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Lingfei Wu 吴凌飞(Chinese name)

Ph.D. (College of William and Mary)

CEO and Co-founder of Anytime AI

Armonk, New York, NY 10504

Email: lwu@anytime-ai.com, teddy.lfwu@gmail.com

Google Scholar, LinkedIn, Curriculum Vitae

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About: 

Dr. Lingfei Wu is a distinguished engineer and entrepreneur known for his contributions in artificial intelligence, machine learning, and natural language processing. Currently, he is the Co-founder and CEO of Anytime.AI, a generative AI startup that aims to boost efficiency and effectiveness in the legal field.

Dr. Wu earned his doctorate in computer science from the College of William and Mary.  He previously served as an engineering Leader in Content Understanding at Pinterest, leading a group of applied scientists, software engineers, and product managers on various projects using large language models (LLMs) and Generative AI technologies. Prior to Pinterest, he was a Principal Scientist at JD.COM Silicon Valley Research Center, leading a team to build next generation Large Language Models (LLMs)-powered E-commerce systems. Earlier in his career, Dr. Wu was a research staff member at IBM Thomas J. Watson Research Center and led a team developing novel Graph Neural Networks methods and systems. This work led to multiple awards and over 65 US. patents, earning him appointments as an IBM Master Inventor in 2020.

Dr. Wu is a TEDx speaker, a book author, and an accomplished research scientist in AI. His TEDx talk "AI in Law: A Mandate, Not An Option" is the first time that Legal AI has come into the TEDx Stage. His book "Graph Neural Networks: Foundations, Frontiers and Applications" published by Springer, has sold more than 10,000 hard copies and more than 300,000 digital copies have been downloaded worldwide. He has published more than 200 top-ranked conference and journal papers with 7000+ citations according to Google Scholar. His research has been recognized on multiple occasions, including receiving the Best Paper Award and Best Student Paper Award at several prominent conferences like IEEE ICC’19, AAAI workshop on DLGMA’20, and KDD workshop on DLG’19. Dr. Wu has also received extensive media attention, with his work being covered in various renowned outlets, including NatureNews, YahooNews, AP News, PR Newswire, The Time Weekly, Venturebeat, MIT News, IBM Research News, and SIAM News. Furthermore, Dr. Wu has served in a number of organizational roles in key industry events and societies, including sponsorship co-chairs of KDD'24, KDD'23 and KDD'22, the Industry and Government Program Co-Chairs of IEEE BigData'22 and the Associate Conference Co-Chairs of AAAI'21. He is the Associate Editor for prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems and ACM Transactions on Knowledge Discovery from Data.

Press coverage of my research: 

News: 

09/2024: Anytime AI Raises $4M to Build the Premier AI Legal Assistant for Plaintiff Lawyers! PR Newswire, AP News, Law360

09/2024: Delivered a TEDx talk "AI in Law: A Mandate, Not An Option" . This is the first time that Legal AI has come into the TEDx Stage!

07/2024: Delivered a generative AI tutorial on "Beyond Human Creativity:  A Tutorial on Advancements in AI Generated Content" at AAAI 2024!

02/2024: Delighted to deliver a tutorial titled "Beyond Human Creativity:  A Tutorial on Advancements in AI Generated Content" at AAAI 2024!

12/2023: One paper titled "KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs" has been accepted by AAAI 2024! 

11/2023: One paper titled "CGKPN: Cross-Graph Knowledge Propagation Network with Adaptive Connection for Reasoning-Based Machine Reading Comprehension" has been accepted by ACM TIST! 

10/2023: One paper titled “RustGraph: Robust Anomaly Detection in Dynamic Graphs by Jointly Learning Structural-Temporal Dependency” had been accepted by TKDE! 

10/2023: Oner paper titled "Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training" has been accepted by BIBM 2023!

10/2023: One paper titled "CP-BCS: Binary Code Summarization Guided by Control Flow Graph and Pseudo Code" has been accepted by EMNLP 2023!

5/2023: Delighted to deliver a keynote speech in the Knowledge Graph Day of TheWebConf 2023 and served as panelist to share thoughts on "Knowledge Graph and ChatGPT (LLMs)" . 

5/2023: One paper titled "Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation" has been accepted by ACM TROS!

5/2023: One paper titled "TeKo: Text-Rich Graph Neural Networks with External Knowledge" has been accepted by TNNLS! 

