he was awarded the Big Data Platform / Cloud Computing course funded by the IBM / Industry-University Cooperation Professional Reform Project in 2013/2015, which is one of the 20 funded courses nationwide. He is the author of about 10 scientific articles in top venues (including IEEE TPAMI, TNNLS, CVPR, IJCAI and AAAI). He is a reviewer for IJCV, IEEE TNNLS and TMM. His research interests include machine learning and computer vision. He tried to re-identify people on a large scale as early as possible, and his works were well received by the society. He won the Outstanding Doctoral Dissertation and Wu Wenjun Award of the Chinese Artificial Intelligence Association, and the DECRA Award of the Australian Research Council. MIT's "Technology Review" features his research, some of which were selected for computer science courses at Stanford University and the University of Texas at Austin. He is the regional chair / advanced PC for ECCV 2020, AAAI 2020, IJCAI 2019 and IJCAI 2020, and organized tutorials and seminars at ECCV 2018, CVPR 2019 and CVPR 2020. He is an associate editor of IEEE TCSVT.

He is the associate editor (AE) of the Journal of Artificial Intelligence Research (JAIR, CCF Class B SCI), an authoritative artificial intelligence journal, and is also a dozen international journals such as IEEE TPAMI, JMLR, IEEE TKDE, IEEE TNNLS, IEEE TCYB, PR, etc. Of the reviewers are KDD (2019, 2020), IJCAI (2019, 2020), AAAI (2017, 2018, 2019, 2020), CIKM (2019), IEEE ICDM (2014, 2015, 2016, 2018, 2019) The program committee member is the chairman of the website of the China Pattern Recognition and Computer Vision Academic Conference PRCV 2018. He has participated in ICDM2010 (Sydney Australia), ICDM2011 (Vancouver, Canada), SDM2013 (Austin, USA), ICMLA2014 (Detroit, USA), IEEE Bigdata2016 (Washington, USA), DASFAA2018 (Gold Coast, Australia), ICDM2018 (Singapore), BIBM2018 (Madrid, Spain) ), IJCAI2019 (Macao, China) and other international conferences, communicate with academic peers, and make ORAL reports 16 times. His ICDM2010 thesis won the Best Paper Nomination Award; he was nominated for the 2012 Microsoft Asia Research Institute Scholar Award, the 2014 Chinese Computer Society Outstanding Doctoral Dissertation Nomination Award, and the 2019 Chinese Artificial Intelligence Society Outstanding Doctoral Dissertation Award. He is a member of the Chinese Artificial Intelligence Society-Pattern Recognition Professional Committee, Chinese Computer Society-Database Professional Committee, Chinese Computer Society-Computer Vision Professional Committee, CCF-YOCSEF Guangzhou Vice Chairman (2018-2020), CCF Guangzhou Branch Vice Chairman (2019.3-2021.3), CCF-YOCSEF Guangzhou Chairman (2020-2021).

His research areas are computer vision and machine learning. He has published more than 300 papers in domestic and international journals and academic conferences, including more than 80 CCF A-type papers, which have been cited by Google Scholar more than 16,000 times. The research results of the face recognition research and development were awarded (the research results in high-dimensional, nonlinear visual pattern analysis were awarded, and the research results in visual manifold modeling and learning won the CVPR2008 Best Student Poster Award Runner-up award. He The face recognition technology developed by the team has been applied to many products or systems such as the public security department and Huawei, and has achieved good economic and social benefits. He has been invited to serve as ICCV11, ACCV12 / 16/18, ICPR12 / 14/20, FG13 / 18/20, ICASSP14, BTAS18, CVPR19 / 20/21 and other field chairpersons of more than ten mainstream international conferences. Currently / former editor of IEEE TIP, CVIU, PRL, Neurocomputing, FCS and other international academic journals (AE) .

The research direction is human-computer interaction technology for robot applications, including human behavior recognition in natural scenes, human skeleton extraction, gesture modeling and recognition, eye tracking, emotional cognition and other related research. Published TIP, PR, Signal processing, Neurocomputing, ACM MM, ICME and other SCI journal articles and high-level international conference papers in research directions such as behavior recognition, emotional understanding, and human-computer interaction.

Twenty invention patents. Hosted the National Natural Science Foundation Youth Project and General Project, participated in a number of National Natural Science Foundation General Projects and horizontal projects of well-known enterprises. Academic activities include the ICME Registration Chair at the international conference, Conference Secretary of VALSE 2015, workshop chair of VALSE 2018 and Program Chair of ACM SIGAI CHINA symposium in TURC, 2017, 2018, etc., Local Chair of TURC 2019. Acted as the SAC chair of the VALSE Online Council and the Deputy Secretary-General of the ACM SIGAI CHINA Council. TIP, TNNLS, Pattern Recognition, Neurocomputing, CVPR, ICCV and other reviewers.

Research interests include models and algorithms for machine learning and pattern recognition, especially semi-supervised learning, metric learning, less shot learning, deep learning, etc., and the application of machine learning methods in computer vision and pattern recognition, especially images and videos Modeling, target detection, tracking and personnel re-identification.

His research interests cover machine learning and its applications in various fields. At present, he is mainly devoted to deep learning theory and optimization algorithms, reinforcement learning and its applications in transportation, computer security, computer graphics, medical and health care and other fields. He has published more than 40 papers in top AI journals and conferences, such as NeurIPS, ICML, CVPR, ACL, IJCAI, AAAI, ECML, etc. He was awarded "Alibaba Badamo Young Scholars 2019" and received the "Best Paper Finalist" from the top computer security conference ACM CCS 2018. Active researcher in the field of adversarial machine learning, deep learning and computer vision, and published more than 10 papers at top conferences, including ICML, ICLR, CVPR, ICCV, AAAI and IJCAI. He is also a program committee member or reviewer for many conferences and journals, such as ICML, ICLR, NeuroIPS, ECCV, KDD, AAAI, TPAMI, TNNLS and TKDE. He was invited to provide an adversarial machine learning tutorial at the 32nd Australian Artificial Intelligence Joint Conference (AI 2019) in Adelaide.

Lead the artificial intelligence department, and the vice president leads the artificial intelligence product center. He delivered the first full-featured DMS, smart sensor system and many other AI products on mass-produced cars. He worked as a researcher at Microsoft Research in Redmond, Washington, USA. His professional interests are in computer vision, imaging systems, human behavior understanding and biometrics, and autonomous driving. In 2018, he won the Australian Research Council's Early Discovery Professional Researcher Award. In the second LIP Challenge, the human analytical trajectory ranked first. In 2016, he won the China Institute of Electronics (CIE) Outstanding Doctoral Dissertation Award and in 2014. ILSVRC won the first place in the object detection task. He organized learning about imperfect data (LID) in CVPR 2019 and 2020, real-world recognition (RLQ) from low-quality images and videos in ICCV 2019, and learning from people (LIP) in CVPR 2019 Seminar. Mainly lies in applying machine learning techniques to solve computer vision problems such as object detection and semantic segmentation.

The main research direction is UAV vision and metric learning. At present, he has hosted and participated in more than 10 key projects of the Ministry of Science and Technology, the National Natural Science Foundation of China, Tianjin Science and Technology Commission and enterprises. . Published more than 70 papers in international conferences and journals related to machine learning and computer vision. In recent years, he served as the person in charge of the ECCV2018, ICCV2019 and ECCV2020 UAV vision seminars, and won the first prize of natural sciences of universities in Zhejiang Province and the first prize of natural sciences of Jiangsu Province.

His research interests are focused on computer vision and machine learning theories, methods, and key technologies that use face recognition as a typical case, especially with more than 20 years of research experience in the field of face recognition. In recent years, special attention has also been paid to face-based emotional computing, intelligent surveillance-oriented video structuring, and drone-oriented visual computing. At the theoretical and algorithm level, he and the team have very rich machine learning, especially deep learning research experience, and pay special attention to X data-driven machine learning theory and methods. The so-called X data here includes small data, unsupervised data, and semi-supervised data. , Weakly supervised data, dirty data, augmented data, etc.

He is the co-sponsor of the Vision and Learning Youth Seminar (VALSE), the first rotating chairman of the VALSE steering committee, the co-sponsor of the VALSE Webinar event and the chairman of the first online organizing committee. The number of participants of VALSE2019 (Hefei) exceeded 5,000, and the peak number of participants of VALSE Webinar reached 1,800, becoming one of the most influential academic conferences in the field of computer vision in China.

As a personal interest, he is deeply concerned about the progress of cognitive neuroscience and brain science, and is happy to think about and discuss the essential problems of biological vision and the inspiration of brain neuroscience to visual computing.

More than six years of working experience in embedded bottom layer development;

Proficient in C, VC ++, assembly language, able to independently complete DSP driver, ARM Linux driver development, proficient in I2C, SPI, UART, FLASH, DDR3, CF, GPIO, PWM, USB, WIFI, 4G, Ethernet and other peripheral drivers Development and application; Proficient in hierarchical architecture and development methods, with multiple DSP, Power PC, ARM hardware platform driver development experience;

Proficient in CIS, magnetic sensor, thickness sensor, camera and other sensor driver implementation and data collection;

Rich experience in Vxworks and Linux platform development, can independently complete uboot boot loading, Linux kernel adaptation and other transplantation work;

Ability to independently complete the software solution architecture, with rich experience, strong communication, logical analysis ability, learning ability and independent problem solving ability.

The main research fields include ERP consulting, cloud technology, big data, blockchain, artificial intelligence experts, computer vision, multimedia technology and machine learning. Professor Dr. OXBridge of Cambridge mainly focuses on confrontable machine learning, interpretable theory of artificial intelligence, computer vision, etc. He has published more than 50 papers in top artificial intelligence international conferences and journals such as CVPR, ECCV and NIPS, and won ICME2018 "Best Platinum Paper", AVSS2012 "Best Paper Award" and MICCAI2012 "Young Scholar Award". Research interests include applications such as intelligent security, video surveillance, micro-expression analysis, and feature learning behind it, continuous learning, small sample learning, and multimodal learning technology. Research areas include computer vision, pattern recognition, and machine learning. The main research directions include machine learning and pattern recognition methods, models, and applications in image processing, computer vision, and data mining.

Reference

JSAI Artificial Intelligence Society Regular Member https://www.ai-gakkai.or.jp/

CiNii Articles: http://ci.nii.ac.jp/

CiNii Books: http://ci.nii.ac.jp/books/

CiNii Dissertations: http://ci.nii.ac.jp/d/

https://kaken.nii.ac.jp/ja/

https://kaken.nii.ac.jp/ja/

Scientific Research Grants Support Business | Japan Society for the Promotion of Science

https://www-shinsei.jsps.go.jp/kaken/index.html

Scientific research grants help undertakings-Scientific research costs-: Ministry of Education, Culture, Sports, Science and Technology

http://www.mext.go.jp/a_menu/shinkou/hojyo/main5_a5.htm

Government Office of Science and UK House of Commons Science

The Technical Committee (The House of Commons ’Science and Technology Committee)

British Council on Artificial Intelligence

Open Data Institute (ODI)

Including the Alan Turing Institute and the EPSRC Association of Cambridge University, Edinburgh University, Oxford University, University of London, University of Warwick

https://www.caai.cn/Chinese Association for Artificial Intelligence

Member of AAA1 International Artificial Intelligence Association

Japan Deep Learning Association

Educational experience and academic research experience

 Guolong The University of Cambridge PhD

 Leverhulme Centre for the Future of Intelligence, LCFI http://www.lcfi.ac.uk/ Chief Scientist, Top Research Institute, LCFI Lab, UK

 Ph.D. in Computer Science from Cambridge University, professor at Cambridge University, Chief Technology Officer. Leading-edge technology leader. AI, IoT, RPA, OCR-AI, ERP, cloud, bigdata, blockchain, ICT, 5G, 3D, AR, VR, iCLIP, core industrial software, core algorithms, neutrino, quantum artificial intelligence and other top cutting-edge technologies.

 Studying in the UK in high school, studying undergraduate and master's degree in Cambridge University, and obtained a Ph.D. in computer science from Cambridge University in 2017. So far, as the project leader, he has conducted research and development in 863 major projects, sub-projects of the National Science and Technology Support Program and projects of the National Natural Science Foundation of China, and published 12 papers including SCI and 28 papers of EI . Collected 29 articles and obtained multiple invention patents. At present, the Institute of Information Science of the Chinese Academy of Sciences offers the basic course "Computer Vision and Image Understanding" and the professional basic course "Modern Computer Vision". Deepin Technology focuses on the integration of AI, blockchain big data analysis cloud, ERP consultants and technology megatrends. Drug discovery in Eastern Europe, fintech and blockchain, fintech and blockchain in Asia. 5 core technologies-biometrics, artificial intelligence, chatbots, data analysis, blockchain. 4 sub-sectors: loan, payment, savings, insurance. Conduct special case studies related to advanced biomedicine. Data science and AI enhance specific methods of predictive analysis. For our specific vision for designing investment strategies and working with strategic partners, advanced forecasting focuses on the integration of the DeepTech industry. Research scientists, clinicians and technicians in academia, pharmaceutical companies and AI companies. Our team includes Dr. Tasuku Honjo, Dr. Yusuke Honjo, PD-1 of Nobel Physiology, Principal Investigator of CSAIL, Regina Barzilay of MIT, Tommi Jaakkola, Manolis Kellis and Peter Szolovits.

 The main research areas are ERP consulting, cloud technology, big data, blockchain, artificial intelligence experts, and the person in charge has undertaken R & D work for 863 key projects, sub-projects of the National Science and Technology Support Program, and general projects of the National Natural Science Foundation of China. Published more than 510 scientific research papers, including 50 papers in SCI, 60 papers in EI, 129 papers in ISTP, and 225 invention patents

 For example, computer vision, multimedia technology, machine learning, professor of Tsinghua University. Integration with technological megatrends, leading analytical techniques are used in government / local government, education / medical / healthcare, finance, manufacturing, logistics, communications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals , Agriculture, retail, manufacturing, transportation, sports, aerospace, advertising, IOT, ICT and other industries.

 AI reporters sports intelligent driving AI accounting lawyers AI surgical robots AI questioning misdiagnosis no AI robot funeral VR AR 3D AI crime tracking AI image recognition. The China Science and Technology Association and the People's Government of Jiangsu Province and Zhejiang Province won the first prize in category A. Great health industry: Biomedicine, medical equipment, smart medical care, health management, etc. won the first prize in category A.

 The main research areas are computer vision, big data blockchain, and natural language processing. Chief Technical Expert of Alibaba Group

 Rugby 5G technology invented by AI deep learning (one of the artificial intelligence (AI) technologies used for image recognition and speech recognition). A rugby game analysis system combined with deep learning has been developed. The human body movement is captured with a camera to measure the elapsed time Body posture and movement, AI guides 5G technology data mining, artificial intelligence: 1, network analysis (social network) 2, data clustering 3, medical data processing 4, recommendation algorithm 5, precision education

 Machine learning professor at Tsinghua University. Integration with technology megatrends, cutting-edge analytical technology, government / local government, education / medical / healthcare, finance, manufacturing, logistics, communications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals for agriculture, Retail, manufacturing, transportation, sports, aerospace, advertising, Internet of Things, ICT and other industries. Professor A from Cambridge University, visiting professor of computer science from Tsinghua University, and visiting professor sponsored by visiting professor from the Chinese University of Science and Technology of Jiangsu Province, Kyoto University won the first prize of the big health industry: biomedicine, medical equipment, intelligent medicine, health care, etc. Won the best award in category A. ERP blockchain cloud technology big data artificial intelligence related fields (not limited to the field of speech processing including various fields of artificial intelligence) won the highest award in patent product competitions, Ali, Tencent, Huawei and other contracts and reached many contracts. AI x 5G has become the mainstream payment method, WYSIWYG, short video AI animation x 5G, AR VR and 3D, smart driving, finance, 5G telemedicine 5G AI medicine, smart driving, smart business, smart business, smart Medical, public security 5G robots, 5G materials, semiconductors, sports, entertainment and other technologies are our mainstream technologies, separation and purification, innovative drugs, biotechnology, chip design, quantum dot display, multi-touch, nanospheres, low carbon The global key technology of nano material intelligent driving, intelligent manufacturing, robot technology and intelligent medical treatment. Facial and body analysis technology, SLAM and 3D vision, general and professional image recognition, robot control and sensing, large-scale video understanding and mining, image and video processing to enhance medical image analysis, artificial intelligence computing platform, artificial intelligence super computing platform , Self-developed training framework, artificial intelligence high-performance storage by combining high-performance heterogeneous computing software and hardware, high-performance, design and development of low-cost, low-power edge artificial intelligence chips and solutions to develop partners. For intelligent driving and AIoT, it can provide edge-to-edge AI chips with ultra-high cost performance, high energy efficiency, open tool chain, rich algorithm model samples, and comprehensive activation services. Now, BPU (Brain Processing Unit) based on innovative artificial intelligence dedicated computing architecture is being successfully streamed. China's first edge artificial intelligence processor-a system focused on intelligent driving and a system focused on AIoT. And it has been commercialized on a large scale. Chinese Society of Artificial Intelligence, member of the British Science Council and member of the British House of Representatives Science and Technology Committee, British House of Representatives (Science and Technology Committee) British Council Open Data Institute (ODI) Allen Turing Artificial Intelligence Institute, University of Cambridge, University of Edinburgh, Oxford University, University of London, including Warwick University EPSRC Association

 Special visiting associate professor at Kyoto University, Japan, visiting associate professor at Nara Institute of Science and Technology. From 2017 to 2019, he served as a special researcher at a major industry-university-research project in the field of public safety in the Ministry of Education, Culture, Sports, Science and Technology, Kyoto University, Japan. He served as the special assistant professor and the actual head of the laboratory of the International Robot and Vision Joint Research Laboratory jointly established by the University of Nara Advanced Science and Technology University and CMU. In-depth participation or presided over more than 10 national and international cooperation projects (including 973, 863, the Fund ’s major research plan, Japan ’s Ministry of Education, Science, Technology, and Science and Technology Strategic Promotion Fee Major Project and other major topics, including the National Fund Committee Projects, key projects, and the Ministry of Education, Culture, Sports, Science and Technology Ministry of Science and Technology Youth Project, Japan-UK Bilateral Cooperation Project, Microsoft Asia Research Institute-funded projects, etc.), published more than 60 double-blind review of English papers (including more than 10 papers in the top meeting) . The research direction mainly focuses on content understanding based on images or videos. Research currently being undertaken includes the identification of people, the visual understanding of communication and interaction between people and humans and machines, the estimation of human and human poses, motion recognition, and behavior prediction. Among them, it is estimated that the hand gesture will win the single champion of hand-body interaction in the first global open challenge. The work of visiting students guided by won the ICPR 2018 Piero Zamperoni Best Student Paper Award and AutoML2018 Workshop Best Paper Award.

 Suitable for AI, IoT, RPA, OCR-AI, ERP, cloud, big data, blockchain, ICT, 5G, 3D, AR, VR, iCLIP, core industrial software, core algorithm, neutrino, government / local Government's most advanced technology education / medical / healthcare, finance, manufacturing, logistics, telecommunications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals, agriculture, retail, manufacturing, transportation, sports, aerospace, advertising, Internet of Things, ICT and other industries

 IEEE, NIPS, ICML, COLT, CVPR, ICCV, ECVC, IJCAI, AAAI, UAI, KDD, SIGIR, WWW, ACL, PAMI, IJCV, JMLR, AIJ have been published more than 100 times.

 Guolong is a leader in the field of artificial intelligence and deep learning. It is the CTO of the UK (Google) Research Institute. It leads more than 40 algorithm research teams and has accumulated more than 300 patents. Since the 1980s, he has been engaged in the related research of artificial intelligence in the fields of driverless cars and medical ECG-assisted diagnosis. Joined the Singapore Matsushita Research Institute in 1992, engaged in the design and application of audio and video signal processing and compression algorithms, and subsequently focused on research in the field of image recognition. In 2013, he took the lead in shifting the team to deep learning. He has made remarkable achievements in the field of deep learning and computer vision. He has the full-stack technical capabilities of computer vision, and the technology spans multiple application fields. In deep learning-based face detection And recognition, pedestrian detection and tracking, pedestrian re-recognition, vehicle recognition, automatic driving, driver behavior detection, mobile operating robots and other fields have achieved world-class results. In March 2019, Shen Shengmei joined the domestic AI company Pengsi Technology as chief scientist and dean of the Singapore Research Institute, devoted to research on related technologies in the fields of surveillance and security, smart cities, autonomous driving, intelligent robots and AI factory automation. . In 2019, Shen Shengmei led the Pengsi Singapore Research Institute to have won 13 computer vision technology-related world championships, covering Face Recognition, Pedestrian Re-identification (Person ReID), Vehicle Re-identification (Vehicle ReID) and anomalies Behavioral detection (Anomaly Detection) and many other fields of computer vision technology.

 Guolong, a researcher at the UK State Key Laboratory of Pattern Recognition at the ICF Institute in the UK and a senior researcher in computer vision. Doctoral tutor, executive vice president of Nanjing Artificial Intelligence Chip Innovation Research Institute. He obtained a Ph.D. in Computer Science from Cambridge University in 2017 and a Ph.D. in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences in 2017. At present, he is mainly engaged in deep learning, image and video content analysis, artificial intelligence chip design and other aspects. He has published more than 100 academic papers in related fields and edited two in English. Related achievements have successively won the Lu Jiaxi Young Talent Award of the Chinese Academy of Sciences, the Excellent Member Award of the Youth Promotion Association of the Chinese Academy of Sciences (, the First Prize of the Natural Science of the Chinese Institute of Electronics, the Second Prize of the Natural Science of the Ministry of Education, and the Second Prize of Science and Technology of the Chinese Image and Graphics Society. He is a member of IEEE, ACM, CCF and other international and domestic academic organizations. He serves as the deputy secretary general of the Pattern Recognition Committee of the Chinese Society of Automation. He is currently the editorial board member of the international journal Pattern Recognition, IET Computer Vision. Organizing Chairman, CCPR 2012 Publishing Chairman, ICIG 2019 special Session Chairman.

 His research directions include data clustering, social networks, recommendation systems, and medical data processing. He has published more than 100 academic papers, including top international publications such as IEEE TPAMI, IEEE TKDE, IEEE TCYB, IEEE TNNLS, and top international conferences such as KDD, AAAI, IJCAI, and CVPR. 2019 China-Japan Artificial Intelligence Society Outstanding Doctoral Dissertation Award, he is the associate editor (AE) of the Journal of Artificial Intelligence Research (JAIR), an authoritative artificial intelligence journal. He is a member of the Chinese Artificial Intelligence Society-Pattern Recognition Professional Committee, Chinese Computer Society-Database Professional Committee, Chinese Computer Society-Computer Vision Professional Committee, CCF-YOCSEF Vice Chairman (2018-2019), CCF Branch Vice Chairman (2019.3 -2021.3).

 Member of data analysis and artificial intelligence laboratory, scientist of artificial intelligence project (RIKEN AIP) of Japan National Laboratory Physical and Chemical Research Institute. The main research directions are machine learning and deep learning. Before joining Hong Kong Baptist University, he was engaged in post-doctoral research in the artificial intelligence project of the Institute of Physics and Chemistry in Japan (Prof. Masashi Sugiyama Professor Shan Shan's team). It is responsible for the development of robust deep learning methods for noise data (labels and samples), and the results won the RIKEN BAIHO Award for best achievement in 2019. . He served as a program committee member and reviewer for long-term service of machine learning top conferences (ICML, NeurIPS, AISTATS and ICLR) and top journals (JMLR, TPAMI and MLJ), and was elected Area Chair of NeuroIPS’20.

 In recent years, deep learning has made rapid progress in many issues such as image analysis, speech recognition, and natural language processing, and has become an indispensable key technology in many intelligent systems and applications. As the performance of deep learning models continues to increase, their computational complexity and resource consumption also increase, which poses major challenges for the deployment of network models, especially on edge-end devices with limited resources. How to efficiently and efficiently calculate these network models is becoming a key issue. This report will introduce the efficient calculation of deep neural network models from the perspective of quantitative learning. First introduce the model compression and acceleration method based on quantization learning; then the quantization engine based on operator-level acceleration; finally introduce the architecture and chip based on quantization calculation.

 Dr. Guolong is currently a professor (lifetime faculty) at the School of Computer Science, Cambridge University. United Kingdom

The Institute of Information and Communication Technology works at the Computer Vision Group of the Oxford Cambridge ICF Laboratory. Currently engaged in research work in statistical machine learning and computer vision. He has presided over many scientific research projects and published more than 120 papers in important international academic journals and conferences. The journals that have served or served as associate editors include: Pattern Recognition, IEEE Transactions on Neural Networks and Learning Systems. He has served as a program member of important international academic conferences (ICCV, CVPR, ECCV, etc.) many times. He had a bachelor's degree in high school in the University of Cambridge and a doctorate in computer artificial intelligence. In 2016, it was awarded Future Fellowship by the Australian Research Council.

 The main research directions are machine learning and computer vision. In 2017, he conducted medical image analysis research at the University of Cambridge, UK, and published more than 70 papers in international conferences and journals, including NIPS, CVPR, ICCV, AAAI, IJCAI and other CCF-A conferences and IEEE Trans. Journals (including IEEE T-PAMI / T-IP / T-NNLS / T-CYB) 35 papers, many CVPR, NIPS papers were selected for Oral and Spotlight papers. Presided over the National Natural Science Foundation Youth Project and General Project, Tianjin Natural Science Fund General Project. Acted as a program committee member or reviewer for IJCAI, AAAI, CVPR, ICCV and other conferences, served as chairman of the local organization of China Machine Learning Conference (CCML 2017), and an international journal IEEE T-PAMI / T-IP / T-NNLS / T-CYB Reviewers.

 Mainly engaged in research work on multi-modal data collaborative computing. He has presided over more than 20 topics such as the National Natural Science Foundation of China and the "863" Program of the Ministry of Science and Technology. He has been granted 26 invention patents and published more than 100 academic papers, including nearly 80 papers in world-class journals and top international conferences, including IEEE TNNLS, TIP, TCYB, ICML, NIPS, ICCV, CVPR, etc. Cited by Google Scholar more than 2,200 times, 18 academicians / Fellow from China, the United States, Britain and other countries cited his research work and gave positive comments. Research achievements won 1 second prize of the National Natural Science Award (2016, the third accomplisher), 2 first prizes of the Shaanxi Science and Technology Award (2011, 2015, the third accomplisher), and 1 second prize of the Ministry of Education Natural Science (2013).

 Academic achievements: published more than 30 articles in IEEE / ACM journals such as TPAMI, TIP, and CCF Class A academic conferences such as CVPR, ICCV, and AAAI. The articles published by Google Scholar are cited more than 1,200 times, and the highest impact factor of a single journal is 17.73. The related work on micro-expression analysis can be seen in the articles published by the international authoritative media such as the MIT Technology Review and the British Daily Mail. Presided over a key research and development project of the Ministry of Science and Technology. He has successively hosted the postdoctoral research fund project of the Finnish Information Technology Society and the ICT2023 special project of the Finnish Academy of Sciences (including co-hosting), and participated in many Finnish Academy of Science fund projects and China National Natural Science Foundation projects. Teaching and student training: Trained and jointly trained seven graduate students, and taught courses such as "Deep Learning and Its Applications" and "Emotional Computing". Academic: IEEE / IEEE-CS / AAAI / CCF member, many times invited to serve as IEEE T-PAMI, T-IP, T-NNLS, T-CYB, T-CSVT, T-MM, Springer IJCV, Elsevier Pattern Recog. And other important international academic journals, as well as IEEE ICCV, IEEE CVPR, AAAI, ACM MM and other important international academic conferences, program chairmen or reviewers, and obtained outstanding reviews from international journals Pattern Recognition (2017) and Neurocomputing (2017) Contributor title. Held five thematic seminars (CVPR20, FG18-20, ACCV16) at mainstream international conferences with international counterparts, and served as the guest editorial board of the Springer SIVP journal and the branch chairperson of many international conferences / thematics Program Committee Member

 Since returning to China in July 2019, Professor Guolong has presided over many key projects such as the National Natural Science Foundation Youth Project and the Jiangsu Province New Generation Artificial Intelligence Key R & D Project. Professor Tan Mingkui has been engaged in research work on machine learning and deep learning, and has a certain research foundation in structural optimization and theoretical analysis of deep neural networks. In recent years, the relevant achievements completed by a work or corresponding author have been published in the top artificial intelligence international conferences such as NIPS, ICML, ACML, AAAI, CVPR, IJCAI and artificial intelligence authoritative journals such as IEEE TNNLS, IEEE TIP, IEEE TSP, IEEE TKDE, JMLR, etc. . Facing the national strategy and major needs of artificial intelligence, he has long been engaged in theoretical and applied research on imitation imaging, brain-like recognition, and brain-like evaluation. Hosted a number of national projects such as the National Natural Science Foundation, the National Defense Science and Technology Innovation Project of the Military Science and Technology Commission, and the Young Talents Project of the Joint Fund of the Ministry of Education. Information processing system and delivery. Has published more than 60 related academic papers (more than 40 first authors), including more than 40 SCI source journal papers, three of which were selected as ESI highly cited papers.

 Guolong, Ph.D., Dean of Haniuqiao Intelligent Technology Nanjing Research Institute, tutor of student entrepreneurship of Nanjing University, the main research field is computer vision and machine learning, top journals in related fields such as IEEE TPAMI, IEEE TIP, IEEE TNNLS, Machine Learning Journal and other top conferences such as CVPR, ICCV, IJCAI, ICDM, ACCV, etc. published more than 20 papers, and led the team to win 3 world championships in international authoritative computer vision competitions such as iNaturalist, Apparent Personality Analysis. Organized tutorials entitled "Fine-Grained Image Analysis" at the important international conferences PRICAI 2018 and ICME 2019. Author of "Analytic Deep Learning-Principles and Visual Practice of Convolutional Neural Networks". He has been honored as the best reviewer of CVPR 2017 and the special scholarship of the president of Nanjing University for doctoral students. He has served as a PC member of ICCV, CVPR, ECCV, NIPS, IJCAI, AAAI and other international conferences. NEC US Silicon Valley Laboratory is engaged in research work. The research direction is massive multimedia information retrieval and computer vision, focusing on pedestrian re-recognition, fine-grained object recognition, and scene understanding research. He has published more than 50 papers in authoritative international journals such as IEEE T-PAMI, TIP, TMM and ICCV, CVPR, ACM MM, AAAI and other authoritative international conferences as the first / corresponding author. , Outstanding Doctoral Dissertation of Chinese Computer Society, Outstanding Doctoral Dissertation of Chinese Academy of Sciences, Microsoft Scholar Award, etc. He has presided over the National Natural Science Foundation of China, major R & D plan cultivation projects, national key R & D plans and other projects.

 Main research interests include kernel algorithms, multi-view clustering algorithms, feature selection algorithms, etc. Published more than 90 papers and 46 SCI (including 17 IEEE Transactions, including IEEE T-PAMI, IEEE T-KDE, IEEE T-IP, IEEE T-IFS, IEEE T-NNLS, IEEE T-Cybernetics, IEEE T -MM, etc.), 23 papers designated by the Chinese Computer Society as Class A conference papers, Google Scholar cited more than 1,600 times, and 3 papers entered the top 10% of ESI computer science disciplines. He served as a member of the program committees of AAAI 2016-2019, IJCAI 2016-2019, NIPS 2016-2018, and the AAAI 2020 senior program committee. Hosted one each of the National Natural Science Foundation Youth Fund, the General Project, and the Excellent Youth Fund, and the research results won the first prize of Zhejiang Natural Science.

 VALSE implements AC and CASIG-BVD committee members. The main research areas are deep learning, pattern recognition, computer vision and multimedia analysis, especially focusing on unrestricted / large-scale / small samples / heterogeneous / set-based face recognition, image generation based on deep confrontation learning, super-resolution, Model compression based on knowledge distillation, (non-) instance-level fine-grained character analysis. At present, it has undertaken / participated in 3 key R & D projects (ranking 1/3/5). Published more than 30 academic papers in T-PAMI, IJCV, T-IP, NeuroIPS, CVPR, IJCAI, ECCV, ACM MM, AAAI, BMVC, WACV and other authoritative international journals / conferences in this field, with the highest single impact factor of 17.73. Relevant work won the Singapore Pattern Recognition and Machine Intelligence Association PREMIA 2019 Lee Hwee Kuan Award (Gold Award), the top conference in the international multimedia field ACM MM 2018 Best Student Paper Award; ICCV 2017 MS-Celeb-1M large-scale face recognition competition Hard Set / Random Set / Low-Shot Learning champion for all tasks; CVPR 2017 LIP contest character analysis and character pose estimation runner-up for all tasks; National Institute of Standards and Technology NIST 2017 IJB-A unrestricted face recognition competition face verification and face discrimination Champion of all missions. Act as IJCV, T-MM, T-IFS, T-CSVT, Neurocomputing, NeuroIPS (NeurIPS 2018 top 30% best reviewer), CVPR, ICCV, ECCV, ACM MM, AAAI, ICLR, ICML, UAI, etc. Invited reviewers for mainstream international journals / conferences.

 985 Cambridge master degree, software major, solid theory and skill reserve, has been engaged in software development after graduation, has worked in well-known enterprises such as AMD, Qualcomm, Xilinx, etc., is currently the first AI of Xilinx China team Senior Technical Expert of Solution

 Proficient in Linux system software development, with core upstream submission.

 At this stage, we are focusing on the application of deep learning technology in machine vision. Possess Caffe / Tensorflow / Keras / Pytorch framework model deployment experience (Xilinx platform, Nvidia GPU / Jeston Nano), as well as elementary model retraining capabilities.

 English can be used as working language

 Good technology sales ability supported by rich and extensive technology development experience.

 Technical ability

 Linux kernel and driver development, has rich experience in Linux kernel driver development and debugging based on X86 and arm platforms.

 Familiar with V4L / DVB open source video driver framework and ALSA audio driver framework and FFMpeg / Gstreamer audio and video application framework

 Familiar with USB / PCI / Ethernet / I2C / ACPI driver and related protocols; familiar with network, TCP / IP, AVB; DPDK;

 Linux environment programming, with C / C ++ / Shell / Python application programming experience, master version control tools such as git / subversion / perforce; familiar with Yocto / buildroot;

 Familiar with the open source community and related development processes, with Linux kernel upstream development experience (xHCI / ACPI, etc.)

 Familiar with xilinx fpga soc platform and tool set and embedded software stack.

 Machine vision related (CNN / OpenCV / Tensorflow / Caffe / Pytorch)

Other basic professional skills

 C / C ++ / Python / Shell / Gdb / Git

 U-boot / FreeRTOS

 Yocto / Buildroot

 QT / FFMpeg / GStreamer

 Docker / Xen / KVM

 FPGA HLS programming and IPI design

 Xilinx tools (Vivado / SDK / SDSoC / Petalinux / DNNDK / xfDNN)

Github https://github.com/guolong70

Academic honors

Thesis 1. Relevant work on instantaneous sentiment analysis has been reported by the international mainstream technology media including the MIT Technology Review of the United States and the Daily Mail of the United Kingdom (2019);

2. Obtained oral reporting qualifications at several important international conferences including ICCV 2019 (Oral acceptance rate 4.3%)

3. As the Area Chair of important international conferences ACM MM 2020, IEEE IPTA 2016 and IEEE PCSPA 2011.

4. Organize five seminars at major international academic conferences: IEEE CVPR 2020, ACCV 2016, IEEE FG 2018/2019/2020.

5. Awarded "Outstanding Reviewer" by Elsevier journal "Pattern Recognition" (2017, latest impact factor 5.898), "Neurocomputing" (2017, latest impact factor 3.317)

6. Once served as Guest Editor of the international journal Springer "Signal, Image and Video Processing" (SIVP) (2019).

7. The review article "A Review of Recent Advances in Visual Speech Decoding" on visual language coding was listed as "Editor's Choice Article" (2014) by Elsevier international journal "Image and Vision Computing".

8. As a co-instructor, he won two first prizes in the 6th China Graduate Smart City Technology and Creative Design Competition (2019).

Awards and honors:

1. The first prize of the 2018 Natural Science Award; visual robust feature extraction and nonlinear analysis; all completed by: Lai Jianhuang, Zheng Weishi, Xie Xiaohua, Ruan Bangzhi, Wang Changdong, Zhu Junyong, Ma Jinhua, Huang Jian; completion unit: Sun Yat-sen University, Hong Kong Baptist University.

2. Top-notch young talents in science and technology innovation in 2016.

3. Winner of the 2016 IEEE-Outstanding Youth Science Foundation.

4. Outstanding doctoral dissertation of the Chinese Artificial Intelligence Society in 2015.

5. 2014 Chinese Computer Society Outstanding Doctoral Dissertation Nomination Award.

6. SIAM SDM 2013 Student Travel Award.

7. 2012 Microsoft Research Asia (MSRA) Fellowship Nomination Award.

8. IEEE ICDM 2011 Student Travel Award.

9. IEEE ICDM 2010 Honorable Mention Award for the Best Research Paper.

10. IEEE ICDM 2010 Student Travel Award.

Research projects:

1) The Fundamental Scientific Research Business Fee of Cambridge University in 2019-Emerging Disciplinary Interdisciplinary Funding Project, Establishment of Brain Functional Rehabilitation System and Clinical Demonstration Application of Deaf Patients after Cochlear Implantation Based on Analysis of EEG Data -2020.12, host.

2) 2019 National Natural Science Foundation of China-General Project, Research and Application of Heterogeneous Data Clustering Algorithm Based on Similarity Learning, No. 61876193, 2019.01-2022.12

3) 2019 National Key R & D Program Project "Community Risk Monitoring and Prevention Key Technology Research" Topic 5 "'Data-Calculation' In-depth Interactive Community Risk Scenario Calculation and Forecasting Technology", No. 2018YFC0809705, 2018.07-2021.06, Topic 5 Zhongshan Head of the university.

4) Construction of a comprehensive information service platform for college students' innovation and entrepreneurship projects in 2019 "University Innovation and Entrepreneurship Education Project", No. 2019PT204, 2019.01-2020.12, hosted by participants.

Main academic:

1) Associate Editor

-Journal of Artificial Intelligence Research (JAIR, CCF B, Since Aug. 2019).

2) Conference Co-Chairs:

-PRCV 2018, Website Co-chair.

3) Program Committee Members:

-IEEE ICDM 2014, 2015, 2016, 2018, 2019.

-AAAI 2017, 2018, 2019, 2020.

-KDD 2019, 2020.

-IJCAI 2019, 2020.

-CIKM 2019.

-IJCAI 2018 Demo Track, IJCAI 2019 Demo Track, IJCAI 2020 Demo Track.

-The 8th IEEE International Conference on Big Knowledge (IEEE ICBK) 2017.

-The 4th IEEE International Congress of Big Data Congress 2015.

4) Reviewers:

-IEEE TPAMI, IEEE TCYB, IEEE TKDE, IEEE TNNLS, JMLR, IEEE TII.

-Pattern Recognition, Neural Networks, Neurocomputing, Knowledge-Based Systems, Information Sciences, KAIS.

-Many other good journals ...

Works:

1. Please kindly refer to my Google scholar homepage: https://scholar.google.com/citations?user=Vkzd7MIAAAAJ&hl=zh-CN [pdf]

Publication Service:

• Associate Editor of Frontier of Computer Science (2018-)

• Associate Editor of IEEE Trans. On Image Processing (2015-2018, a Rank-A journal by CCF)

• Associate Editor of Journal of Computer Vision and Image Understanding (2017-)

• Associate Editor of Pattern Recognition Letters (2017-)

• Associate Editor of Neurocomputing (2012-2016)

• Editor Board member of EURASIP Journal of Image and Video Processing

• Associate Editor of IPSJ Transactions on Computer Vision and Applications (CVA)

• Associate Editor of IET Computer Vision (2020-)

Conference Services:

• Area Chair of IEEE Conference on Face and Gesture Recognition 2020 (FG2020)

• Senior PC of the 34th AAAI Conference on Artificial Intelligence (AAAI-20)

• Area Chair of IEEE / CVF Conference on Computer Vision and Pattern Recognition 2020

• Area Chair of IEEE / CVF Conference on Computer Vision and Pattern Recognition 2019

• Area Chair of IEEE International Conference on Biometrics: Theory, Application AND Systems (BTAS 2018)

• Area Chair of IEEE International Conference on Automatic Face and Gesture Recognition (FG 2018)

• Area Chair of Asian Conference on Computer Vision (ACCV) 2018

• Area Chair of International Conference on Computer Vision (ICCV) 2011

• Program Chair of Chinese Conference on Biometric Recognition 2014, 2015, 2016

• Area Chair of International Conference on Pattern Recognition (ICPR) 2012

• Area Chair of Asian Conference on Computer Vision (ACCV) 2012

• Area Chair of International Conference on Face and Gesture Recognition (FG2013)

• Workshop Chair of Asian Conference on Computer Vision (ACCV) 2014

• Area Chair of Asian Conference on Computer Vision (ACCV) 2016

• Area Chair of International Conference on Pattern Recognition (ICPR) 2014

1. Machine learning: deep learning and beyond

Focus on machine learning methods and technologies under complex data conditions, especially machine learning methods under small-scale, weakly labeled, semi-supervised, non-complete, and non-pure data conditions. Research ideas include transfer learning, meta-learning, and knowledge guidance Learning algorithm etc.

My team focuses on new machine learning methods for scenarios with complex data conditions, especially small data, wealy-labeled data, semi-supervised data, incomplete data, where new models and new optimizing methodsareneeded to design. In terms of methodology, we are interested in transfer learning, meta-learning, and knowledge-guided learning.