4/2023: One paper titled "SkillQG: Learning to Generate Question for Reading Comprehension Assessment" has been accepted by ACL'23!

4/2023: One paper titled "TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature" has been accepted by TNNLS! 

3/2023: One paper titled "Toward Subgraph Guided Knowledge Graph Question Generation with Graph Neural Networks" has been accepted by TNNLS! 

2/2023: Two papers on "Online Selling Point Extraction and Generation for E-commerce" and "Automatic Product Copywriting for E-commerce" have been accepted by AI Magazine! 

2/2023: Our GNN tutorial "Graph Neural Networks: Foundations, Frontiers, and Applications" in AAAI'23 has been well received and attracted more than 250 attendees! 

2/2023：One survey paper on "Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art" has been accepted by The Journal of Computers & Security!

2/2023: One paper on "GraphFlow+: Exploiting Conversation Flow in Conversational Machine Comprehension with Graph Neural Networks" has been acceped by The Journal of Machine Intelligence Research! 

1/2023: One paper on "KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks" has been accepted by TheWebConf'23! 

1/2023: Successfully held large online virtual GNN event "Graph Neural Networks: The Year in Review and Predictions for 2022 ", joint with Datafun and Post & Telecom Press! Revisit our recorded video on Datafun! 

12/2022: Our GNN tutorial "Graph Neural Networks: Foundations, Frontiers, and Applications" has been accepted by TheWebConf'23! 

11/2022: The Chinese Version of our GNN Book (图神经网络中文城堡书) has been published by Post & Telecom Press and accepts Pre-Order in JD.com! Get 50% Promotion now! 

11/2022: Two papers on "Graph Structure Learning with Incomplete Features and Structure" and "Deep Generative Learning for Urban Planning" have been accepted by AAAI'23! 

10/2022: One paper on "Meta Policy Learning for Cold-Start Conversational Recommendation" has been accepted by WSDM'23! 

09/2022: Our GNN tutorial "Graph Neural Networks: Foundations, Frontiers, and Applications" has been accepted by AAAI'23! 

09/2022: Our Deep Learning on Graph Workshop (DLG-AAAI'23) has been accepted by AAAI'23! 

09/2022: Two papers on "Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search" and "Automatic Scene-based Topic Channel Construction System for E-Commerce" have been accepted by EMNLP'22! 

09/2022:  One paper on "Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph" has been accepted by AACL-IJCNLP 2022 !

09/2022: Our graph4nlp long survey paper (190 pages) has been accepted by Foundations and Trends® in Machine Learning Journal! The first comprehensive overview of GNNs for NLP, covering around 500 papers in top AI conferences in last 5 years! 

09/2022: Our survey paper on ""Knowledge-aware Document Summarization" has been accepted by Knowledge-Based Systems Journal! 

08/2022: Delivered two KDD papers on "personalized product description" and "automatic image-sequence generation" in KDD'22! 

08/2022: Our joint DLG-MLG-KDD'22 workshop was a ver successful one which contains four keynotes and one insightful panel discussion, which which attracted more than 250 attendees! 

08/2022: Delivered a keynote speech on AutoML-KDD'22 Workshop on GNNs! 

08/2022: Our GNN tutorial "Graph Neural Networks: Foundations, Frontiers, and Applications" has been well received in KDD'22, which attracted more than 200 attendees! 

07/2022: Delighted to deliver our GNN tutorial "Graph Neural Networks: Foundations, Frontiers, and Applications" in IJCAI'22! 

07/2022: One paper on "BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning" has been accepted by IEEE Transaction on Big Data! 

5/2022: Two papers on "Controllable Product Copywriting" and "Automatic Generation of Product-Image Sequence" in e-commerce have been accepted by KDD'22! 

5/2022: Two papers on "Robust Meta-Learning via Eigen-Reptile" and "Input-agnostic Certified Group Fairness" have been accepted by ICML'22! 

4/2022: Our DLG-KDD22 workshops on "Deep Learning on Graphs: Methods and Applications" has been accepted by KDD'22!

3/2022: Delighted to deliver a talk on "Graph Neural Networks: Foundations, Frontiers, and Applications" in Machine Learning Lunch Seminar at Vanderbilt! Check out my talk video at Youtube!

2/2022: One paper titled "Feeding What You Need by Understanding What You Learned" has been accepted by ACL 2022!