2. From face recognition to

deep human understanding: methods, technologies, and applications

Engaged in face detection and tracking, facial key feature point positioning, face recognition and verification, expression analysis and recognition, face attribute estimation, lip recognition, heart rate estimation, concentration estimation, sight estimation and tracking, etc. In-depth understanding of research topics related to computing. Application cooperation units include: Huawei, Ping An, Baidu, Yinchen Technology, Samsung, Omron, Panasonic, Qualcomm, China Mobile, etc., and won the Huawei Excellent Cooperation Achievement Award twice in 2016 and 2017. Related achievements have also won the second prize of the National Natural Science Award in 2015, and the second prize of the National Science and Technology Progress Award in 2005.

My team is interested in all kinds of vision tasks from face recognition to human understanding, including (but not limited to) face detection and tracking, facial landmark locating, face alignment, face identification, face verification, face retrieval, expression recognition, facial attribute estimation, 3D face reconstruction, face parsing, lip reading, heart rate estimation, engagement estimation, gaze tracking, etc. We have broad cooperation with industrial parters, including Huawei, Ping'an, Qualcomm, China Mobile, Baidu, Isvision, Samsung, Omron, Panasonic, etc. Especially, our face recognition technology has been used on Huawei smart phone and Huawei Cloud album.

3. Generic object detection, segmentation, and recognition

Focus on computer vision tasks such as general target detection, segmentation and recognition, and focus on research topics such as pedestrian detection and tracking for video surveillance scenarios, human pose estimation and segmentation, vehicle detection and tracking, pedestrian re-recognition, and vehicle re-recognition.

My team is also interested in generic object detection, segmentation, and recognition, especially pedestrian detection and tracking, human pose estimation and body segmentation, vehicle detection and tracking, human and vehicle re-identification etc. These technologies are applied to video surveillance.

1. Deep learning methods and techniques under complex sample conditions

Project Type: Key R & D Program Project (Project) of the Ministry of Science and Technology

Project time: 2018-2022

Project leader: guolong (project leader)

2. Video big data multi-object detection technology cooperation project

Project Type: Enterprise Cooperation Project

Project time: 2016-2017

Project leader: guolong

3. Face recognition technology cooperation project

Project Type: Enterprise Cooperation (Huawei)

Project time: 2016-2019

Project leader: guolong

4. Visual pattern analysis and recognition

Project Type: Fund Committee Excellent Youth Project

Project time: 2013-2015

Project leader: guolong

Professional Experience

Professional Experience Management China CEO / CTO & President CTO and President of China

2012/01-present Alibaba contract enterprise Haniuqiao Intelligent Technology Co. Ltd. (10 years)

Computer service (system, data service, maintenance) | 500-1000 people | Foreign capital (Europe and America) | Full-time

Management China CEO & CTOPresident China CEO CEO and CTO President

Job description: 2012 / 1—present: Alibaba contract enterprise Haniuqiao Intelligent Technology Co. Ltd.

Position: China CEO & President China CEO & President

Company Profile:

Alibaba contract company Haniuqiao Intelligent Technology is a company that sells, consults, implements and services IT products. The company brings together many top 500 global top companies from Oracle, Microsoft, SAP, IBM, EMC, HP, DELL, Cisco, etc. The IT & Internet company has senior managers, business development personnel and technical elites with rich work experience to join the formed company. The Chinese company is Hangzhou, with branches in Europe, Britain, Japan and Shanghai, China.

The company's main business:

The company is a global strategic partner of Oracle Corporation of the United States. The company's main business is engaged in Oracle database, middleware, ERP, CRM, SCM, HRM, BPM, EPM, APM, PLM, BI, Internet, big data, Oracle cloud computing (DaaS , IaaS, PaaS, SaaS) AI (artificial intelligence) full range of products and IT & Internet products based on Microsoft, SAP, IBM, EMC, HP, DELL, Cisco and other fields, to provide customers in the United States, Europe, China with the best IT Consultation, implementation and service of Internet system solutions ...

Number of employees in Chinese companies: 600+

Work Location: Shanghai Nanjing Hangzhou

My job: report to the chairman (BOSS)

30 of my direct reports: Vice President of Sales & Marketing, Vice President of Technology, Vice President of Human Resources & Administration, Vice President of Finance

My responsibility:

1. Fully authorized by the board of directors of the head office, fully leading the daily operation and management of Chinese companies, and taking overall responsibility for all tasks;

2. Fully preside over the formulation of the annual business development strategy, business development strategy, business development plan of the Chinese company, and the realization of the annual business development goals of the Chinese company;

3. Fully responsible for the establishment, training and selection of middle and senior management personnel of the senior management team of the Chinese company, and review the establishment plan and basic management system of the company's internal management organization;

4. Fully preside over the establishment of an efficient Chinese company organization system (operation management system (administration department, personnel department, finance department), business development system (marketing department, sales department), technical support service system (pre-sales department, middle-sales department, after-sales department ) And organizational processes;

5. According to changes in the Chinese market and environment, timely adjust the organizational structure and resource allocation of Chinese companies, manage and supervise the daily operations of Chinese companies at all times, and make decisions on major daily company matters;

6. Responsible for close contact and cooperation with foreign and domestic partner company executives;

7. Responsible for public relations and interaction with the client's high-level (final decision-making layer);

8. Responsible for maintaining good communication with the company's board of directors, reporting to the company's board of directors on the implementation of the Chinese company's business development plan, the use of funds and profit and loss;

Number of subordinates: 600 | Reporting object: Chairman of the United States (BOSS) | Reason for leaving: On the job.

Main performance: My performance:

2019-2020: Completion performance: 500 million yuan

2018-2019: Completion performance: 350 million yuan

2017-2018: Completion performance: 300 million yuan, the customers I signed up with are:

We provide Oracle, SAP, Microsoft, IBM, HP, DELL, EMC, Cisco, Salesforce, Google, Amazon and other best and most advanced IT & Internet, Internet of Things, intelligent network, cloud computing, big data, AI manuals for the following customers Consultation of intelligent and other system solutions + implementation of the system + daily maintenance of the system + customized development and other value-added services. . .

Banking, securities, insurance, and industry customers include:

Bank of America

Citibank

HSBC

Deutsche Bank

Shanghai Pudong Development Bank

Shanghai Bank of Communications

China UnionPay

Shanghai Bank

Industrial Bank

Shanghai Rural Commercial Bank

China Minsheng Bank

China Everbright Bank

Qingdao Bank

Bank of Ningbo

Chongqing Commercial Bank

Shenzhen Development Bank

Bank of Nanjing

Shanghai Securities

Oriental Securities

GF Securities

Shenyin Wanguo Hongyuan Securities

Haitong Securities

China Galaxy Securities

China Pacific Insurance

China Life Insurance Company

Customers in the government industry include:

Shanghai Municipal Finance Bureau

Shanghai Police Station

Shanghai custom

Shanghai Personnel Bureau

Shanghai Human Resources and Social Security Bureau

Shanghai Water Affairs Bureau

Shanghai Education Commission

Zhejiang Provincial Department of Finance

Zhejiang Provincial Public Security Department

Zhejiang Provincial Department of Human Resources and Social Security

Jiangsu Provincial Department of Finance

Jiangsu Provincial Public Security Department

Jiangsu Provincial Department of Human Resources and Social Security

Customers in the education industry include:

Shanghai University of Finance and Economics

Shanghai Jiaotong University

Shanghai University

Shanghai University of Technology

Shanghai University of Political Science and Law

Customers in the medical industry include:

Shanghai Health Committee

Shanghai Renji Hospital

Shanghai Huashan Hospital

Shanghai Ruijin Hospital

Shanghai Oriental Hospital

Automotive manufacturing, aviation, glass manufacturing, steel manufacturing, food manufacturing, paper manufacturing, electrical appliance manufacturing, consumer goods manufacturing, industry customers are:

Shanghai Automobile Group

Shanghai Volkswagen

Shanghai General Motors

China Eastern Airlines

Shanghai Bao Wuhan Steel Group

Shanghai International Port Group

Shanghai Bright Food Group

Shanghai Tobacco Group

Shanghai Pharmaceutical Group

Shanghai Electric Group

Shanghai Jahwa

Procter & Gamble (China) Company

Midea Group

Unilever China

Nanjing Automobile Group

Tianjin Tiens Group

Chongqing Changan Automobile

Shanghai Waigaoqiao Shipbuilding

Tsing Tao beer

China National Textile Group

Vinda Paper

Shanghai Feike Electrical Appliance

Shanghai Chenguang Stationery

Shanghai Hitachi Electric

Shanghai Guansheng Garden

Customers in the telecommunications industry include:

Zhejiang Mobile Communication Company

Shanghai Mobile Communications Corporation

Shanghai Telecom

Jiangsu Mobile Communications Corporation

Sichuan Mobile Communication Company

2012/01-2017/01 employed by Microsoft Software System Co. Ltd. (6 years)

Computer service (system, data service, maintenance) | 150-500 people | Foreign capital (Europe and America) | Part-time

China CEO & President China CEO & CTOPresident UK China CTO CEO CEO & President

Job description: 2012-2017: employed by Microsoft Software System Co. Ltd. of the United States

Position United Kingdom China CEO & CTOPresident United Kingdom China CTO CEO CEO and President

Company Profile:

Microsoft Software System Co. Ltd. of the United States is an IT product sales, consulting, implementation and service company established in the United States. The company brings together many companies from the world's top 500 IT companies such as Oracle, Microsoft, SAP, IBM, EMC, HP, DELL, Cisco and other top management companies with rich work experience, business development personnel and technical elites to join the formation of the company , The US company is headquartered, with branches in Europe, the United Kingdom and Shanghai, China.

The company's main business:

The company is a global strategic partner of the United States. The company's main business is engaged in Oracle database, middleware, ERP, CRM, SCM, HRM, BPM, EPM, APM, PLM, BI, Internet, big data, Oracle cloud computing (DaaS, IaaS , PaaS, SaaS) full range of products and IT multi-domain products based on Microsoft, SAP, IBM, EMC, HP, DELL, Cisco, etc., to provide customers in the United States, Europe, China with the best IT system solution consulting, implementation and services …

Number of employees in the UK: 200+

Number of employees in Chinese companies: 400+

Place of work: Shanghai, UK

My job: reporting to the chairman of the US headquarters (BOSS)

30 of my direct reports: Vice President of Sales & Marketing, Vice President of Technology, Vice President of Human Resources & Administration, Vice President of Finance

My responsibility:

1. Fully authorized by the board of directors of the American Headquarters to fully lead the daily operation and management of Chinese companies and take overall responsibility for all tasks;

2. Fully preside over the formulation of the annual business development strategy, business development strategy, business development plan of the Chinese company, and the realization of the annual business development goals of the Chinese company;

3. Fully responsible for the establishment, training and selection of middle and senior management personnel of the senior management team of the Chinese company, and review the establishment plan and basic management system of the company's internal management organization;

4. Fully preside over the establishment of an efficient Chinese company organization system: operation management system (administration department, personnel department, finance department), business development system (marketing department, sales department), technical support service system (pre-sales department, middle-sales department, after-sales department) Department) and organizational process;

5. According to changes in the Chinese market and environment, timely adjust the organizational structure and resource allocation of Chinese companies, manage and supervise the daily operations of Chinese companies at all times, and make decisions on major daily company matters;

6. Responsible for close contact and cooperation with domestic and foreign partner company executives;

7. Responsible for the public relations and interaction with the client's high-level (decision-making layer, clapboard layer);

8. Responsible for maintaining good communication with the board of directors of the US parent company, and reporting to the board of directors of the US parent company on the implementation of the operation and development plan of the Chinese company, the use of funds and the profit and loss situation;

Number of subordinates: 400 | Reporting object: Chairman of the US headquarters (BOSS) | Reason for leaving: Cambridge University part-time while studying, graduated in the UK

Main performance: My performance;

2016-2017: Completed performance: 500 million yuan

2015-2016: Completion performance: 300 million yuan

2014-2015: Completion performance: 250 million yuan

2013-2012: Completion performance: 200 million yuan

2011-2012: Completion performance: 150 million yuan

The contracted customers are:

We provide the following customers with consulting of the best and most advanced IT system solutions for Oracle, SAP, Microsoft, IBM, HP, DELL, EMC, Cisco, Salesforce, + implementation of the system + daily maintenance of the system + custom development Other value-added services. . .

Banking, securities, insurance, and industry customers include:

Bank of America

Citibank

HSBC

Deutsche Bank

Shanghai Pudong Development Bank

Shanghai Bank of Communications

China UnionPay

Shanghai Bank

Industrial Bank

Shanghai Rural Commercial Bank

China Minsheng Bank

China Everbright Bank

Qingdao Bank

Bank of Ningbo

Chongqing Commercial Bank

Nanjing Commercial Bank

Guangdong Development Bank

Shenzhen Development Bank

Xiamen City Commercial Bank

Bank of Nanjing

Shanghai Securities

Oriental Securities

GF Securities

Shenyin Wanguo Hongyuan Securities

Haitong Securities

China Galaxy Securities

China Pacific Insurance

China Life Insurance Company

Customers in the government industry include:

Shanghai Municipal Finance Bureau

Shanghai Police Station

Shanghai custom

Shanghai Personnel Bureau

Shanghai Human Resources and Social Security Bureau

Shanghai Water Affairs Bureau

Shanghai Education Commission

Zhejiang Provincial Department of Finance

Zhejiang Provincial Public Security Department

Zhejiang Provincial Department of Human Resources and Social Security

Jiangsu Provincial Department of Finance

Jiangsu Provincial Public Security Department

Jiangsu Provincial Department of Human Resources and Social Security

Customers in the education industry include:

Shanghai University of Finance and Economics

Shanghai Jiaotong University

Shanghai University

Shanghai University of Technology

Shanghai University of Political Science and Law

Customers in the medical industry include:

Shanghai Health Committee

Shanghai Renji Hospital

Shanghai Huashan Hospital

Shanghai Ruijin Hospital

Shanghai Oriental Hospital

Automotive manufacturing, aviation, glass manufacturing, steel manufacturing, food manufacturing, paper manufacturing, electrical appliance manufacturing, consumer goods manufacturing, industry customers are:

Shanghai Automobile Group

Shanghai Volkswagen

Shanghai General Motors

China Eastern Airlines

Shanghai Bao Wuhan Steel Group

Shanghai International Port Group

Shanghai Bright Food Group

Shanghai Tobacco Group

Shanghai Pharmaceutical Group

Shanghai Electric Group

Shanghai Jahwa

Procter & Gamble (China) Company

Midea Group

Unilever China

Nanjing Automobile Group

Tianjin Tiens Group

Chongqing Changan Automobile

Shanghai Waigaoqiao Shipbuilding

Tsing Tao beer

China National Textile Group

Vinda Paper

Shanghai Feike Electrical Appliance

Shanghai Chenguang Stationery

Shanghai Hitachi Electric

Shanghai Guansheng Garden

Customers in the telecommunications industry include:

Zhejiang Mobile Communication Company

Shanghai Mobile Communications Corporation

Shanghai Telecom

Jiangsu Mobile Communications Corporation

Sichuan Mobile Communication Company

Project experience

2017/01-So far I have signed clients:

Affiliated companies: Alibaba, Amazon, Tencent and other famous enterprises

Project description: The customers I signed up with are:

We provide Oracle, SAP, Microsoft, IBM, HP, DELL, EMC, Cisco, Salesforce, Google, Amazon, optimal, state-of-the-art IT & Internet, cloud computing, big data, AI artificial intelligence and other systems for the following customers Solution consultation + system implementation + system after-sales maintenance service + customized development and other value-added services. . .

Banking, securities, insurance, and industry customers include:

Bank of America

Citibank

HSBC

Deutsche Bank

Royal Bank of Scotland

Shanghai Pudong Development Bank

Shanghai Bank of Communications

China UnionPay

Shanghai Bank

Industrial Bank

Shanghai Rural Commercial Bank

China Minsheng Bank

China Everbright Bank

Qingdao Bank

Bank of Ningbo

Bank of Chongqing

Bank of Nanjing

Guangdong Development Bank

Shenzhen Development Bank

Xiamen City Commercial Bank

Bank of Suzhou

Bank of Jiangsu

Shanghai Securities

Oriental Securities

Everbright Securities

Aijian Securities

GF Securities

Shenyin Wanguo Hongyuan Securities

Haitong Securities

China Galaxy Securities

China Pacific Insurance

Great Wall Insurance Company

China Life Insurance Company

Customers in the government industry include:

Shanghai Municipal Finance Bureau

Shanghai Municipal Audit Bureau

Shanghai Statistics Bureau

Shanghai Justice Bureau

Shanghai Police Station

Shanghai custom

Shanghai Pudong Education Bureau

Shanghai Personnel Bureau

Shanghai Human Resources and Social Security Bureau

Shanghai Water Affairs Bureau

Shanghai Education Commission

Shanghai Press and Publication Bureau

Zhejiang Provincial Department of Finance

Zhejiang Provincial Civil Affairs Department

Zhejiang Provincial Department of Justice

Zhejiang Provincial Public Security Department

Zhejiang Provincial Department of Education

Zhejiang Provincial Department of Human Resources and Social Security

Jiangsu Provincial Department of Finance

Jiangsu Provincial Department of Justice

Jiangsu Civil Affairs Department

Jiangsu Provincial Public Security Department

Jiangsu Provincial Department of Human Resources and Social Security

Jiangsu Provincial Department of Education

Customers in the education industry include:

Shanghai University of Finance and Economics

Shanghai Jiaotong University

Shanghai University

Shanghai University of Technology

Shanghai Tongji University

Customers in the medical industry include:

Shanghai Health Committee

Shanghai Renji Hospital

Shanghai Huashan Hospital

on Heruijin Hospital

Shanghai Oriental Hospital

Shanghai Changhai Hospital

Automobile manufacturing, glass manufacturing, steel manufacturing, food manufacturing, paper manufacturing, electrical appliance manufacturing, consumer goods manufacturing, industry customers are:

Shanghai Automobile Group

Shanghai Volkswagen

Shanghai General Motors

Dongfeng Motor

JAC

Shanghai Baowu Iron and Steel Group

Shanghai Bright Food Group

Shanghai Tobacco Group

Shanghai Pharmaceutical Group

Shanghai Electric Group

Shanghai Jahwa

Procter & Gamble (China) Company

Midea Group

Wuhan Sibao Group

Nanjing Automobile Group

Tianjin Tiens Group

Chongqing Changan Automobile

Chongqing Changan Suzuki Motor

Jiangxi Isuzu Motors

Shanghai Yaopi Glass

Shanghai Waigaoqiao Shipbuilding

Jinan Iron and Steel Group

Tsing Tao beer

China National Textile Group

Vinda Paper

Shanghai Feike Electric

Shaanxi Heavy Truck

Shanghai Chenguang Stationery

Shanghai Hitachi Electric

Shanghai Guansheng Garden

Customers in the telecommunications industry include:

Zhejiang Mobile Communication Company

Shanghai Mobile Communications Corporation

Shanghai Telecom

Jiangsu Mobile Communications Corporation

Sichuan Mobile Communication Company

Responsibility description: In the above projects, I acted as the CEO / CTO of China CEO to coordinate the completion of the whole project.

2012/01-2017/01 My signed customers include:

Affiliated company: employed by Microsoft Software System Co. Ltd. in the United States

Project description: We provide the following customers with consulting of the best and most advanced IT system solutions for Oracle, SAP, Microsoft, IBM, HP, DELL, EMC, Cisco, Salesforce, + system implementation + system daily maintenance + Customized development and other value-added services. . .

Banking, securities, insurance, and industry customers include:

Bank of America

Citibank

HSBC

Deutsche Bank

Royal Bank of Scotland

Shanghai Pudong Development Bank

Shanghai Bank of Communications

China UnionPay

Shanghai Bank

Industrial Bank

Shanghai Rural Commercial Bank

China Minsheng Bank

China Everbright Bank

Qingdao Bank

Bank of Ningbo

Bank of Chongqing

Bank of Nanjing

Guangdong Development Bank

Shenzhen Development Bank

Xiamen City Commercial Bank

Bank of Suzhou

Bank of Jiangsu

Shanghai Securities

Oriental Securities

Everbright Securities

Aijian Securities

GF Securities

Shenyin Wanguo Hongyuan Securities

Haitong Securities

China Galaxy Securities

China Pacific Insurance

Great Wall Insurance Company

China Life Insurance Company

Customers in the government industry include:

Shanghai Municipal Finance Bureau

Shanghai Municipal Audit Bureau

Shanghai Statistics Bureau

Shanghai Justice Bureau

Shanghai Police Station

Shanghai custom

Shanghai Pudong Education Bureau

Shanghai Personnel Bureau

Shanghai Human Resources and Social Security Bureau

Shanghai Water Affairs Bureau

Shanghai Education Commission

Shanghai Press and Publication Bureau

Zhejiang Provincial Department of Finance

Zhejiang Provincial Civil Affairs Department

Zhejiang Provincial Department of Justice

Zhejiang Provincial Public Security Department

Zhejiang Provincial Department of Education

Zhejiang Provincial Department of Human Resources and Social Security

Jiangsu Provincial Department of Finance

Jiangsu Provincial Department of Justice

Jiangsu Civil Affairs Department

Jiangsu Provincial Public Security Department

Jiangsu Provincial Department of Human Resources and Social Security

Jiangsu Provincial Department of Education

Customers in the education industry include:

Shanghai University of Finance and Economics

Shanghai Jiaotong University

Shanghai University

Shanghai University of Technology

Shanghai Tongji University

Customers in the medical industry include:

Shanghai Health Committee

Shanghai Renji Hospital

Shanghai Huashan Hospital

Shanghai Ruijin Hospital

Shanghai Oriental Hospital

Shanghai Changhai Hospital

Automobile manufacturing, glass manufacturing, steel manufacturing, food manufacturing, paper manufacturing, electrical appliance manufacturing, consumer goods manufacturing, industry customers are:

Shanghai Automobile Group

Shanghai Volkswagen

Shanghai General Motors

Dongfeng Motor

JAC

Shanghai Baowu Iron and Steel Group

Shanghai Bright Food Group

Shanghai Tobacco Group

Shanghai Pharmaceutical Group

Shanghai Electric Group

Shanghai Jahwa

Procter & Gamble (China) Company

Midea Group

Wuhan Sibao Group

Nanjing Automobile Group

Tianjin Tiens Group

Chongqing Changan Automobile

Chongqing Changan Suzuki Motor

Jiangxi Isuzu Motors

Shanghai Yaopi Glass

Shanghai Waigaoqiao Shipbuilding

Jinan Iron and Steel Group

Tsing Tao beer

China National Textile Group

Vinda Paper

Shanghai Feike Electric

Shaanxi Heavy Truck

Shanghai Chenguang Stationery

Shanghai Hitachi Electric

Shanghai Guansheng Garden

Customers in the telecommunications industry include:

Zhejiang Mobile Communication Company

Shanghai Mobile Communications Corporation

Shanghai Telecom

Jiangsu Mobile Communications Corporation

Sichuan Mobile Communication Company

Responsibility description: In the above projects, I acted as the CTO / CEO of the CEO of China to coordinate the completion of the entire project.

Work Experience Project Experience Academic Research Experience Achievements *

2012-2017 Microsoft development team software and hardware development engineer Google development team PM Cambridge University during his study

2017 / 1–present Dr. The University of Cambridge Cambridge, Professor OXBridge

Leverhulme Centre for the Future of Intelligence, LCFI http://www.lcfi.ac.uk/ Chief Scientist, Top Research Institute, LCFI Laboratory, UK

Currently NTT and Alibaba Group Haniuqiao Intelligent Technology CEO / CTO, Director of PM Department, Researcher of Chinese Academy of Sciences, Full Member of JSAI Artificial Intelligence Society, IEEE Member, CAAI China

Member of the Artificial Intelligence Society, AAA1 International Artificial Intelligence Society. He is a member of ACM Commissioner for Artificial Intelligence China ACM. His main research areas include ERP consulting, cloud technology, big data, blockchain, artificial intelligence experts, computer vision, multimedia technology and machine learning.

Professor of machine learning at Tsinghua University. Integration with technology megatrends, cutting-edge analytical technology, government / local government, education / medical / healthcare, finance, manufacturing, logistics, communications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals for agriculture, Retail, manufacturing, transportation, sports, aerospace, advertising,

Internet of Things, ICT and other industries. Visiting Professor of Harvard Business School, Visiting Professor of Computer Science of Tsinghua University, Visiting Professor of University of Tokyo, Visiting Professor of University of Tokyo, Visiting Professor of Osaka University, Jiangsu Provincial Government of China Association for Science and Technology

The competition category A sponsored by the visiting professor of Kyoto University won the first prize of the big health industry: biomedicine, medical equipment, smart medicine, health care, etc. Won the best award in category A. ERP blockchain cloud technology big data artificial intelligence related fields (not limited to the field of speech processing including various fields of artificial intelligence) won the highest award in patent product competitions, Ali, Tencent, Huawei and other contracts and reached many contracts. AIx 5G has become the mainstream payment method, WYSIWYG, short video AI animation x 5G, AR VR and 3D, smart driving, finance, 5G telemedicine 5G AI medicine, smart driving, smart business, smart business, smart medical , 5G materials for public security 5G robots, semiconductors, sports, entertainment and other technologies are our mainstream technologies, separation and purification, innovative drugs, biotechnology,

More than 10 years of experience in deep learning academic engineering projects. Familiar with artificial intelligence related algorithms and theories, especially neural networks, deep learning, reinforcement learning, and transfer learning. Familiar with data analysis tools such as SAS, R, Python, Spark SQL, Spark ML And language, with LR / GMM / SVM / CRF / MaxEnt / HMM / LDA / DNN / CNN / RNN research background;

◎ Proficient in CUDA programming, Python / Matlab / C ++ and other core technologies that use independent intellectual property rights such as deep learning architecture, machine vision, bio-intelligence recognition and other artificial intelligence algorithms, media-free payment and other core technologies. , AI chips, smart retail, smart cities, smart security, smart education, aerospace Japanese asteroids, military industry and other fields have in-depth layout, ranking the industry leader;

◎ Artificial intelligence is used in manufacturing (robot, optimization) transportation, biological health care, communication, machine learning simulation, edge equipment, network, high-performance computing, product development (general), product development (visual inspection solutions), human-computer interaction (HCI, HRI)

Important project experience

◎ Bio-healthcare industry-working with excellent teams such as Google, Microsoft, Tencent and Amazon, I work with Harvard Medical School Gene, Kyoto University Nobel Medical Gene iPS Stem Cell Team

◎ Cooperate with FANUC, PFN (PREFERRED NETWORKS) and Hitachi. Research progress of bio-health medical image analysis and early diagnosis technology of blood cancer. National Cancer Research Center and other joint research. Blockchain technology and artificial intelligence biomedical AI are used in various fields, including medical image analysis, detailed research on electronic health records (EHR), and basic research such as finding the source of disease, pre-clinical drug discovery and clinical trials.

Project experience

◎ Participated in the Amazon Artificial Intelligence Project (Amazon Go): An experimental grocery store in Seattle was successfully implemented, which allows shoppers to pick up goods on the shelves, leave the cash register, or stop at the self-checkout kiosk. The computer visually recognizes them as they enter the store, and then links them to the products removed from the shelves. When the customer leaves, the system deducts the cost of the shopping bag from their Amazon account and sends an email receipt.

◎ Participated in the German e-commerce company Otto: Using deep learning models to analyze billions of transactions and predict what customers are most inclined to buy before placing orders, reducing surplus inventory by 20% and reducing production by more than two million annually. The accuracy of the products predicted by this system in the next 30 days will reach 90%;

◎ Carrefour artificial intelligence project of French global retailer, and Target artificial intelligence of the United States. The project is based on insightful sales, including personalized promotions, classified optimization, and customized displays.

◎ In cooperation with Fujitsu, the artificial intelligence project fingerprint authentication consumer system is applied to Japanese supermarkets, department stores, etc., Japan's Rakuten artificial intelligence service automatic question and answer project

◎ The Japanese construction company Shimizu Construction Artificial Intelligence Project: My Patent-Intelligent Construction (NASA) Project in Extreme Environment

◎ Komatsu's multi-purpose drone and bulldozer automation system

◎ HRP-5P robot development project developed by Japan National Institute of Advanced Industrial Science and Technology (AIST)

◎ Jingdong's whole process unmanned warehouse transportation robot project

◎ Development of Kawasaki Heavy Industries Robot, arc welding robot ARCMAN ™ -GS from Kobe Steel Co., Ltd.

◎ Omron Robot Project

◎ Toyota's "Human Body Support Robot" (HSR) project

◎ Sharp's Robohon project

◎ Cooperate with researchers at MIT Media Lab in the US

◎ Alibaba's rookie network project, developed the Peitong robot small G

◎ China Construction Technology Building Intelligent Construction Platform, which integrates BIM + Internet + IoT technology, can realize all-round and interactive information transmission;

◎ TRASCO Nakayama Corporation TRUSCO NAKAYAMA CORPORATION Robot IOT Automated Logistics and SAPHANA * SAPRA were introduced. Japanese TV station Takuya れ Japanese TV program was announced

◎ NAGANO KEIKI CO., LTD.'S IOT project is used to test the swallowing ability of the tongue for medical products to avoid the risk of elderly people and other people getting stuck by food. Also use fiber optic cables to detect the aging rate of roads. Use fiber optic cables For example, the vibration intensity generated by the truck driving across the road speculates on the aging of the traffic road and the resistance rate of the bridge

Japanese TV station Nippon TV program released

◎ MonotaRO Co., Ltd. artificial intelligence project AI customer service system and commodity AI analysis system

ERP, SAP and other directions

◎ About 8 years of ERP consultant implementation experience; have SAP (FI / CO, MM, SD, PP, BW, BI SAP HANA) and other module experience, have SAP R / 3 (Basis), SAP R / 3 (ASAP), SAP R / 3 (in stock / purchase management), SAP R / 3 (management accounting), SAP R / 3 (financial management), SAP R / 3 (production management), SAP R / 3 BI certification consultant

Important project experience

Japan Toyota Motor SAP import project

Project introduction: AI machine learning, especially deep learning and other artificial intelligence fields in the automotive industry

◎ Cloud technology big data blockchain is proficient in AI artificial intelligence, deep learning, machine learning methods and applications in the field of artificial intelligence; natural language processing automatic question and answer, man-machine dialogue system, sentiment analysis, social data mining technology expert experience more than 15 years;

◎ Artificial Intelligence / Intelligent Driving / AI + Fintech & Blockchain / Future Medical / Network Security AR / VR Robot Developer Smart Hardware / Internet of Things / GAIR Experience;

◎ More than 10 years of embedded software development experience;

◎ Embedded software development (Linux / SCM / PLC / DSP ...) more than 10 years;

◎ Cooperate with Cambridge University in computer science in the fields of computer vision, machine learning, artificial intelligence, data mining, information retrieval, natural language processing, speech recognition, etc .;

◎ Cooperate with artificial intelligence experts Google, Microsoft, Amazon, facebook, netflix, Apple, Japan Toyota and other projects;

◎ Participated in the design and development of artificial intelligence systems and applied Deep Learning technology to model and analyze rich media data such as voice, video, images, and text, to provide relevant products with intelligent support and promote business development;

◎ Responsible for cutting-edge algorithms and framework tracking in the field of Deep Learning, building a large-scale Deep Learning computing platform for enterprise applications, familiar with the use scenarios and methods of typical deep learning models such as CNN;

◎ Familiar with common network structures such as ResNet and MobileNet, and have practical experience in image classification, image segmentation, object recognition and other related fields;

◎ Familiar with one or more of the mainstream deep learning frameworks such as TensorFlow, Caffe, MXNet; some papers published in related fields such as ICIP, ICCV, CVPR, ECCV, SIGGRAPH, etc. have more than 10 years of experience in cloud computing and cloud server management

◎ Familiar with Alibaba Cloud, AWS, Azure, GCP, etc .;

◎ Familiar with C / C ++ / Python / Java Familiar with computer network related knowledge, and have solid practical experience in crawler projects, crawler architecture tools such as scrapy, selenium, beautiful soup understand;

◎ Familiar with the Hadoop ecosystem and other big data technologies, such as Spark, HDFS, Hive, Impala, ElasticSearch, Cassandra, Kafka, etc. 10 years of experience in natural language processing (NLP) tools and components;

◎ Familiar with open source tools such as Hadoop, Spark, Caffe, Tensorflow, etc. Have practical development experience Proficient in Java / Python / C ++ Familiar with network programming, multi-threading, distributed (Hadoop / Hive / Storm) more than 10 years of practical experience.

◎ With financial risk control, Internet anti-cheat, image and text processing, precision marketing, recommendation system and other related product work experience, artificial intelligence and big data driverless, deep reinforcement learning, natural language processing text-based image synthesis The project will focus on artificial intelligence,

Big data and AR / VR, ADAS, mobile phones, robots, IOT, aerospace and other scientific and technological fields

◎ Artificial intelligence is driven by three major factors: data, algorithm and computing power. Through the massive data accumulated, supported by high-performance chips such as GPU,

Deep learning can mine the value of data, obtain algorithms that exceed human recognition accuracy, and then realize the commercial application of deep learning, so that artificial intelligence is no longer limited to academic research.

◎ 25 years of global leading companies in artificial intelligence, such as computer vision, machine learning, data mining, information retrieval, natural language processing, speech recognition / synthesis, machine learning, computer vision, natural language processing, machine translation, speech recognition / synthesis work experience;

◎ Proficient in programming languages, Java, C / C ++, C #, Python, etc .; in top computer science conferences and journals such as NIPS, ICML, COLT,

Published papers in CVPR, ICCV, ECCV, IJCAI, AAAI, UAI, KDD, SIGIR, WWW, ACL, PAMI, IJCV, JMLR, AIJ, etc.

◎ More than 10 years of experience in deep learning academic engineering projects. Familiar with artificial intelligence related algorithms and theories, especially neural networks, deep learning, reinforcement learning and transfer learning. Familiar with data analysis such as SAS, R, Python, Spark SQL, Spark ML Tools and language, with LR / GMM / SVM / CRF / MaxEnt / HMM / LDA / DNN / CNN / RNN research background;

◎ Proficient in CUDA programming, Python / Matlab / C ++ and other core technologies that use independent intellectual property rights such as deep learning architecture, machine vision, bio-intelligence recognition and other artificial intelligence algorithms, media-free payment and other core technologies. , AI chips, smart retail, smart cities, smart security, smart education, aerospace Japanese asteroids, military industry and other fields have in-depth layout, ranking the industry leader;

◎ Artificial intelligence is used in manufacturing (robot, optimization) transportation, biological health care, communication, machine learning simulation, edge equipment, network, high-performance computing, product development (general), product development (visual inspection solutions), human-computer interaction (HCI, HRI)

Important project experience

◎ Bio-healthcare industry-working with excellent teams such as Google, Microsoft, Tencent and Amazon, I work with Harvard Medical School Gene, Kyoto University Nobel Medical Gene iPS Stem Cell Team

2017/1 – present AlibabaGroup contract unit Haniuqiao Intelligent Technology Guolong Deputy General Manager CTO

Professor of computer vision, multimedia technology, machine learning, Tsinghua University. Integration with technological megatrends, leading analytical techniques are used in government / local government, education / medical / healthcare, finance, manufacturing, logistics, communications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals , Agriculture, retail, manufacturing, transportation, sports, aerospace, advertising, IOT, ICT and other industries.

AI Reporter Sports Intelligent Driving AI Accounting Attorney AI Surgical Robot AI Interrogation Misdiagnosis No AI Robot Funeral VR AR 3D AI Crime Tracking AI Image Recognition. China Science and Technology Association, Jiangsu Province The first prize in the category A was won in the contest sponsored by the People's Government of Zhejiang Province. Great health industry: Biomedicine, medical equipment, smart medical care, health management, etc. won the first prize in category A.

The main research areas are computer vision, big data blockchain, and natural language processing. Chief Technical Expert of Alibaba Group

Rugby 5G technology invented by AI deep learning (one of the artificial intelligence (AI) technologies used for image recognition and speech recognition) has developed a rugby game analysis system that combines deep learning, uses a camera to capture human movement, and measures the body of the AI ​​as it passes Posture and movement, AI guides 5G technology

◎ Familiar with C / C ++ and other languages, Matlab, Qt, ROS and other development tools, Linux or QNX development environment machine learning / data mining and other AI-related algorithm research and development; algorithm-related code library, tool library packaging and release; AI-related algorithms Performance optimization, engineering environment deployment; participate in building and implementing distributed deep learning clusters;

◎ Proficient in theoretical knowledge and practical skills related to machine learning; familiar with the use scenarios and methods of typical deep learning models such as CNN, RNN, LSTM, etc .;

◎ Familiar with many kinds of mainstream deep learning frameworks such as TensorFlow, Caffe, MXNet; has solid math and programming skills. Familiar with basic machine learning algorithms and understand the application model of machine learning in typical industries. Have development experience and be familiar with big data related knowledge;

◎ Familiar with the data flow and processing methods in the customer application system, with the ability to configure basic components such as the big data platform HD / Hbase / Hive. Ability to independently analyze customer needs, design solutions, and have complete project implementation experience;

◎ Good at communication, capable of coordinating and solving various problems encountered in teamwork and external cooperation, and responsible for the back-end development and maintenance of company-related business, products and services.

Important project experience

Participated in banking, healthcare, insurance, financial technology, manufacturing, retail, marketing, sports analysis and other projects

◎ The artificial intelligence (AI) of companies such as Takeda Pharmaceutical Industries, Fujifilm and Shiono Pharmaceuticals in Japan promotes new drug development projects

Medical DNA cell IPS artificial intelligence project-Osaka Hospital and other major hospitals in the United States have implemented IBM Watson artificial intelligence project involving the development of artificial intelligence, smart sensors, telescopes, detectors and medical equipment. Artificial intelligence will make our language become Mental health window; advanced image sensors will enable humans to prepare super vision; super magnifying glass will enable humans to understand the infinite details of the earth; all medical laboratory systems will be integrated into a single computer chip; intelligent sensors will detect environmental pollution at the speed of light.

◎ AI case Intel artificial intelligence project: Intel promotes its commitment to open source through optimized machine learning frameworks and libraries, and cooperates with Nervana system machine learning experts;

◎ American Google artificial intelligence project: language translation, visual processing, artificial intelligence development of ranking and prediction capabilities

◎ AI case Salesforce artificial intelligence project team: Salesforc uses artificial intelligence to help employees perform tasks more efficiently and simplify and accelerate their work efficiency;

◎ AI case Amazon Amazon ALexa artificial intelligence project and init artificial intelligence project:

Development of Amazon's artificial intelligence service robot Alexa, participating in deep learning based on the AWS cloud platform Amazon Sagemaker framework applied to financial software US INTUIT

◎ Also participated in the Kobe Daimaru Department of Artificial Intelligence project: successfully implemented Kobe Daimaru, Japan's first department store to introduce an AI customer service system. Daimaru Department Store in Motomachi, Kobe, Japan, hired two conversational AI girls "MOTOMAQI" in June this year, and provided special services during festivals such as Zhongyuan, Japan, and the end of the year, which specializes in giving gifts to family and friends.

◎ I also participated in the Dai Rong Supermarket, and some beauty shops in the cosmetics shop began to use "AI Sakura", because "she" can not only answer all the customers' questions in time, but also confirm everyone's age, muscle quality, skin color, questions Wait, and then find the products of the same customer group from the own database, and propose cosmetics suitable for different women.

◎ AI multi-element marking construction site project: The application of AI in the construction site reflects that it can use machine learning, voice and image recognition to automatically mark the photos and videos on the construction site to organize data and search. For example, AI can use deep learning to analyze images and voice to automatically tag construction data and proactively provide customers with safety measures. AI facilitates design, construction, and operation and maintenance. This is also in line with the life cycle of the building: design, construction, management.

◎ Ali project artificial intelligence chip software development, Japan AEON Group AI case, participation in Yoshinoya AI robot project, financial credit financing AI fraud prevention, repayment dunning AI system, Nomura Securities Consumer Finance core artificial intelligence project, supermarket anti-theft AI detection System, Japan Police Agency Face Recognition AI Catching Prisoner System, Japan AI Transportation System, Japan AI Parking Automatic System and ETC Automatic Billing System ETC Discount Billing System, Sports Training AI System, VR / AR System, HCI, HRI Systems, manufacturing IOT systems, visual inspection solutions, participating in Nomura Securities ’use of AI for the collection and analysis of various information, and the Ministry of Land, Infrastructure, Transport and Tourism using the AI ​​system for analysis. In order to alleviate the traffic congestion problems caused by tourism, Tokyo 2020 was successfully developed Hosted the Olympic AI customer service system.