2/2022: We delivered our DLG4NLP tutorial in AAAI'22! Checkout out our slides here! 

1/2022: One paper titled "Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks" has been accepted by ACM Transaction on CHI! 

1/2022: Two papers titled "Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation" and "Compact Graph Structure Learning via Mutual Information Compression" have been accepted by TheWebConf 2022!

1/2022: One paper titled "Deep Graph Translation" has been accepted by TNNLS! 

1/2022: Our GNN book titled "Graph Neural Networks Foundations, Frontiers, and Applications" is published by Springer! The book is available for pre-order on Springer, Amazon, and JD.COM! !

12/2021: Our tutorial "Deep Learning on Graphs for Natural Language Processing" has been accepted by TheWebConf 2022!

11/2021: Our DLG4NLP workshop on "Deep Learning on Graphs for Natural Language Processing" is also accepted by ICLR'22!

11/2021: One paper titled "MalGraph: Hierarchical Graph Neural Networks for Robust Windows Malware Detection" has been accepted by IEEE INFOCOM'22!

11/2021: One NAACL workshop on "Deep Learning on Graphs for Natural Language Processing" has been accepted by NAACL'22!

11/2021: Two award papers on "Automatic Product Copywriting for E-Commerce" and "Intelligent Online Selling Point Extraction for E-Commerce Recommendation" are accepted by AAAI/IAAI 2022! 

11/2021: Two WSDM workshops on "Machine Learning on Graphs" and "Interactive and Scalable Information Retrieval Methods for eCommerce" are accepted by WSDM'22!

09/2021: Our tutorial "Deep Learning on Graphs for Natural Language Processing" has been accepted by AAAI 2022!

09/2021： Two papers (Visual Question Generation with Noisy Supervision, Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory) have been accepted by NeurIPS'21! 

08/2021: Four papers (Timeline summarization, Text Classification, Text Adversarial and code documentation generation) have been accepted by EMNLP'21!

08/2021: We delivered our DLG4NLP tutorial in IJCAI'21! Checkout out our slides here! 

08/2021: Honored to serve as one of the panelists on VLDB2021 to talk about recent developments of GNNs in both research and industrial applications! 

08/2021: Two KDD workshops on IRS and DLG have successfully delivered! You are welcome to check out various talks videos from our keynotes and authors. 

08/2021: We delivered our DLG4NLP tutorial in KDD'21! Checkout out our slides here! 

08/2021: One paper titled "Sequential Search with Off-Policy Reinforcement Learning" has been accepted by CIKM'21!

07/2021: One paper titled "Hierarchical Graph Matching Networks for Deep Graph Similarity Learning" has been accepted by TNNLS! 

07/2021: Delivered a keynote talk about DLG4NLP at ICML'21 Workshop on Representation Learning for Finance and E-Commerce Applications! 

07/2021: Delivered an invited talk on "Deep Learning on Graphs for Natural Language Processing" at UT-Austin CS Department! 

07/2021: We delivered our DLG4NLP tutorial in SIGIR'21! Checkout out our slides and talks here! 

07/2021: One paper titled "Linking the Characters: Video-oriented Social Graph Generation via Hierarchical-cumulative GCN" has been accepted by ACM MM'21!

06/2021: One paper titled "Modeling Health Stage Development of Patients with Dynamic Attributed Graphs in Online Health Communities" has been accepted by TKDE !

06/2021: Delivered an invited talk on "Deep Learning on Graphs for Natural Language Processing" at Linkedin!

06/2021: Check out our most recent survey paper (about 127 pages and covering around 500 papers) , titled "Graph Neural Networks for Natural Language Processing: A Survey"! First comprehensive survey on GNNs for NLP! 

06/2021: We are delighted to release our Graph4NLP library, which is the first library for the easy use of GNNs for NLP! Try it out and let us know what you feel! 

06/2021: We just delivered a very successful tutorial titled "Deep Learning on Graphs for Natural Language Processing" in NAACL'21! Check out our slides via this link! 

05/2021: Our tutorial "Deep Learning on Graphs for Natural Language Processing" has been accepted by KDD'21!

05/2021: One paper titled "Attacking Graph Convolutional Networks via Rewiring" has been accepted by KDD'21!

05/2021: One paper titled "Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks" has been accepted by ICML'21!

04/2021: Our tutorial "Deep Learning on Graphs for Natural Language Processing" has been accepted by SIGIR'21!