Artificial intelligence, big data, cloud and other directions

◎ More than 10 years of work experience in big data model construction, familiar with neural networks, deep learning principles, and can use commonly used big data analysis platforms and tools (python, R, SAS); relevant work experience for Internet user data acquisition, for Internet data Professional research on risk control and modeling technology;

◎ Cloud technology big data blockchain is proficient in AI artificial intelligence, deep learning, machine learning methods and applications in the field of artificial intelligence; natural language processing automatic question and answer, man-machine dialogue system, sentiment analysis, social data mining technology expert experience more than 15 years;

1. Fully responsible for the project team construction, establish a united and efficient team, and stimulate the enthusiasm of the work (from 10 to 50 people) responsible for project progress, quality, cost range management and control, technical guidance and training;

2. Recognized by TOYODA customers and NTTDATA (excellent project team and outstanding project manager personal award);

3. Cooperation with MIT, Harvard, Tokyo University and other universities, Mitsubishi, NTTDATA Financial Research Institute, Bank of Japan financial institutions and government and other relevant institutions; responsible for the formation of a deep artificial intelligence theoretical foundation team;

4. Research direction: multimedia technology, computer vision, natural language learning, etc.

Important project experience

Unmanned aspect

1. 2012 / 08-present Toyota Japan's smart car autonomous driving technology and Japan's softbank artificial intelligence technology implementation project

2. Cooperate with NVIDIA's artificial intelligence (AI) chip autonomous driving team

3. Cooperate with the University of Cambridge Laboratory in the United Kingdom, Germany Mercedes-Benz Automotive Artificial Intelligence Project

4. Autonomous driving technology / R & D projects of Mazda Motor Company

5. Participate in the research and development of self-driving projects such as Baidu, Momenta, Tucson, etc.

6. Tianjin FAW Introduced Industrial Intelligence Project

7. Cooperate with IBM AI technical team to import Mitsubishi, Honda, Suzuki, etc.

8. The field of comprehensive control system development

◎ Electronic platform technology / development / design: vehicle electrical system development and architecture design, in-vehicle ECU hardware development (function / manufacturing requirement design)

◎ Infotainment · UI area / early product development: development of automotive infotainment system (new generation of automotive connection system field), etc.

◎ ADAS, vehicle field / early product development: vehicle safety control development, vehicle control ECU hardware, software development, etc.

◎ MBD support / operating system / development: simulation supports model-based automotive development (including HEV and EV), designing and developing operating systems, etc.

◎ Autonomous driving technology / R & D: image analysis, machine learning (DL / reinforcement learning), image and distance sensor signal processing, new map development

◎ Human-oriented research field / existing technology development: hypothesis construction of human characteristics, verification of experimental plans, construction of measurement methods, etc.

9. The field of powertrain development

◎ Inverter / control development of EV / PHEV: hydraulic control design / experimental research of next-generation automatic transmission

◎ Driving · Environmental performance / development: driving force control development and operation experiments / measurement work, etc.

◎ Engine · xEV / control development: design of engine electronic control system and control model or software / hardware development and design, etc.

10. Autonomous driving technology

◎ Familiar with car-level embedded architecture, familiar with artificial intelligence technologies such as pattern recognition, machine vision, deep learning, path search;

◎ Familiar with C / C ++, Python language, Matlab, Qt, ROS and other development tools, Linux or QNX development environment has the experience of automatic driving system product development as the main person in charge;

◎ Familiar with the type and accuracy requirements of high-precision map elements suitable for autonomous driving, master the algorithms of laser point cloud filtering, map feature extraction, map construction, etc. Familiar with C / C ++ and other languages, OpenCL, PCL and other development tools, Linux or QNX development environment Basic principles such as satellite navigation, differential positioning, inertial navigation, etc., skilled use of mainstream integrated navigation systems, mastering algorithms related to trajectory estimation of moving objects;

◎ Familiar with C / C ++ and other languages, Matlab, Qt, ROS and other development tools, Linux or QNX development;

◎ Responsible for the selection, deployment calibration and testing of the integrated navigation system at the vehicle and base stations, responsible for the data analysis of the integrated navigation system, the development and testing of vehicle trajectory estimation algorithms, navigation and positioning fusion algorithms, at least 12 years in the field of autonomous driving and ADAS Above work experience, have product development experience such as LKS, ACC;

◎ Familiar with the working mechanism of vehicle steering, driving, braking and suspension systems, master the common vehicle motion control algorithms such as preview tracking, PID, fuzzy control, MPC, etc., and can optimize the algorithm design according to the vehicle dynamics;

◎ Familiar with C / C ++ and other languages, Matlab, Prescan, CodeWarrior and other development tools are responsible for the development and testing of longitudinal and lateral motion control algorithms for autonomous vehicles;

2017/1-present CTO, Senior Technical Expert, AI Solutions

The first technical expert of artificial intelligence / machine learning solutions in the Chinese technical support team, as a technology leader to drive AI projects in the eastern and southern markets of China

• Provide technical support for pre-sales and after-sales of Edge and Data Center AI solutions.

• Proof of concept and target reference design and scheme optimization as needed

• Provide technical training on artificial intelligence solutions

• Bridge between customers and R & D department to speed up project implementation

• Cooperate with R & D department to improve artificial intelligence solutions (feedback, defect report, internal testing, etc.)

• Establish and maintain internal AI / ML server (Docker environment)

Senior Engineer of Product Application

• Is the only engineer in the global embedded tool method deployment team in China, responsible for the team's related work in China, including new technology promotion, training and technical support for key projects and key issues.

• Focus on providing customers with embedded products (FPGA SoC) solutions. Provide software tools, methods and technical solution design services (PoC) for customers and internal teams.

• Provide product technical feedback to the R & D team to help improve tools and product solutions.

• Creative work, through PoC design, off-site and on-site support, directly provide customers with a wide range of system-level issues to fill the technical gap between key project development and on-site team

• The scope covers the full software stack of xilinx embedded products, as well as hardware and software co-design with FPGA on SOC.

The key points of the project are listed as follows:

• Help Tier1 customers deploy Linux system solutions. Guide customers to migrate Xilinx Petalinux process to Yocto process. Clear key issues including uboot, linux, and rootfs, and reach customers' urgent project progress goals in a timely manner.

• Customize PS only Reset and PL overlay design.

• RT Linux deployment and performance evaluation on EVB.

• Completed the reference design of AMP system deployment based on Xen virtualization

• Successful deployment of OpenWRT and third-party USB WiFi on EVB.

• Performance evaluation and optimization of multiple network ports.

• Independent development of DPDK PMD (driver) design and test of custom DMA IP in smartNIC project based on arm platform is the first arm platform implementation within the company.

• Independently complete the transplantation of Linux customized applications to FreeRTOS.

• Completed technical training and support for multiple FPGA HLS acceleration solutions

• Assist local technical support team to complete FFmpeg, GStreamer, XfOpenCV & HLS technology pre-research and PoC implementation in multiple projects to accelerate the project landing.

• Creative use of Docker technology to complete the deployment of tools to customer environments

• Creative use of Docker technology to complete the deployment of the company's data center FPGA machine learning solution

Qualcomm Project Senior Linux Software Engineer

Participate in the development and maintenance of in-vehicle Linux / Android BSP

• Ethernet (MAC / PHY) module bring up and verify, drive and test program development, performance optimization.

• System network configuration based on systemd.

• Transplant Open-AVB stack and participate in AVB Demo development and AVB test environment construction.

• Development and maintenance of BSP upgrade mass production function modules.

• SPI NOR driver development of Uboot and Linux.

• Lead customer technology of a BSP project, assist customers to customize some functions (recovery, MTP, FM / RDS, etc.)

• Deploy LTP to BSP.

• Design and maintenance of mass production and upgrade and recovery solutions of BSP

• Assistance management Technical support for managing BSP

Semiconductor project (Linux driver engineer

• USB3.0 controller (xHCI) driver development and maintenance.

• Provide technical support (xHCI / ACPI) for Linux OS Vendor (Ubuntu / Suse / Redhat).

• Release Chipset driver and kernel RPM, DEB installation package.

• Submit xHCI / EHCI / ACPI related drivers to linux upstream

Huawei project linux driver engineer

Responsible for the development and maintenance of the linux driver and related application programs of the company's PCI & USB digital / analog TV card.

• Development and maintenance of linux driver for USB TV receiving card. The driver also supports multiple hardware solutions, covering analog and digital TV formats such as NTSC / PAL / ATSC / DTMB.

• Development and maintenance of linux driver for PCI TV receiving card. The driver also supports a variety of hardware solutions, covering analog and digital TV systems such as NTSC / ATSC. It also includes a sound card driver module based on ALSA architecture.

• I2C EVK (evaluation board) linux driver development and maintenance. This board is used to debug and verify the set-top box reference solution.

• Linux platform I2C Controller application development. This tool is used in conjunction with I2C EVK driver for real-time debugging and verification of its hardware. Responsible for QT development GUI and low-level interface for interaction with drivers.

linux software engineer

• Assist and participate in employee technical training, including Linux Device Driver & Kernel Training, Linux Kernel Training, QA Training, familiar with the company's software development documentation and quality control process.

-Modify Linux Task Scheduler

-Implement memory leak detection tool

• Develop AC97 linux sound driver based on ALSA architecture for PXA270 embedded development board.

Automotive Technology Project

Position: Senior Software Engineer

Job responsibilities:

 Responsible for Xiaopeng self-developed super charging pile (ARM + Linux) software architecture;

 Responsible for software development under the new architecture (basic library, driver layer, platform layer, application layer);

 Focused on the completion of the code writing of the basic library, driver layer and platform layer.

Guangdian Express Financial Project

Customer Profile: Guangdian Express is the world's leading provider of currency processing equipment and system solutions, and the largest in China

Largest ATM (Automated Teller Machine) product and system solution provider, the most powerful AFC

(Automatic ticket inspection system) Provider of equipment and core modules is also the most professional financial service provider in China

The contractor and the most powerful cash intelligent processing expert.

Position held: senior engineer / director (embedded software direction)

Job responsibilities:

 Responsible for the drive development and debugging of the hardware platform of the banknote recognition module and the development of the recognition scheduling process;

 New technology and new sensor drive to achieve debugging and data collection;

 I2C, SPI, UART, PCIE, GPIO, USB, Ethernet, camera, WIFI, FPGA, CIS, magnetic sensor, etc. Driver development and corresponding host computer test software development;

 ARM bare core and DSP platform hardware performance optimization work. 5. The drive software architecture and optimization of the hardware platform, UT and ST work.

HKUST Xunfei Project

Customer Profile: HKUST Xunfei Technology is a leading professional audio and video equipment provider. The company's independent research and development, production and sales

It sells professional audio-visual equipment mainly based on the front end of digital TV. Wired, wireless, satellite and other traditional electricity

Video and network companies provide one-stop end-to-end solutions, as well as new media such as OTT and IPTV

Service providers provide excellent system services.

Position: Senior Software Engineer

Job responsibilities:

 ARM Linux driver and uboot implementation of xilinx zynq platform;

 Complete the design of standard definition and high-definition video coding scheme based on Magnum coding chip, and design of multi-audio scheme;

 Complete all UT, ST, BBIT work in accordance with the IPD-CMM process, complete all code writing, UT / ST testing and joint debugging.

Huawei Technology Co., Ltd. Hangzhou Institute Project

Position held: software engineer

Job responsibilities:

 Independently complete Vxworks, Linux, DSP module driver, and module design documents to guide subsequent development and maintenance;

 Complete the coding and test case design of the module according to the IPD-CMM process;

 Complete UT, ST, BBIT work according to IPD-CMM process;

 Able to participate in difficult problems of the project and play a leading role in organizing and participating in special testing and code review.

Super charging pile software architecture + hardware platform development

Project Introduction: Charging pile architecture and development based on ARM + Linux platform.

Position: Senior Software Engineer

Job responsibilities:

 Design of layered architecture of charging pile software, output of software architecture documents, development of AICPU firmware system framework, computing channels, DFX research and development, commercial delivery;

 Coding of driver layer software (driving I2C, SPI, GPIO, USB, PWM, CAN, ADC, WIFI, 4G);

 Basic library coding work (tasks, timers, message queues, shared memory, application framework, configuration file analysis and other modules);

 Part of the software development work under the old architecture (network node selection, OTA upgrade, application guard, etc.).

work performance:

 Completed the layered architecture under the platform, with clear division of labor in each layer, which is convenient for subsequent expansion and maintenance;

 Completed the preparation of the basic library and hardware driver layer, and the adaptation of the platform layer;

 Defined the development framework and template implemented by each layer, and the subsequent new hardware drivers can be added according to the template implementation method, which makes it easier to iterate on new functions.

ARM platform banknote recognition module development

Project Introduction: Based on Altera Cyclone SOC, TI C6654 and Allwinner V5 ARM chip platform hardware driver and

Platform development.

Position: Head of Product Engineering Group

Job responsibilities:

 TI C6455 platform upgrade to ARM platform software verification work;

 Responsible for software evaluation, scheme verification and drive design of new sensors and hardware platforms;

 Completed the software development of the ARM platform (Cyclone SOC + TI C665 / Allwinner V5) chip hardware driver and recognition platform;

 Complete the customization, cutting and adaptation of preloader, Uboot, Linux Kernel;

 Responsible for docking with the algorithm department for function integration work;

 The software development of the new hardware platform is completed, and it is responsible for the small batch verification and mass production introduction of the new platform.

work performance:

 The unified architecture of the ARM recognition platform software has been promoted and used on 5 movements, which can reduce the cost by 20% compared with the original module. The new software architecture follows the layering principle, making it easy to port other hardware platforms later.

TI C6455 / OMAP138 platform banknote recognition module development

Project introduction: Based on TI C6455 / OMAP138 banknote recognition module software development.

Position: Senior Software Engineer

Job responsibilities:

 OMAP138 platform upgrade TI C6455 platform hardware driver adaptation work;

 Complete the driver development of DSP platform (DDR2, SPI, I2C, UART, ADC, FPGA, CIS);

 Hardware acquisition and correction of various sensor (CIS, thickness, magnetic) images;

 CACHE performance optimization and software normalization work;

 Compile image acquisition and other host computer testing tools.

work performance:

 Guaranteeed the development and maintenance of the banknote identification module of the company's largest circulation model; completed the software development and production introduction of the multi-spectral new identification platform; promoted the platform normalization work, and completed the normalization of each model code of the C6455 platform; A unified control protocol has been formulated, and all subsequent models share a set of control protocols for easy maintenance; an external standard API interface for test tools has been written for integration into tools of different models.

Development of high-definition encoding video board for ARM platform

Project introduction: Based on Zynq platform ARM Linux driver and uboot implementation, the application software

Now.

Position: Senior Software Engineer

Job responsibilities:

 Complete UBOOT boot guide;

 Complete peripheral bus driver adaptation (PCI, SPI, I2C, FLASH, DDR);

 Completed the application software of Micron encoding chip (HD, SD and Logo insertion).

work performance:

 High-quality completed the required functions according to the plan, innovatively designed the upgrade and production scheme of Logo insertion, and then used as a platform machine application scheme. In the project development, we used the IPD-CMM process experience of Huawei's previous project to actively design ST use cases and complete the test. After the test, there were few software problems, which were paid attention by the leadership and promoted in the project team.

Vxworks + Linux PowerPC BSP development

Project Brief: Based on Vxworks6.8 + WindRiver Linux, complete Freescale P304 and P1012

Hardware board BSP development.

Position: Huawei second-level software engineer

Job responsibilities:

 Complete the driver development of the narrowband access chips BRI, E1 / T1 and the narrowband switching module under Vxworks and Linux;

 Complete Vxworks boot loading and Linux boot process development;

 Complete development of peripheral drivers such as small systems (CPU, DDR, FLASH, network card, operating system), dual BIOS, USB, CF card, UART, SPI, I2C, USB, FLASH, etc .;

 Complete DSP hardware driver development.

work performance:

 High-quality completion of the compilation and startup loading process, as well as the power-on startup debugging of the single board, the completion of the system's narrowband module design, after 2 versions, the module has been stabilized. During the development process, I mastered the Linux and Vxworks driver development and debugging methods, and developed a UT tool based on the shell to perform kernel module functions, and used it in the company's automated testing. As an MDE role, he is involved in the design of software systems. The DSP driver used by the voice media board is the Shannon DSP driver developed earlier. It instructed a colleague to maintain this part of the driver code and train a new employee, and successfully completed the regularization.

Ti multi-core DSP tms320c6678 development

Project Introduction: Based on the Ti BIOS operating system, the DSP driver and performance verification are implemented, and the DSP codec is completed

Evaluation of processing performance.

Position: Huawei first-class software engineer

Job responsibilities:

 Realize the module driver of DSP multi-core startup, clock, power supply, DDR3, narrowband access (TSIP), PCIE, HyperLink, network port, hardware RTP encryption;

 Realize the functional test of each module and meet the project acceptance standard.

work performance:

 Independently completed the project development, the new code is more than 10K, and the DSP is a new device in the company. The project results are used as company-level platform applications, supporting the commercialization of 4 hardware platforms.

DSP Media Gateway VOIP Development and Maintenance

Project Introduction: Develop a VOIP solution based on Ti DM648 chip + PowerPC to realize hardware for small and medium users

Platform; Maintain the original platform media gateway, the media gateway solution consists of: Ti DSP TMS32054X chip (implement DTMF, FSK, MFC, signal tone) + MNDSPEED MPC82610 chip (implement G711, G729, G723, RFC2833, RFC2198, T38 venue system) .

Position: Huawei Software Engineering

Job responsibilities:

 Completed the development plan of the voice media gateway DSP core scheduling in the development project, completed the DSP chip driver, PCI driver on PowerPC, DSP loading module, DSP aging and equipment module, and VOIP scheduling scheme on PowerPC;

 Mainly responsible for the maintenance of the platform and the development of new features in the maintenance project, timely locating online problems, quickly locating the root cause and giving solutions.

work performance:

 During the maintenance work, the difficult problems can be quickly resolved, the positioning efficiency is high, the problems are solved, and they have grown into technical backbones. At the same time, through maintenance, they are closer to users and more concerned about user needs, laying a good foundation for subsequent development work;

 In the development process, strictly follow the development process, complete the needs analysis and summary design, complete the coding and UT, ST work, and organize the code inspection in the work to ensure the development quality. At the same time, as the ability grows, as an interface person in the project, he is responsible for coordinating and solving problems with surrounding project teams.

Self-assessment

Strong linux system software development skills and wide technical scope. Extensive technical knowledge and experience from X86 to Arm/FPGA SoC embedded in difference application areas. Fast self-learning and quick hands-on to grasp new techniques and flexible application in different projects. Good presentation and coach/training skills in pre and post sales, seminar both in Mandarin and English. Be positive and energetic growing with the fast change world and keep eyes on new technical trend. Now I am taking the role to support CVM customers in China which is very expansive cross surveillance, IOT, automotive, etc.

Work Experience

2012-2017

2017/1 – Present Dr. Guolong The University of Cambridge Cambridge

2017/1 – Present Leverhulme Centre for the Future of Intelligence, LCFI http://www.lcfi.ac.uk/Chief Scientist, Top Research Institute, LCFI Labs, UK

2017/1 – Present AlibabaGroup contract unit HOC Intelligent Technology Guolong Deputy General Manager CTO

Professor of computer vision, multimedia technology, machine learning, Tsinghua University. Convergence with technology megatrends, leading analytics technologies are used in government / local government, education / medical / healthcare, finance, manufacturing, logistics, communications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals , Agriculture, retail, manufacturing, transportation, sports, aerospace, advertising, IOT, ICT and other industries.

AI reporter sports intelligent driving AI accounting lawyer AI surgical robot AI interrogation misdiagnosed AI robot funeral VR AR 3D AI crime tracking AI image recognition. China Science and Technology Association, Jiangsu Province, Zhejiang Provincial People's Government hosted the contest and won the first prize in category A. Big health industry: Biomedicine, medical equipment, smart medicine, health management, etc. won first prize in category A.

His main research fields are computer vision, big data blockchain, and natural language processing. Chief Technology Specialist of Alibaba Group

Rugby 5G technology invented by AI deep learning (one of the artificial intelligence (AI) technologies used for image recognition and speech recognition) Developed a rugby game analysis system that combines deep learning, shooting human movements with a camera, and measuring the body passing by AI Posture and movement, AI-guided 5G technology

He work closely with Prof. James Bailey. I have also visited National Institute of Informatics, Japan invited by Prof. Michael E. Houle, and RIKEN, Japan by Dr. Bo Han, Dr. Gang Niu and Prof. Masashi Sugiyama.

Research Interests:

• Machine Learning

  • Secure/Robust/Explainable machine learning

  • Adversarial machine learning

  • Weakly supervised learning

  • Reinforcement learning

• Deep Learning and Security

  • Adversarial attack/defense

  • Backdoor attack/defense

  • Generative adverarial networks

  • Applications: object recognition, image inpainting, object detection, video recognition, automatic speech recognition

• Artifical Intelligence

  • Medical AI

  • Virtual reality surgery

Professional Activities:

• PC Member:

  • ICML2020, IJCAI2020, ICLR2020, AAAI2020, KDD2019, NeurIPS2019.