04/2021: One paper titled "Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing" is accepted by TKDD!

04/2021: Our tutorial "Deep Learning on Graphs for Natural Language Processing" has been accepted by IJCAI'21!

03/2021: Two KDD workshops on "Deep Learning on Graphs" and "Industrial Recommendation System" are accepted by KDD'21!

03/2021: One paper titled "Technical Question Answering across Tasks and Domains" is accepted by NAACL'21!

02/2021: Delivered DLG-AAAI'21 Workshop on "Deep Learning on Graphs: Methods and Applications"  joint with Yinglong Xia, Jiliang Tang and Jian Pei, and Dawei Zhou. Attracted more than 210+ attendees in zoom!

01/2021: Honored to deliver a talk on Deep Graph Learning for GNN in AI Seminar at Jefferson National Lab!

01/2021: One paper on "Deep Graph Matching and Searching for Semantic Code Retrieval" is accepted by TKDD!

12/2020: Two papers on Neural QA for Code Summarization and Subroutines are accepted by SANER'21! 

12/2020: Very delighted to received the second IBM High Value Patent Award for our sequence embeddings idea! 

12/2020: I am delighted to receive IBM Research 2020 Accomplishment Award (for Research Contributions to Enterprise-Strength Federated Learning for Hybrid Cloud and Edge)

12/2020: Very honored to be appointed as Virtual Conference Chairs in KDD'21! Please consider submitting your best works to KDD'21! 

12/2020: Our tutorial "Deep Learning on Graphs for Natural Language Processing" has been accepted by NAACL'21!

12/2020: One paper on Relation Prediction in KG using novel GNNs is accepted by AAAI'21! 

11/2020: I am thrilled to receive IBM Research 2020 Outstanding Accomplishment Award (for Research Contributions to GTS TSS Multi-Vendor Services Transformation). 

11/2020: Honored to deliver a talk on Deep Graph Learning for GNN in R&D Seminar at Goldman Sachs!

11/2020: Honored to deliver a Guest Lecture on Deep Learning on Graphs at Vanderbilt University! 

11/2020: Very honored to be appointed as Associate Editor of IEEE Transactions on Neural Networks and Learning Systems! Please consider submitting your best works to TNNLS!

10/2020: I am thrilled to be appointed as IBM Master Inventor, Class of 2020! 

09/2020: One paper on Deep Graph Learning for Graph Neural Networks is accepted by NeurIPS'20! 

09/2020: Three papers on Summary Quality Evaluation, Semantic Parsing (Graph2Tree), and TechQA with Deep Transfer Learning are accepted by EMNLP'20! 

08/2020: Delivered KDD'20 Workshop on "Deep Learning on Graphs: Methods and Applications"  joint with Yinglong Xia and Jian Pei, and Co-organizers from MLG Workshop. Attracted more than 180 online attendees in zoom!

08/2020: Delivered KDD'20 tutorial on "Deep Graph Learning: Foundations, Advances and Applications" with researchers from IBM Research, Tencent AI Lab, and MSU. Attracted more than 150 online attendees in zoom! 

08/2020: Received IBM High Value Patent Award for our text embeddings idea! 

08/2020: Our NeurIPS 2020 Workshop on Scalability, Privacy, and Security in Federated Learning (SpicyFL 2020) has been accepted ! Welcome to submit your FL works to our workshop! 

08/2020: I have been invited as SPC of AAAI'21 and IJCAI'21! Happy to serve the community! 

07/2020: Received IBM Fifth/Sixth/Seventh Level Plateau Invention Achievement Awards, 2020!

06/2020: Delivered a keynote speech entitled "Deep Learning on Graphs in Natural Language Processing and Computer Vision" In the DIRA workshop at CVPR 2020! Video and Slides are available! 

06/2020: Very honored to be appointed as Associate Conference Chairs for Virtual Operations in AAAI'21! Please consider submitting your best works to AAAI'21! 

05/2020: Our tutorial entitled "Graph Neural Networks: Foundations, Models and Applications" has been accepted by KDD'20!

05/2020: One paper on Interpretable Graph Generation is accepted by KDD'20!

04/2020: Three papers on KB-to-Text, Conversational MRC, and Grounded Video Description are accepted by IJCAI'20! 

04/2020: Four papers on summarization, question retrieval, information extraction, and code matching are accepted by ACL'20!  