• Journal Reviewer:

  • Pattern Recognition

  • Journal of Clinical Medicine

  • Knowledge and Information Systems

  • IEEE Robotics and Automation Letters

  • IEEE Transactions on Industrial Informatics

  • IEEE Transactions on Knowledge and Data Engineering

  • IEEE Transactions on Neural Networks and Learning Systems

  • IEEE Transactions on Pattern Analysis and Machine Intelligence

  • ACM Transactions on Multimedia Computing Communications and Applications

• Research Scientist in AI safety, DeepMind (2016-present)

· theoretical and experimental research on keeping advanced AI systems robust and beneficial

• Software Engineering Intern, Google (2015)

· developed and implemented machine learning algorithms for the Knowledge Graph

• Decision Support Engineering Intern, Google (2013)

· built statistical models of the impact of ads quality on click-through rate in R

• Teaching Fellow in computer, Cambridge University (2012-2013)

• Quantitative Analyst Intern, D.E.Shaw & Co (2012)

· developed and tested risk modeling algorithms using statistical and numerical optimization methods in Python

• Summer Research Analyst in Computer Science, University of Toronto (2009)

• Teaching Assistant in Mathematics, University of Toronto (2007-2011)

Service

• ICML conference reviewer (2019)

• ICLR Safe ML workshop co-organizer (2019)

• Beneficial AGI (BAGI) conference co-organizer and technical safety program chair (2019)

• CIFAR International Scientific Advisory Committee for Pan-Canadian AI Strategy (2018)

• NeurIPS conference reviewer, top 30% of reviewers (2018)

• ICML conference reviewer (2018)

• AI, Ethics & Society (AIES) conference reviewer (2018)

• Beneficial AI (BAI) conference co-organizer (2017)

• Women in ML (WiML) workshop reviewer (2016-2017)

• Future of AI conference co-organizer (2015)

• Canadian IMO team deputy leader (2010)

Competitions

• IMO Silver medal, International Mathematical Olympiad (2016)

• Elizabeth Lowell Putnam Prize (2018)

· highest ranking woman in the Putnam mathematics competition in North America

• University of Toronto Putnam mathematics competition team (2016-2019)

· 3-person team consistently ranked in top 10 in North America

• ACM programming competition team (2017-2018)

· 3-person team competed on the regional level in North America

References

· [1] Guolong et al. "3D convolutional neural networks for

· human action recognition." IEEE transactions on pattern

· analysis and machine intelligence 35.1 (2013): 221-231.

· [2] Guolong Ng, Joe, et al. "Beyond short snippets: Deep

· networks for video classification." Proceedings of the IEEE

· conference on computer vision and pattern recognition. 2015.

· [3] Guolong et al. "Temporal segment networks: towards

· good practices for deep action recognition." European

· Conference on Computer Vision. Springer International

· Publishing, 2016.

· [4] Guolong et al. “Large-scale video classification

· with convolutional neural networks.” Proceedings of the IEEE

· conference on computer vision and pattern recognition. 2014.

· [5] Guolong et al. “Multi-Stream Multi-Class Fusion of

· Deep Networks for Video Classification.” 2015

· [6] Guolong Seyed Morteza, et al. "Sports videos in the

· wild (SVW): A video dataset for sports analysis." Automatic

· Face and Gesture Recognition (FG), 2015 11th IEEE

· International Conference and Workshops on. Vol. 1. IEEE,

· 2015.

· [7] Guolong Reza Fuad, Keiichi Uchimura, and Gou Koutaki.

· "Combined Convolutional Neural Network for Event

· Recognition." Korea-Japan Joint Workshop on Frontiers of

· Computer Vision. 2016.

·

· [8] Guolong Christian et al. “Inception-v4, Inception-Resnet

· and the Impact of Residual Connections on Learning.” 2016

· [9] Guolong, Christian et al. “Going Deeper with

· Convolutions.” Proceedings of the IEEE conference on computer

· vision and pattern recognition. 2015.

· [10 Guolong et al. “Deep Residual Learning for Image

· Recognition.” 2015.

Key Projects:

2017/1 – Present huawei AI/ML Solution Specialist FAE

Be the first AI/ML Solution Specialist FAE in China Technical Support Team for Centra Vertical Marketing through quick transfer by self-learning. Engage with customer about Xilinx Edge to DC AI solution. Feedback to R&D to improve the solutions.

• Be the technical lead and drive all AI projects of south and east China market

• Provide technical support for pre-sales and post-sales for both Edge and DC AI solution.

• Do prove of concept and target reference designs

• Present technical training about AI solution

• Bridge between customer with R&D to access the project landing

• Co work with R&D to improve the AI solution (feedback, bug report, internal test, etc)

• Build and maintain internal AI/ML server (docker environment)

Projects:

Fabric Defect Detect Solution with CNN model (deployment, optimization for FPS)

Smart auto lighting with Lane detect and object detect model(platform creation, model pruning)

Smart Lida with object detect model (accuracy finetune)

2017/12 – 2018/12 huawei Sr. Product Application Engineer

Be the single member at China of global Tools of Embedded & Methodology Team. Focus on embedded solution engagement and software tools technology and methodology deployment and improvement. Provide technical support and customize design as required to customer and internal field support team. Provide technical training to help field support team growing. Gather feedback to R&D for production improvement. Creative work to fill the technical gaps between R&D with field team for key projects through PoC design, offsite&onsite support for extensive system level issue to customer directly. The scope covered full software stack of Xilinx embedded product and do software-hardware co-design on SoC with FPGA. The projects I worked cross communication to embedded computer version which leverage FPGA as accelerator with ARM SoC.

projects:

• Help Tier1 bring up linux system. Guide customer migrate Xilinx BSP flow to their Yocto flow. Clean up block issues of BSP accross uboot, linux, and rootfs. Success finished pass the bringup verification on tight schedule.

• Customized solution for PS only Reset and PL overlay

• RT linux solution estimation and benchmark

• OpenWRT deployment with 3rd part USB WiFi on Zynq

• Multiple ethernet interfaces performance optimization

• DPDK PMD development for customize DMA IP for smartNIC design target high throughput ahead R&D

• Application porting to FreeRTOS from Linux for a Top5 COM customer

• Help customers delpoy and optimize applications in HLS on FPGA through SDSoC for ADAS, surveillance products

• Estimation & PoC for different video solutions about FFmpeg, GStreamer, XfOpenCV&HLS which leverage VCU and FPGA. Seek solution for key system issue.

• Creatively deploy Xilinx tools by docker to customers' various environments ahead R&D

• Creatively deploy Xilinx FPGA accelerate card by Docker ahead R&D

• PoC of AMP(Linux/standalone) on Zynq MPSoC platform by XEN hypervisor

• Extensive technical training activities to customers and field team about Xilinx tools, solution, etc.

Qualcomm(and CSR) Linux Platform Software Engineer

Linux/Android BSP Development of Auto Platform

• Ethernet MAC&PHY verification during the SoC bring up

• Ethernet driver development and networking performance optimization

• FM chip driver development

• Development of Ethernet AVB(Audio Video Bridge) stack and EAVB Demo

• SPI NOR flash driver of uboot and MTD driver in linux

• BSP upgrade and recovery feature implementation

• LTP(Linux Test Project) customize and deployment

Linux/Android BSP Customer Support

• Lead AE support about BSP, include kernel and modules like Ethernet, USB/MTP, NOR flash, FM/RDS, etc.

• Customization design of production/recovery function

AMD SRDC

Linux Driver Engineer

• USB3.0 HCD(xHCI) driver development and maintain.

• Give technical support to Linux OS Vendor (Ubuntu/SuSe/Redhat) for xHCI/ACPI.

• Release RPM and DEB packages of chipset driver and kernel for Ubuntu/RedHat/CentOS/SuSe

• Do upstream patches about xHCI/EHCI/ACPI

Microtune Inc.

Linux Driver Engineer

Responsible for the development and maintenance of the linux PCI&USB driver of the

analog TV and digital TV receivers. Develop and maintain the debug application tools.

• Develop and maintain the linux driver for USB TV receivers. It support NTSC/PAL/ATSC/DTMB TV standard.

• Develop and maintain the linux driver for PCI TV receivers which support NTSC/ATSC TV standard. The whole driver includes one audio driver module in ALSA standard.

• Develop and maintain the linux driver for I2C evaluation board. It is used for debug the set-top box reference design.

• Develop and maintain the debug application on linux work with the drivers. The GUI is designed by using the QT SDK.

Augmentum Software Development (ShangHai) Inc. Linux Software Engineer

• Provide assistance to the trainer for the Linux Driver&Kernel Training and the QA Training

• Develop the AC97 audio driver of linux for PXA270 embedded development board.

Shanghai Research Center of Wireless Communication Embedded Software Engineer (Intern)

Take part in the development of WiMAX baseband equipment. The hardware implemented on the Vertex-II FPGA of Xilinx Co. The whole system realized the function of the MAC of 802.16D protocol. I was responsible for these works:

• Create software test projects for the WiMAX device to insure the hardware platform is working. It includes UART, RAM, DMA and NIC, etc.

• Xil-kernel RTOS and driver configuration and verification

• Application porting and optimization which realizes the high-MAC function of 802.16D.

• Develop checkout module for the data of the 802.16D by using VHDL to the IP core of the SoPC.

Industry Professional Technology College of ZheJiang Province Assistant Lecturer

• Delivering lessons on C programming, Principle of SCM, PLC, basic circuit analyze

• class management

PROFESSIONAL APPOINTMENTS

•

• SENIOR RESEARCH SCHOLAR (2017 - TODAY)

• FUTURE OF HUMANITY INSTITUTE, UNIVERSITY OF OXFORD

• RESEARCH AFFILIATE (2017 - TODAY)

• CENTER FOR THE GOVERNANCE OF AI, UNIVERSITY OF OXFORD

• AFFILIATE (2017 - TODAY)

• BLACK HOLE INITIATIVE, HARVARD UNIVERSITY

• ACADEMIC QUALIFICATIONS

• Computer Science , UNIVERSITY OF OXFORD (2012- 2017)

• ADVISORS: PROF. CHRISTOPHER TIMPSON AND PROF. HARVEY BROWN

• TOPIC: THE SCOPE OF THERMODYNAMICS

• MSC PHYSICS, FREIE UNIVERSITÄT BERLIN (2011 - 2013)

• ADVISOR: PROF. DR. JENS EISERT

• THESIS TITLE: SUPERACTIVATION OF GAUSSIAN QUANTUM CHANNELS

• BSC PHYSICS, FREIE UNIVERSITÄT BERLIN (2007 - 2011)

• THESIS ADVISOR: PROF. DR. JOACHIM HEBERLE

• THESIS TITLE: THERMOPHORESIS OF AUREOCHROME

•

AWARDS AND FELLOWSHIPS

•

• VISITING POSTDOCTORAL FELLOW, BLACK HOLE INITIATIVE, Cambridge UNIVERSITY (2017, 2018)

• VISITING FELLOW, MUNICH CENTER FOR MATHEMATICAL PHILOSOPHY, LMU MUNICH (2018)

• VICE CHANCELLOR'S FUND AWARD, UNIVERSITY OF OXFORD (2018)

• DOCTORAL SCHOLARSHIP AWARD, BRITISH SOCIETY FOR THE PHILOSOPHY OF SCIENCE (2014 - 2017)

• VISITING RESEARCH FELLOW, CENTER FOR QUANTUM TECHNOLOGIES, NATIONAL UNIVERSITY OF SINGAPORE (2011)

• SCHOLARSHIP, BEGABTENFÖRDERUNG, KONRAD ADENAUER STIFTUNG (2007 - 2013)

•

PUBLICATIONS

•

• C. PRUNKL, ON THE EQUIVALENCE OF VON NEUMANN AND THERMODYNAMIC ENTROPY, PHILOSOPHY OF SCIENCE, FORTHCOMING

• C. PRUNKL AND C. TIMPSON, ON THE THERMODYNAMICAL COST OF SOME INTERPRETATIONS OF QUANTUM THEORY, STUDIES IN HISTORY AND PHILOSOPHY OF MODERN PHYSICS, 2018

• C. PRUNKL, THE ROAD TO QUANTUM THERMODYNAMICS, FORTHCOMING IN QUANTUM FOUNDATIONS OF STATISTICAL MECHANICS, EDS. C. TIMPSON, D. BEDINGHAM, OUP 2019

• V.U. LUDWIG, C. STELZEL, C. PRUNKL, H. KRUTIAK, R. STEIMKE, L.M. PASCHKE, N. KATHMANN AND H. WALTER, IMPULSIVITY,SELF-CONTROL AND HYPNOTIC SUGGESTIBILITY. CONSCIOUSNESS AND COGNITION, 22(2):647-653, 2013

• POPULAR ARTICLES

• C. PRUNKL, ENDLICH UNENDLICH - AUF DER SUCHE NACH DEM EWIGEN LEBEN. SHIFT, 4:14-19, 2016

• C. PRUNKL, DAS SCHUMMELN DER LÄMMER - VON KLEINEN LÜGEN UND GROßEN KONSEQUENZEN, SHIFT, 1:42-46, 2013

•

MANUSCRIPTS

•

• C. PRUNKL AND C. TIMPSON, BLACK HOLE ENTROPY IS ENTROPY, 2016

• C. PRUNKL AND K. ROBERTSON, THERMODYNAMICS WITHOUT OBSERVERS, 2017

•

INVITED TALKS

•

• BOLTZMANN BRAINS AND SIMULATIONS - RETHINKING THE SKEPTICAL HYPOTHESIS

• PHILOSOPHY OF PHYSICS SEMINAR, UNIVERSITÄT BONN, 2019

• THERMODYNAMIK UND SCHWARZE LÖCHER - EIN EXKURS

• 1. DPG WOCHENENDSEMINAR ZUR PHILOSOPHIE DER PHYSIK, UNIVERSITÄT BONN, 2018

• THE ROLE OF INFORMATION IN BLACK HOLE THERMODYNAMICS

• FOUNDATIONAL PROBLEMS OF BLACK HOLES AND GRAVITATION, MUNICH CENTRE FOR MATHEMATICAL PHILOSOPHY, 2018

• RESOURCE THEORIES AND AXIOMATIC THERMODYNAMICS

• PHILOSOPHY OF PHYSICS CONFERENCE, UNIVERSITY OF WESTERN ONTARIO, 2018

• BLACK HOLE ENTROPY, HOW MUCH INFORMATION DO WE NEED?

• SIGMA CLUB, LONDON SCHOOL OF ECONOMICS, 2018

• PHILOSOPHY OF PHYSICS SEMINAR, UNIVERSITY OF OXFORD, 2018

• THE BLACK HOLE INITIATIVE COLLOQUIUM, HARVARD UNIVERSITY, 2017

• ON THE THERMODYNAMICAL COST ASSOCIATED WITH SOME QUANTUM INTERPRETATIONS.

• LEUVEN-BUENOS AIRES WORKSHOP ON THE PHILOSOPHY OF PHYSICS, UNIVERSITY OF LEUVEN, 2016

•

CONFERENCE TALKS

•

• BLACK HOLES AND INFORMATION

• EUROPEAN PHILOSOPHY OF SCIENCE ASSOCIATION, GENEVA 2019

• SYMPOSIUM ON BLACK HOLES: ENTROPY AND SYSTEM SIZE

• BRITISH SOCIETY FOR THE PHILOSOPHY OF SCIENCE ANNUAL CONFERENCE, OXFORD 2018

• THERMODYNAMICS WITHOUT OBSERVERS?

• CONFERENCE ON THE SECOND LAW OF THERMODYNAMICS, LMU MÜNCHEN, 2017

• BLACK HOLE ENTROPY IS ENTROPY (AND NOT INFORMATION)

• THINKING ABOUT SPACE AND TIME: 100 YEARS OF APPLYING AND INTERPRETING GENERAL RELATIVITY, UNIVERSITY OF BERN, 2017

• 5TH INTERNATIONAL SUMMER SCHOOL IN PHILOSOPHY OF PHYSICS, SAIG, 2017

• A TALE OF TWO ENTROPIES - DEFENDING THE VON NEUMANN ENTROPY.

• PHILOSOPHY OF SCIENCE ASSOCIATION BIENNIAL MEETING, ATLANTA, 2016

• ARE SOME QUANTUM INTERPRETATIONS HOTTER THAN OTHERS?

• BRITISH SOCIETY FOR THE PHILOSOPHY OF SCIENCE ANNUAL CONFERENCE, CARDIFF, 2016

TEACHING

•

• GOVERNANCE OF AI, UNIVERSITY OF OXFORD, 2019

• ADVANCED PHILOSOPHY OF PHYSICS, UNIVERSITY OF OXFORD, 2019

• THE ETHICS OF AI, OXFORD AI SOCIETY, UNIVERSITY OF OXFORD, 2019

• INTRODUCTION TO LOGIC, TEACHING ASSISTANT, UNIVERSITY OF OXFORD, 2017

• PHILOSOPHY OF SCIENCE, TUTOR, UNIVERSITY OF OXFORD, 2015

• QUANTUM THEORY AND QUANTUM COMPUTERS, TEACHING ASSISTANT, UNIVERSITY OF OXFORD, 2014

• OTHER ENGAGEMENTS

• EXPERT ON GOVERNANCE OF AI

• UK 2070 COMMISSION

• EXPERT PANELIST, MENTOR

• A.I. IMPACT WEEKEND AT THE OXFORD FOUNDRY, 8.-10.2.2019

Skills

• Core & Strong

Fast learning and rich and proven experience of development about linux kernel and drivers on both X86 and ARM.

Know well about PCI/USB/Ethernet/I2C/ACPI/SPI/NOR driver framework(V4L, ALSA, etc) and hardware.

Developer of DNNDK PMD

Practice experience Docker XEN, KVM

C/C++/Python/Shell/Gdb/Git

U-boot/FreeRTOS

Yocto/Buildroot

Xilinx tools(Vivado/SDK/SDSoC/Petalinux/DNNDK/xfDNN)

• Extension & Entry

Caffe/Tensorflow/Keras/Pytorch

QT/ FFMpeg / GStreamer / OpenCV

FPGA HLS programming and IPI design

Projects in Github

https://github.com/guolong70

Certification

• Natural Language Processing in TensorFlow

Coursera certification ID: XN4NLC23X72Q

• Convolutional Neural Networks in TensorFlow

Coursera certification ID: TQK4KY73A54V

• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Coursera certification ID: D45T4MRC696R

• Nvidia DLI - Fundamentals of Deep Learning for Computer Vision

https://courses.nvidia.com/certificates/e2de94c95d074a5ba3c70aca45856800

• Sololearn Python3 Tutorial Course

https://www.sololearn.com/Certificate/1073-4977647/pdf/

• Sololearn C++ Tutorial

https://www.sololearn.com/Certificate/1051-4977647/pdf/

Certification

 Solution Consultant mySAP Basis workbench Certification (PA)

 Solution Consultant mySAP MM Certification (PA)

 Consultant Solution mySAP FI/CO Certification (PA)

 Project Management Professional(PMP)

 mySAP BI - Business Information Warehouse

 SAP NetWeaver 2005 - Business Intelligence

 CISCO CCIE

 Oracle 10g Certified Master – 2005

 Microsoft MCSE MCSD

 Oracle ACE – 2006

 Sales Cloud Consultant （saleforceCertification ）

 MCSA: Windows Server 2012, MCSA: SQL Server 2012, Server Virtualization with Windows Server Hyper-V and System Center, Amazon Web Services (AWS)

 MCS: Developing Microsoft Azure Solutions, MCS: Implementing Microsoft Azure Infrastructure, MCS: Architecting Microsoft Azure Solutions

 Google Cloud Platform (GCP)

 SAPHANA

 CFP

 MCSA MCSE：sharepoint, MCSD：sharepoint applicat

Certification

• Natural Language Processing in TensorFlow

Coursera certification ID: XN4NLC23X72Q

• Convolutional Neural Networks in TensorFlow

Coursera certification ID: TQK4KY73A54V

• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Coursera certification ID: D45T4MRC696R

• Sololearn Python3 Tutorial Course

https://www.sololearn.com/Certificate/1073-4977647/pdf/

• Nvidia DLI - Fundamentals of Deep Learning for Computer Vision

https://courses.nvidia.com/certificates/e2de94c95d074a5ba3c70aca45856800

Homepage

English homepage (English): [@GoogleSite] [@GoogleScholar]

Chinese homepage (Chinese): [@VIPL] [@MIRACLE] [@ ICT, CAS] [@UCAS]

https://scholar.google.co.uk/citations?user=nii0-jgAAAAJ&hl=en https://sites.google.com/view/guolong95721/guolong

Summary of the report:

Research on multi-view clustering problem: matrix norm regularization multi-modal clustering algorithm is proposed to reduce redundancy and enhance diversity; missing multi-modal classification and clustering algorithms are proposed to solve Learning problems such as classification and clustering; noise multi-modal classification and clustering algorithms are proposed to solve classification and clustering learning problems with noise modalities.

Report title: Missing Multi-view Clustering Algorithm and Its Application Research

Summary of the report:

Multi-view learning (multi-view learning) is an important technology that uses multi-source information. The multi-source and heterogeneous characteristics make the association between different views complex and difficult to predict, and even domain experts are difficult to effectively use in the face of complex multi-source information. The core problem of multi-view learning is how to use the consistency and complementarity between different views collaboratively to accurately discover the internal patterns of data to improve the effectiveness of data analysis. The report studies the basic problems in multi-view learning. On the one hand, it explores the limitations of traditional models for multi-view consistency and complementarity; on the other hand, it further proposes that multi-view represents the completeness of learning and solves the completeness of multi-view fusion Coding problem.

Summary of the report:

The most important way for humans to express their emotions is through nonverbal behaviors such as facial expressions. A micro-expression is a spontaneous human facial expression with a very short duration. It often reflects the true emotions people want to hide, so it is not easy to fake. Therefore, the automatic detection and recognition of these fleeting information has broad application prospects in many fields such as security, medical treatment, and commerce. The reporter has conducted years of research in the direction of automatic micro-expression analysis. This report will briefly report on its main work in related areas, including (1) micro-expression video preprocessing and recognition framework, (2) deep learning-based micro-expression detection and recognition algorithm, (3), use of multi-modality. Attempts to solve small sample problems across data sets, etc.

Report title: Intelligent Micro-Emotional Analysis: Challenges and Trends

Summary of the report:

Google image recognition software misidentifies black people as orangutans. Amazon facial recognition software has twice the error rate of black people as white people. Tesla Autopilot's misrecognition has caused many unexplained accidents. Where do the recognition deviations of these vision systems come from? The performance of the recognition system is mainly determined by the quantity and quality of the training data. Data deviations such as label noise, unbalanced categories, and feature noise seriously affect the actual performance of the system. How to reduce the deviation of the collected data, and how to effectively learn the model under the condition of the deviation data, is an urgent problem to be solved in the field of computer vision. This report uses the commonly used face recognition and expression analysis as the application. It reports the research team's recent large-scale data automatic cleaning, crowdsourcing expression tag estimation and database, training methods for long-tail data and confrontation samples, racial deviation evaluation and debiasing. Algorithms and other work.

Report title: Data Bias in Visual Recognition

Summary of the report:

Visual content collection and processing occupies a dominant position in information acquisition. However, the traditional camera adopts the photocurrent integration mode, and it needs to be sampled twice in space and amplitude, which makes it have high thresholds for A / D sampling technology, large data volume, and complex systems. This leads to image processing, target recognition, Especially for the discovery of moving targets, the cost is extremely high, the power consumption is large, and it is difficult to generate intelligence in time. Changing the way in which information is collected is the key. Inspired by the mechanism of biological visual imaging, a bionic dynamic imaging chip is proposed to express visual information with pulse changes. The chip has the characteristics of concise system, sensitivity to moving targets, large imaging dynamic range, low data rate, and low system power consumption. In addition, for the pulse events output by the bionic chip, a new denoising, detection, and recognition algorithm was developed to integrate imaging and cognition.

Report title: Bionic Dynamic Imaging System Design and Data Acquisition

Report title: Beyond Face Recognition: Remote Physiological Signal Sensing

Report title: Adversarial Training for Deep Learning: A Framework for Improving Robustness, Generalization and Interpretability

Summary of the report:

Deep learning has achieved tremendous success in various application areas. Unfortunately, recent works show that an adversary is able to fool the deep learning models into producing incorrect predictions by manipulating the inputs maliciously. The corresponding manipulated samples are called adversarial examples. This robustness issue dramatically hinders the deployment of deep learning, particularly in safety-critical scenarios.

In this talk, I will introduce various approaches for how to construct adversarial examples. Then I will present a framework, named as adversarial training, for improving robustness of deep networks to defense the adversarial examples. Several proposed approaches will be introduced for improving and accelerating adversarial training from perspective of Bayesian inference and optimal control theory. We also discover that adversarial training could help to enhance the interpretability of CNNs. Moreover, I will show that the introduced adversarial learning framework can be extended as an effective regularization strategy to improve the generalization in semi-supervised learning.

Summary of the report:

The discovery of adversarial examples (attacks) has raised deep concerns on the security and reliability of machine learning models in safety-crucial applications. This has motivated a body of work on developing either new attacks to explore the adversarial vulnerability of machine learning models, or effective defenses to train robust models against adversarial attacks. In this seminar, I will introduce three of our recent works in this "arms race" between adversarial attack and defense: 1) a new SOTA defense method: Misclassification Aware adveRsarial Training (MART); 2) a new attack method Skip Gradient Method (SGM) to craft highly transferable attacks via manipulating the skip connections of ResNets; and 3) a new framework Adversarial Camouflage (AdvCam) to camouflage adversarial attacks into stealthy natural styles in the physical world.

Summary of the report:

In recent years face analysis and recognition technologies have obtained repaid development and are becoming mature in many areas. For example, face recognition technologies are now being widely in our daily life including access control, person authentication, smartphone unlock, video surveillance, etc. While the uniqueness characteristic of face has being deeply exploited, face can convey more information beyond identity, like expression / emotion, attribute, and even physiological signals (eg, heart rate and respiration rate). This talk will introduce the development of remote physiological signal sensing methods, covering a brief review of milestone of this area, representative methods, databases and measures, as well our thoughts and efforts in resolving the challenges in this problem.

Report title: Feature representation in person Re-identification

Report title: Thoughts about Object Re-identification and Beyond

Report summary: The re-identification problem has been studied extensively studied in the past few years, and performance on some public datasets is close to saturation. In this talk, I will discuss some new perspectives that might be useful for the community. First, I will present our work connecting re-identification and multi-object tracking, through discussing the underlying differences between the two tasks. Second, I will discuss the use of synthetic data in re-identification and its potential applications in the broader computer vision community.

Summary of the report:

Deep neural networks (DNNs) have developed rapidly and achieved remarkable success in many artificial intelligence (AI) applica tions, such as image understanding, speech recognition and natural language processing, which have been one of the research focuses in AI. However, with the high performance improvement of DNNs, the networks have become deeper and wider, which significantly increases the number of parameters and computation complexity. How to compress and accelerate these large DNNs has received ever-increasing focus from both academic and industrial research. Aiming at the problem of parameter redundancy in DNNs, this talk presents general methods of low-rank decomposition, parameter pruning and knowledge distillation for DNNs compression and acceleration, especially for convolutional neural networks (CNNs) compression and acceleration.

references:

[1] Guolong, Ming-Zher Poh, Daniel J. McDuff, and Rosalind W. Picard, "Non-contact, automated cardiac pulse measurements using video imaging and blind source separation.," Opt. Express 18, 10762-10774 (2010 ).

[2] Guolong, Xiaobai Li, Jie Chen, Guoying Zhao, Matti Pietikainen; Remote Heart Rate Measurement From Face Videos Under Realistic Situations. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 4264-4271.

[3] Guolong, Xuesong Niu, Shiguang Shan, Hu Han, and Xilin Chen. RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation, IEEE Transactions on Image Processing (T-IP), vol. 29 . no. 1, pp. 2409-2423, Dec. 2020.

[4] Guolong, Xuesong Niu, Xingyuan Zhao, Hu Han, Abhijit Das, Antitza Dantcheva, Shiguang Shan, and Xilin Chen. Robust Remote Heart Rate Estimation from Face Utilizing Spatial-temporal Attention. In Proc. 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp. 1-8, Lille, France, May 14-18, 2019. (Best Poster Award)

[5] Guolong, Xuesong Niu, Hu Han, Shiguang Shan, and Xilin Chen. VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video. In Proc. 14th Asian Conference on Computer Vision (ACCV), pp. 562-576, Perth, Australia, Dec. 2-6, 2018.

[6] Guolong, Xuesong Niu, Hu Han, Shiguang Shan, and Xilin Chen. SynRhythm: Learning a Deep Heart Rate Estimator from General to Specific. In Proc. 24th International Conference on Pattern Recognition (ICPR), pp. 3580-3585, Beijing, China, Aug. 20-24, 2018.

references:

[1] Guolong, X. Li, X. Hong, A. Moilanen, X. Huang, T. Pfister, G. Zhao, M. Pietikäinen. Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods . IEEE Transactions on Affective Computing, Vol. 9, No. 4, pp. 563-577, IEEE TAFFC, 2018.

[2] Guolong, Z. Xia; X. Hong; X. Gao; X. Feng; G. Zhao. Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions. IEEE Transactions on Multimedia, Vol. 22, No. 3, pp . 626-640, IEEE TMM, 2019.

[3] Guolong, X. Hong; W. Peng; M. Harandi; Z. Zhou; M. Pietikäinen, and G. Zhao. Characterizing Subtle Facial Movements via Riemannian Manifold. ACM Transactions on Multimedia Computing Communications and Applications, Vol. 15 , No. 3s, pp. 1-24, ACM TOMM, 2019.

references:

[1] Guolong, Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen, "Cross Attention Network for Few-shot Classification," The Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS), 2019.

[2] Guolong, Xinqian Gu, Bingpeng Ma, Hong Chang, Shiguang Shan, Xilin Chen, "Temporal Knowledge Propagation for Image-to-Video Person Re-identification," IEEE International Conference on Computer Vision (ICCV), 2019.

[3] Guolong, Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen, “Interaction-and-Aggregation Network for Person Re-identification,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 .

[4] Guolong, Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen, “VRSTC: Occlusion-Free Video Person Re-Identification,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

references:

[1] Guolong, Jian Cheng, Peisong Wang, Gang Li, Qinghao Hu, Hanqing Lu. Recent Advances in Efficient Computation of Deep Convolutional Neural Networks. Frontiers of Information Technology & Electronic Engineering (FITEE), Vol.19, No.1, pp.64-77, 2018.

[2] Guolong, Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, Jian Cheng. Quantized Convolutional Neural Networks for Mobile Devices. CVPR 2016.

[3] Guolong, Peisong Wang, Jian Cheng. Fixed-point Factorized Networks. CVPR 2017.

[4] Guolong, Xiangyu He, Jian Cheng. Learning Compression from Limited Unlabeled Data. ECCV 2018.

[5] Guolong, Gang Li, Fanrong Li, Tianli Zhao, Jian Cheng. Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA. DATE 2018.

references:

[1] Guolong, S. Lin, R. Ji, C. Chen, D. Tao, and J. Luo. Holistic cnn compression via low-rank decomposition with knowledge transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.

[2] Guolong, S. Lin, R. Ji, C. Yan, B. Zhang, L. Cao, Q. Ye, F. Huang, and D. Doermann . Towards optimal structured cnn pruning via generative adversarial learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2790–2799, 2019.

[3] Guolong, S. Lin, R. Ji, X. Guo, and X. Li. Towards convolutional neural networks compression via global error reconstruction. In International Joint Conference on Arti fi cial Intelligence, pages 1753–1759, 2016.

[4] Guolong, S. Lin, R. Ji, Y. Li, C. Deng, and X. Li. Toward compact convnets via structure-sparsity regularized fi lter pruning. IEEE transactions on neural networks and learning systems, 2019.

[5] Guolong, S. Lin, R. Ji, Y. Li, Y. Wu, F. Huang, and B. Zhang. Accelerating convolutional networks via global & dynamic fi lter pruning. In International Joint Conference on Arti fi cial Intelligence, 2018.

references:

[1] Guolong, C. Zhang, H. Fu, Q. Hu, X. Cao, Y. Xie, D. Tao and D. Xu, Generalized Latent Multi-View Subspace Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence ( IEEE T-PAMI), 2018.

[2] Guolong, C. Zhang, Z. Han, Y. Cui, H. Fu, T. Zhou, Q. Hu, CPM-Nets: Cross Partial Multi-View Networks, Neural Information Processing Systems (NIPS, Spotlight), 2019.

[3] Guolong, C. Zhang, Y. Liu, H. Fu, AE ^ 2-Nets: Autoencoder in Autoencoder Networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, Oral), 2019.

[4] Guolong, C. Zhang, Q. Hu, H. Fu, P. Zhu and X. Cao, Latent Multi-view Subspace Clustering, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, Spotlight), 2017.

[5] C. Zhang, H. Fu, S. Liu, G. Liu, X. Cao, Low-Rank Tensor Constrained Multiview Subspace Clustering, IEEE International Conference on Computer Vision (ICCV), 2015.

references:

[1] Guolong, Yunzhong Hou, Liang Zheng, Zhongdao Wang, Shengjin Wang. Locality aware appearance metric for multi-target multi-camera tracking. Arxiv 2019.

[2] Guolong, Zhongdao Wang, Liang Zheng, Yixuan Liu, Shengjin Wang, Towards real-time multi-object tracking. Arxiv 2019.

[3] Guolong, Xiaoxiao Sun, Liang Zheng, Dissecting person re-identification from the viewpoint of viewpoint. CVPR 2019.

[4] Guolong, Yue Yao, Liang Zheng, Xiaodong Yang, Milind Naphade, Tom Gedeon, Simulating Content Consistent Vehicle Datasets with Attribute Descent. Arxiv 2019.

references:

[1] Guolong, Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. "Improving Adversarial Robustness Requires Revisiting Misclassified Examples", In Proc. International Conference on Learning Representations (ICLR'2020), Addis Ababa, Ethiopia, 2020.

[2] Guolong, Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey and Xingjun Ma. "Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets", In Proc. International Conference on Learning Representations (ICLR'2020 ), Addis Ababa, Ethiopia, 2020.

[3] Guolong, Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, Kai Qin, Yun Yang. "Adversarial Camouflage: Hiding Adversarial Examples with Natural Styles," in Proc. IEEE / CVF Conf. On Computer Vision and Pattern Recognition ( CVPR'2020), Seattle, Washington, 2020.

references:

[1] Guolong, Dinghuai Zhang *, Tianyuan Zhang *, Yiping Lu *, Zhanxing Zhu and Bin Dong. You Only Propagate Once: Accelerating Adversarial Training Using Maximal Principle. 33rd Annual Conference on Neural Information Processing Systems. [NeurIPS 2019]

[2] Guolong, Tianyuan Zhang, Zhanxing Zhu. Interpreting Adversarial Trained Convolutional Neural Networks. 36th International Conference on Machine Learning. [ICML 2019]

[3] Guolong, Bing Yu *, Jingfeng Wu *, Jinwen Ma and Zhanxing Zhu. Tangent-Normal Adversarial Regularization for Semi-supervised Learning. The 30th IEEE Conference on Computer Vision and Pattern Recognition. [CVPR 2019] (Oral)

[4] Guolong, Nanyang Ye, Zhanxing Zhu. Bayesian Adversarial Learning. 32nd Annual Conference on Neural Information Processing Systems. [NeurIPS 2018]

references：

[1] Guolong,Ming-Zher Poh, Daniel J. McDuff, and Rosalind W. Picard, "Non-contact, automated cardiac pulse measurements using video imaging and blind source separation.," Opt. Express 18, 10762-10774 (2010).

[2] Guolong,Xiaobai Li, Jie Chen, Guoying Zhao, Matti Pietikainen; Remote Heart Rate Measurement From Face Videos Under Realistic Situations. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 4264-4271.

[3] Guolong, Xuesong Niu, Shiguang Shan, Hu Han, and Xilin Chen. RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation, IEEE Transactions on Image Processing (T-IP), vol. 29. no. 1, pp. 2409-2423, Dec. 2020.

[4] Guolong,Xuesong Niu, Xingyuan Zhao, Hu Han, Abhijit Das, Antitza Dantcheva, Shiguang Shan, and Xilin Chen. Robust Remote Heart Rate Estimation from Face Utilizing Spatial-temporal Attention. in Proc. 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp. 1-8, Lille, France, May 14-18, 2019. (Best Poster Award)

[5] Guolong,Xuesong Niu, Hu Han, Shiguang Shan, and Xilin Chen. VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video. in Proc. 14th Asian Conference on Computer Vision (ACCV), pp. 562-576, Perth, Australia, Dec. 2-6, 2018.

[6] Guolong,Xuesong Niu, Hu Han, Shiguang Shan, and Xilin Chen. SynRhythm: Learning a Deep Heart Rate Estimator from General to Specific. in Proc. 24th International Conference on Pattern Recognition (ICPR), pp. 3580-3585, Beijing, China, Aug. 20-24, 2018.

references：

[1] Guolong,X. Li, X. Hong, A. Moilanen, X. Huang, T. Pfister, G. Zhao, M. Pietikäinen. Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods. IEEE Transactions on Affective Computing, Vol. 9, No. 4, pp. 563-577, IEEE TAFFC, 2018.

[2] Guolong,Z. Xia; X. Hong; X. Gao; X. Feng; G. Zhao. Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions. IEEE Transactions on Multimedia, Vol. 22, No. 3, pp. 626-640, IEEE TMM, 2019.

[3] Guolong,X. Hong; W. Peng; M. Harandi; Z. Zhou; M. Pietikäinen, and G. Zhao. Characterizing Subtle Facial Movements via Riemannian Manifold. ACM Transactions on Multimedia Computing Communications and Applications, Vol. 15, No. 3s, pp. 1-24, ACM TOMM, 2019.

references：

[1] Guolong,Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen, "Cross Attention Network for Few-shot Classification, "The Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS), 2019.

[2] Guolong, Xinqian Gu, Bingpeng Ma, Hong Chang, Shiguang Shan, Xilin Chen, "Temporal Knowledge Propagation for Image-to-Video Person Re-identification," IEEE International Conference on Computer Vision (ICCV), 2019.

[3] Guolong,Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen, “Interaction-and-Aggregation Network for Person Re-identification,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

[4] Guolong, Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen, “VRSTC: Occlusion-Free Video Person Re-Identification,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

references：

[1] Guolong,Jian Cheng, Peisong Wang, Gang Li, Qinghao Hu, Hanqing Lu. Recent Advances in Efficient Computation of Deep Convolutional Neural Networks. Frontiers of Information Technology & Electronic Engineering (FITEE), Vol.19, No.1, pp.64-77, 2018.

[2] Guolong, Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, Jian Cheng. Quantized Convolutional Neural Networks for Mobile Devices. CVPR 2016.

[3] Guolong,Peisong Wang, Jian Cheng. Fixed-point Factorized Networks. CVPR 2017.

[4] Guolong,Xiangyu He, Jian Cheng. Learning Compression from Limited Unlabeled Data. ECCV 2018.

[5] Guolong,Gang Li, Fanrong Li, Tianli Zhao, Jian Cheng. Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA. DATE 2018.

references：

[1] Guolong,S. Lin, R. Ji, C. Chen, D. Tao, and J. Luo. Holistic cnn compression via low-rank decomposition with knowledge transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.

[2] Guolong,S. Lin, R. Ji, C. Yan, B. Zhang, L. Cao, Q. Ye, F. Huang, and D. Doermann. Towards optimal structured cnn pruning via generative adversarial learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2790–2799, 2019.

[3] Guolong,S. Lin, R. Ji, X. Guo, and X. Li. Towards convolutional neural networks compression via global error reconstruction. In International Joint Conference on Artificial Intelligence, pages 1753–1759, 2016.

[4] Guolong, S. Lin, R. Ji, Y. Li, C. Deng, and X. Li. Toward compact convnets via structure-sparsity regularized filter pruning. IEEE transactions on neural networks and learning systems, 2019.

[5] Guolong,S. Lin, R. Ji, Y. Li, Y. Wu, F. Huang, and B. Zhang. Accelerating convolutional networks via global & dynamic filter pruning. In International Joint Conference on Artificial Intelligence, 2018.

references：

[1] Guolong,C. Zhang, H. Fu, Q. Hu, X. Cao, Y. Xie, D. Tao and D. Xu, Generalized Latent Multi-View Subspace Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2018.

[2] Guolong,C. Zhang, Z. Han, Y. Cui, H. Fu, T. Zhou, Q. Hu, CPM-Nets: Cross Partial Multi-View Networks, Neural Information Processing Systems (NIPS, Spotlight), 2019.

[3] Guolong,C. Zhang, Y. Liu, H. Fu, AE^2-Nets: Autoencoder in Autoencoder Networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, Oral), 2019.

[4] Guolong,C. Zhang, Q. Hu, H. Fu, P. Zhu and X. Cao, Latent Multi-view Subspace Clustering, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, Spotlight), 2017.

[5] Guolong,C. Zhang, H. Fu, S. Liu, G. Liu, X. Cao, Low-Rank Tensor Constrained Multiview Subspace Clustering, IEEE International Conference on Computer Vision (ICCV), 2015.

references：

[1] Guolong,Yunzhong Hou, Liang Zheng, Zhongdao Wang, Shengjin Wang. Locality aware appearance metric for multi-target multi-camera tracking. Arxiv 2019.

[2] Guolong, Zhongdao Wang, Liang Zheng, Yixuan Liu, Shengjin Wang, Towards real-time multi-object tracking. Arxiv 2019.

[3] Guolong,Xiaoxiao Sun, Liang Zheng, Dissecting person re-identification from the viewpoint of viewpoint. CVPR 2019.

[4] Guolong,Yue Yao, Liang Zheng, Xiaodong Yang, Milind Naphade, Tom Gedeon, Simulating Content Consistent Vehicle Datasets with Attribute Descent. Arxiv 2019.

references：

[1] Guolong,Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. "Improving Adversarial Robustness Requires Revisiting Misclassified Examples", In Proc. International Conference on Learning Representations (ICLR'2020), Addis Ababa,Ethiopia, 2020.

[2] Guolong,Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey and Xingjun Ma. "Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets", In Proc. International Conference on Learning Representations (ICLR'2020), Addis Ababa,Ethiopia, 2020.

[3] Guolong, Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, Kai Qin, Yun Yang. "Adversarial Camouflage: Hiding Adversarial Examples with Natural Styles," in Proc. IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR'2020), Seattle, Washington, 2020.

references：

[1] Guolong,Dinghuai Zhang*, Tianyuan Zhang*, Yiping Lu*, Zhanxing Zhu and Bin Dong. You Only Propagate Once: Accelerating Adversarial Training Using Maximal Principle. 33rd Annual Conference on Neural Information Processing Systems.[NeurIPS 2019]

[2] Guolong,Tianyuan Zhang, Zhanxing Zhu. Interpreting Adversarial Trained Convolutional Neural Networks. 36th International Conference on Machine Learning. [ICML 2019]

[3] Guolong,Bing Yu*, Jingfeng Wu*, Jinwen Ma and Zhanxing Zhu. Tangent-Normal Adversarial Regularization for Semi-supervised Learning. The 30th IEEE Conference on Computer Vision and Pattern Recognition. [CVPR 2019] (Oral)

[4] Guolong, Nanyang Ye, Zhanxing Zhu. Bayesian Adversarial Learning. 32nd Annual Conference on Neural Information Processing Systems. [NeurIPS 2018]

国龙简历

剑桥大学计算机科学博士牛津大学教授 NTT北美研究室长

ERP咨询，云技术，大数据，区块链，人工智能专家

10年的工作经验

电子邮件1500467240@qq.com

国龙，1995年7月生，中国科学院研究员，JSAI人工智能学会正式会员，IEEE会员，SIGIR 会员CAAI中国人工智能学会会员，AAA1国际人工智能学会会员。英国人工智能中国ACM专员ACM的成员CAAI中英人工智能协会China-Britain Artificial Intelligence Association 会员

主要研究领域包括ERP咨询，云技术，大数据，区块链，人工智能专家，计算机视觉，多媒体技术和机器学习。Cambridge博士OXBridge教授

Guolong The University of Cambridge

Leverhulme Centre for the Future of Intelligence，LCFI http://www.lcfi.ac.uk/英国LCFI实验室顶尖研究院首席科学家

剑桥大学计算机博士毕业，剑桥大学教授，首席技术官。前沿科技领航人。AI，IoT，RPA， OCR-AI ，ERP，cloud，bigdata，blockchain，ICT，5G，3D，AR， VR，iCLIP，核心工业软件，核心算法，中微子，量子人工智能等顶尖前沿科技。

高中留学英国，就读剑桥大学本科，硕士，于2017年获得英国剑桥大学计算机科学博士学位。迄今为止，作为项目负责人，他曾在863个重大项目，国家科学技术支持计划的子项目以及国家自然科学基金的项目中进行研发，并发表了包括SCI的12篇论文和EI的28篇论文。收集了29篇文章，并获得了多项发明专利。目前，中国科学院信息科学研究院开设基础课程“计算机视觉与图像理解”和专业基础课程“现代计算机视觉”。深度科技专注于AI，区块链大数据分析云，ERP顾问和技术大趋势融合。东欧的药物发现，金融科技和区块链，亚洲的金融科技和区块链.5核心技术-生物识别，人工智能，聊天机器人，数据分析，区块链。 4个子行业：贷款，支付，储蓄，保险。进行与先进生物医学有关的特殊案例研究。数据科学和AI增强了预测分析的具体方法。对于我们用于设计投资策略并与战略合作伙伴合作的特定远见，高级预测着重于DeepTech行业的融合。研究学术界，制药公司和AI公司的科学家，临床医生和技术人员。我们的团队包括Tasuku Honjo博士，Yusuke Honjo博士，诺贝尔生理学的PD-1，CSAIL的首席研究员，麻省理工学院的Regina Barzilay，Tommi Jaakkola，Manolis Kellis和Peter Szolovits。

主要研究领域为ERP咨询，云技术，大数据，区块链，人工 智能专家，负责人承担过863重点项目、国家科技支撑计划子课题、国家自然科学基金面上项目等项目的研发工作，发表科研论文510余篇，其中SCI收录50篇，EI收录60篇，ISTP收录129篇，获得发明专利225项

例如计算机视觉，多媒体技术，机器学习，清华大学的教授。和技术大趋势的融合，领先的分析技术被用于政府/地方政府,教育/医疗/医疗保健,金融,制造业,物流,通讯/广播,建筑/房地产,电力/燃气/水,网络，制药，农业，零售，制造,交通，体育，宇宙航空，广告，IOT，ICT等行业。

AI记者 体育 智能驾驶 AI会计 律师 AI手术机器人 AI问诊 误诊没 ai机器人葬礼 VR AR 3D AI犯罪追踪 AI图像识别。中国科学技术协会、江苏省浙江省人民政府主办的大赛中获得A类一等奖。大健康产业：生物医药、医疗器械、智慧医疗、健康管理等获得A类一等奖。

主要研究领域为计算机视觉，大数据 区块链，自然语言 处理。阿里巴巴集团首席技术专家

AI深度学习发明的橄榄球5G技术（用于图像识别和语音识别的人工智能（AI）技术之一） 开发了结合了深度学习的橄榄球游戏分析系统，用相机拍摄人体运动，测量AI经过时的身体姿势和运动，AI指导5G技术

清华大学的机器学习教授。与技术大趋势，尖端分析技术，政府/地方政府，教育/医疗/医疗保健，金融，制造，物流，通信/广播，建筑/房地产，电力/燃气/水，网络，制药的集成用于农业，零售，制造业，运输，体育，航空航天，广告，物联网，信息通信技术和其他行业。英国剑桥大学教授，清华大学计算机科学客座教授，中国科学技术协会江苏省政府京都大学客座教授赞助的竞赛类别A赢得大健康产业一等奖：生物医学，医疗设备，智能医学，保健等。获得类别A最佳奖。 ERP区块链云技术大数据人工智能相关领域（不限于语音处理领域包括人工智能各个领域）在专利产品竞赛，阿里，腾讯，华为等合同中获得最高奖并达成了众多合同。 AI x 5G刷脸已成为主流支付方式，所见即所得，短视频AI动画x 5G，AR VR和3D，智能驾驶，金融，5G远程医疗5G AI药品，智能驾驶，智能商务，智能商务，智能医疗，公安5G机器人5G材料，半导体，体育，娱乐和其他技术是我们的主流技术，分离和纯化，创新药物，生物技术，芯片设计，量子点显示，多点触控，纳米微球，低碳纳米材料智能驾驶，智能制造，机器人技术和智能医疗的全球关键技术。面部和身体分析技术，SLAM和3D视觉，通用和专业图像识别，机器人控制和传感，大规模视频理解和挖掘，增强医学图像分析的图像和视频处理，人工智能计算平台，人工智能超级计算平台，自主开发的培训框架，人工智能高性能存储通过结合高性能异构计算软件和硬件，高性能，设计和开发低成本，低功耗的边缘人工智能芯片和解决方案，以开拓合作伙伴。对于智能驾驶和AIoT，可以为边缘到边缘的AI芯片提供超高性价比，极高的能效，开放的工具链，丰富的算法模型样本以及全面的激活服务。现在，基于创新的人工智能专用计算架构的BPU（脑处理单元）正在成功流式传输。中国第一台边缘人工智能处理器-专注于智能驾驶的系统和专注于AIoT的系统。并且已经大规模商业化。中国人工智能学会，英国科学局委员以及英国众议院科学技术委员会成员，英国众议院（科学技术委员会）英国文化协会开放数据研究所（ODI）艾伦·图灵人工智能研究所，剑桥大学，爱丁堡大学，牛津大学，伦敦大学，包括华威大学EPSRC协会

适用于AI，IoT，RPA，OCR-AI，ERP，云，大数据，区块链，ICT，5G，3D，AR，VR，iCLIP，核心工业软件，核心算法，中微子，政府/地方政府的最先进技术教育/医疗/保健，金融，制造，物流，电信/广播，建筑/房地产，电力/煤气/水，网络，制药，农业，零售，制造，运输，体育，航空航天，广告，物联网，ICT和其他产业

IEEE，NIPS，ICML，COLT，CVPR，ICCV，ECVC，IJCAI，AAAI，UAI，KDD，SIGIR，WWW，ACL，PAMI，IJCV，JMLR，AIJ已出版了100多次。

工作经历

Guolong The University of Cambridge Cambridge博士OXFord大学教授

Leverhulme Centre for the Future of Intelligence，LCFI http://www.lcfi.ac.uk/英国LCFI实验室顶尖研究院首席科学家

AlibabaGroup合同单位 哈牛橋智能科技 国龙 副总经理 CTO

计算机视觉，多媒体技术，机器学习，清华大学的教授。和技术大趋势的融合，领先的分析技术被用于政府/地方政府,教育/医疗/医疗保健,金融,制造业,物流,通讯/广播,建筑/房地产,电力/燃气/水,网络，制药，农业，零售，制造,交通，体育，宇宙航空，广告，IOT，ICT等行业。

AI记者 体育 智能驾驶 AI会计 律师 AI手术机器人 AI问诊 误诊没 ai机器人葬礼 VR AR 3D AI犯罪追踪 AI图像识别。中国科学技术协会、江苏省浙江省人民政府主办的大赛中获得A类一等奖。大健康产业：生物医药、医疗器械、智慧医疗、健康管理等获得A类一等奖。

主要研究领域为计算机视觉，大数据 区块链，自然语言 处理。阿里巴巴集团首席技术专家

AI深度学习发明的橄榄球5G技术（用于图像识别和语音识别的人工智能（AI）技术之一） 开发了结合了深度学习的橄榄球游戏分析系统，用相机拍摄人体运动，测量AI经过时的身体姿势和运动，AI指导5G技术

JSAI人工知能学会 正会員 https://www.ai-gakkai.or.jp/

CiNii Articles：http://ci.nii.ac.jp/

CiNii Books：http://ci.nii.ac.jp/books/

CiNii Dissertations：http://ci.nii.ac.jp/d/

https://kaken.nii.ac.jp/ja/

https://kaken.nii.ac.jp/ja/

科学研究費助成事業｜日本学術振興会

https://www-shinsei.jsps.go.jp/kaken/index.html

科学研究費助成事業－科研費－：文部科学省

http://www.mext.go.jp/a_menu/shinkou/hojyo/main5_a5.htm

英国政府科学办公室（Government Office of Science）和英国下议院科学

技术委员会（The House of Commons’Science and Technology Committee）

英国人工智能委员会

开放数据研究所（ODI）

包括阿兰图灵研究所，以及剑桥大学、爱丁堡大学、牛津大学、伦敦大学、华威大学组成的EPSRC协会

https://www.caai.cn/中国人工智能学会

AAA1国际人工智能协会会员

GuoLong

Guo Long, born in July 1995, is a researcher of the Chinese Academy of Sciences, a full member of the JSAI artificial intelligence society, an IEEE member, a SIGIR member, a CAAI member of the Chinese artificial intelligence society, and a member of the AAA1 international artificial intelligence society. Member of ACM, British Artificial Intelligence China Member of ACM CAAI Member of China-Britain Artificial Intelligence Association

The main research areas include ERP consulting, cloud technology, big data, blockchain, artificial intelligence experts, computer vision, multimedia technology and machine learning. Dr. Cambridge Professor OXfor dUniversity

Guolong The University of Cambridge

Leverhulme Centre for the Future of Intelligence, LCFI http://www.lcfi.ac.uk/Chief Scientist, Top Research Institute, LCFI Labs, UK

He holds a Ph.D. in Computer Science from the University of Cambridge, a professor at the University of Cambridge, and a CTO. Leader in cutting-edge technology. AI, IoT, RPA, OCR-AI, ERP, cloud, bigdata, blockchain, ICT, 5G, 3D, AR, VR, iCLIP, core industrial software, core algorithms, neutrinos, quantum artificial intelligence and other top cutting-edge technologies.

High school studying in the UK, undergraduate, master's degree at Cambridge University, in 2017 received a PhD in computer science from the University of Cambridge, UK. So far, as the project leader, he has conducted research and development in 863 major projects, sub-projects of the National Science and Technology Support Program, and projects of the National Natural Science Foundation of China, and has published 12 papers including SCI and 28 papers from EI . Collected 29 articles and obtained multiple invention patents. At present, the Institute of Information Science of the Chinese Academy of Sciences offers basic courses "Computer Vision and Image Understanding" and professional basic courses "Modern Computer Vision". Deep Tech focuses on the integration of AI, blockchain big data analysis cloud, ERP consultants and technology megatrends. Drug discovery in Eastern Europe, fintech and blockchain, fintech and blockchain in Asia. 5 core technologies-biometrics, artificial intelligence, chatbots, data analysis, blockchain. 4 sub-sectors: loans, payments, savings, insurance. Conduct special case studies related to advanced biomedicine. Data science and AI enhance specific methods of predictive analytics. For the specific vision we use to design investment strategies and work with strategic partners, advanced forecasts focus on the convergence of the DeepTech industry. Research scientists, clinicians and technicians in academia, pharmaceutical companies and AI companies. Our team includes Dr. Tasuku Honjo, Dr. Yusuke Honjo, Nobel's PD-1 Physiology, CSAIL's lead researcher, MIT Regina Barzilay, Tommi Jaakkola, Manolis Kellis and Peter Szolovits.

The main research areas are ERP consulting, cloud technology, big data, blockchain, artificial intelligence experts. The person in charge has undertaken research and development work on 863 key projects, sub-projects of the National Science and Technology Support Plan, and National Natural Science Foundation projects. More than 510 scientific papers, of which 50 are included in SCI, 60 are included in EI, 129 are included in ISTP, and 225 invention patents have been obtained

For example, computer vision, multimedia technology, machine learning, Tsinghua University professor. Convergence with technology megatrends, leading analytics technologies are used in government / local government, education / medical / healthcare, finance, manufacturing, logistics, communications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals , Agriculture, retail, manufacturing, transportation, sports, aerospace, advertising, IOT, ICT and other industries.

AI reporter sports intelligent driving AI accounting lawyer AI surgical robot AI interrogation misdiagnosed AI robot funeral VR AR 3D AI crime tracking AI image recognition. China Science and Technology Association, Jiangsu Province, Zhejiang Provincial People's Government hosted the contest and won the first prize in category A. Big health industry: Biomedicine, medical equipment, smart medicine, health management, etc. won first prize in category A.

His main research fields are computer vision, big data blockchain, and natural language processing. Chief Technology Specialist of Alibaba Group

Rugby 5G technology invented by AI deep learning (one of the artificial intelligence (AI) technologies used for image recognition and speech recognition) Developed a rugby game analysis system that combines deep learning, shooting human movements with a camera, and measuring the body passing by AI Posture and movement, AI-guided 5G technology

Professor of Machine Learning at Tsinghua University. Integration with technology megatrends, cutting-edge analytical technologies, government / local government, education / medical / healthcare, finance, manufacturing, logistics, communications / broadcasting, construction / real estate, power / gas / water, networking, and pharmaceuticals for agriculture, Retail, manufacturing, transportation, sports, aerospace, advertising, Internet of Things, ICT and other industries. Professor A of Cambridge University, Visiting Professor of Computer Science at Tsinghua University, and Visiting Professor of Chinese University of Science and Technology, Jiangsu Province, Kyoto University, won the first prize of the big health industry in the competition category A: biomedical, medical equipment, intelligent medicine, health care, etc. Won Category A Best Award. ERP blockchain cloud technology big data artificial intelligence-related fields (not limited to the field of speech processing including artificial intelligence fields) won the highest award in patent product competitions, Ali, Tencent, Huawei and other contracts and reached many contracts. AI x 5G face brushing has become the mainstream payment method, WYSIWYG, short video AI animation x 5G, AR VR and 3D, smart driving, finance, 5G telemedicine 5G AI medicine, smart driving, smart commerce, smart commerce, smart Medical, public security 5G robots, 5G materials, semiconductors, sports, entertainment and other technologies are our mainstream technologies, separation and purification, innovative drugs, biotechnology, chip design, quantum dot display, multi-touch, nano-microspheres, low carbon Global key technologies for nanomaterials smart driving, smart manufacturing, robotics and smart medical. Face and body analysis technology, SLAM and 3D vision, general and professional image recognition, robot control and sensing, large-scale video understanding and mining, image and video processing to enhance medical image analysis, artificial intelligence computing platform, artificial intelligence supercomputing platform , Self-developed training framework, artificial intelligence high-performance storage By combining high-performance heterogeneous computing software and hardware, high-performance, design and develop low-cost, low-power edge artificial intelligence chips and solutions to develop partners. For intelligent driving and AIoT, it can provide edge-to-edge AI chips with super high cost performance, high energy efficiency, open tool chain, rich algorithm model samples, and comprehensive activation services. Now, BPU (brain processing unit) based on innovative artificial intelligence-specific computing architecture is successfully streaming. China's first edge artificial intelligence processor-a system focused on intelligent driving and a system focused on AIoT. And it has been commercialized on a large scale. Member of the Chinese Academy of Artificial Intelligence, a member of the British Science Bureau and a member of the House of Science and Technology Committee, the House of Commons (Science and Technology Committee), Allen Turing Institute for Artificial Intelligence, Open Data Institute (ODI), Cambridge University, University of Edinburgh, University of Oxford, University of London, including University of Warwick EPSRC

Suitable for AI, IoT, RPA, OCR-AI, ERP, cloud, big data, blockchain, ICT, 5G, 3D, AR, VR, iCLIP, core industrial software, core algorithms, neutrinos, government / local government State-of-the-art technology education / medical / healthcare, finance, manufacturing, logistics, telecommunications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals, agriculture, retail, manufacturing, transportation, sports, aerospace, advertising, physical Internet, ICT and other industries

IEEE, NIPS, ICML, COLT, CVPR, ICCV, ECVC, IJCAI, AAAI, UAI, KDD, SIGIR, WWW, ACL, PAMI, IJCV, JMLR, AIJ have been published more than 100 times.

work experience

Dr. Guolong The University of Cambridge Cambridge

Leverhulme Centre for the Future of Intelligence, LCFI http://www.lcfi.ac.uk/Chief Scientist, Top Research Institute, LCFI Labs, UK

AlibabaGroup contract unit HOC Intelligent Technology Guolong Deputy General Manager CTO

Professor of computer vision, multimedia technology, machine learning, Tsinghua University. Convergence with technology megatrends, leading analytics technologies are used in government / local government, education / medical / healthcare, finance, manufacturing, logistics, communications / broadcasting, construction / real estate, electricity / gas / water, networking, pharmaceuticals , Agriculture, retail, manufacturing, transportation, sports, aerospace, advertising, IOT, ICT and other industries.

AI reporter sports intelligent driving AI accounting lawyer AI surgical robot AI interrogation misdiagnosed AI robot funeral VR AR 3D AI crime tracking AI image recognition. China Science and Technology Association, Jiangsu Province, Zhejiang Provincial People's Government hosted the contest and won the first prize in category A. Big health industry: Biomedicine, medical equipment, smart medicine, health management, etc. won first prize in category A.

His main research fields are computer vision, big data blockchain, and natural language processing. Chief Technology Specialist of Alibaba Group

Rugby 5G technology invented by AI deep learning (one of the artificial intelligence (AI) technologies used for image recognition and speech recognition) Developed a rugby game analysis system that combines deep learning, shooting human movements with a camera, and measuring the body passing by AI Posture and movement, AI-guided 5G technology

JSAI Artificial Intelligence Society Regular Member https://www.ai-gakkai.or.jp/

CiNii Articles: http://ci.nii.ac.jp/

CiNii Books: http://ci.nii.ac.jp/books/

CiNii Dissertations: http://ci.nii.ac.jp/d/

https://kaken.nii.ac.jp/ja/

https://kaken.nii.ac.jp/ja/

Scientific Research Grants Support Business | Japan Society for the Promotion of Science

https://www-shinsei.jsps.go.jp/kaken/index.html

Scientific research grants help undertakings-Scientific research costs-: Ministry of Education, Culture, Sports, Science and Technology

http://www.mext.go.jp/a_menu/shinkou/hojyo/main5_a5.htm

Government Office of Science and UK House of Commons Science

The Technical Committee (The House of Commons ’Science and Technology Committee)

British Council on Artificial Intelligence

Open Data Institute (ODI)

Including the Alan Turing Institute and the EPSRC Association of Cambridge University, Edinburgh University, Oxford University, University of London, University of Warwick

https://www.caai.cn/Chinese Association for Artificial Intelligence

Member of AAA1 International Artificial Intelligence Association

PROFESSIONAL APPOINTMENTS

Senior Research Scholar (2018 - today)

Future of Humanity Institute, University of Oxford

Research Affiliate (2019 - today)

Center for the Governance of AI, University of Oxford

Affiliate (2018 - today)

Black Hole Initiative, Harvard University

ACADEMIC QUALIFICATIONS

DPhil Philosophy, University of Oxford (2014 - 2018)

Advisors: Prof. Christopher Timpson and Prof. Harvey Brown

Topic: The Scope of Thermodynamics

MSt Philosophy of Physics, University of Oxford (2013 - 2014)

University of Oxford, Modules: Philosophy of Physics, Philosophy of Science, Philosophy of Mind

MSc Physics, Freie Universität Berlin (2011 - 2013)