04/2020: Our proposal on "The Second International Workshop on Deep Learning on Graphs: Methods and Applications (KDD-DLG’20)" has been accepted by KDD 2020! Welcome to submit your works to our workshop!

03/2020: Lingfei has been recognized by IBM Outstanding Technical Achievement Award! 

03/2020: One paper on Code Summarization via GNNs is accepted by ICPC'20! 

03/2020: One paper on Automatic Summarization of Subroutines is accepted by MSR'20! 

02/2020: Delighted to receive the BEST STUDENT PAPER AWARD of DLGMA'20 joint with my RPI collaborators Yu Chen and Mohammed J Zaki! 

02/2020: our AAAI'20 Workshop on "Deep Learning on Graphs: Methodologies and Applications (DLGMA’20)" was a great success (#1 largest number of attendees) and many thanks for your supports! 

02/2020: Delivered a tutorial entitled "Graph Neural Networks: Models and Applications" in AAAI'20. If you are interested in Graph Neural Networks or Deep Learning on Graphs, this is for you! 

12/2019: Our paper on RL Based Graph2Seq for Question Generation is accepted by ICLR'20! Graph2Seq has been proved again to perform better than Seq2Seq! 

12/2019: Lingfei Wu has been invited as SPC of KDD'20, IJCAI'20, and AAAI'20!

11/2019: Honored and humbled to lead a team to receive 2019 IBM Research Accomplishment Award for contributions of fundamental research in graph neural network and its applications in a wide range of AI tasks!

11/2019: Delighted to receive NSF funding (Co-PI in IBM) of EFRI C3 SoRo for our proposal "EFRI C3 SoRo: Functional-Domain Soft Robots Precisely Controlled by Quantitative Dynamic Models and Data"!

11/2019: Lingfei Wu has been selected as one of the representatives at IBM to attend Yoshua Bengio's Turing Award event in Montreal at MILA!

11/2019: Lingfei Wu has been appointed as an Associate Editor for ACM Transactions on Knowledge Discovery from Data! Welcome to submit your works to this top-ranked data mining journal!

10/2019: Keynote/Invited talks on China International Symposium on Artificial Intelligence and Education in Hefei China, International Workshop on Machine Learning & Artificial Intelligence in Paris France, visiting UCLA, UCSB, Nanjing University, Zhejiang University, and Fudan University.

09/2019: Our tutorial proposal "Graph Neural Networks: Models and Applications" has been accepted by IEEE AAAI 2020! If you are working on GNNs or want to know more about GNNs, welcome to attend our tutorial in February!

09/2019: Our paper on Kernelized Graph Matching is accepted by NeurIPS'19 as a spotlight talk paper, acceptance rate: 3% (200/6743)!

08/2019: Our GNN workshop "Deep Learning on Graphs: Methodologies and Applications ​(DLGMA’19)" has been accepted by AAAI 2020! Welcome to submit your works to our workshop!

08/2019: One paper on Deep Classifier Cascades for Open World Recognition is accepted by CIKM'19!

08/2019: Two papers on Dynamic-Graph-to-Sequence Interpretable Learning and Feature Dependency Pattern Mining are accepted by the ICDM'19!

08/2019: Our innovative GNN models for RDF-to-Text, and Semantic Parsing have won BEST Paper Award, and BEST STUDENT PAPER Award in KDD workshop on Deep Learning on Graphs!

08/2019: Our DLG'19 workshop in KDD 2019 has attracted 400+ attendees to listen five world-class top researchers/industrial leaders in ML/DL/NLP! 

08/2019: Received the MIT-IBM Watson AI Lab Award (2019) for the proposal entitled "Intelligent Multi-Scale Design of De Novo Proteins to Enhance Food Security"!

06/2019: Three papers on Graph Neural Networks for RDF-to-Text, Semantic Parsing, and Graph Translation are accepted by the KDD 2019 Workshop on Deep Learning on Graphs: Methods and Applications!

05/2019: Our workshop paper on Graph Neural Networks for Conversational Machine Comprehension is accepted by the ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data!

05/2019: Our SysML 2019 paper on text adversarial attack and training is covered by NatureNews!

05/2019: Our AIAED2019 paper entitled "Toward Automated Queries Generation from Natural Language Description Using Graph Neural Networks" has won the BEST STUDENT PAPER Award! Big congrats to the team!

05/2019: Our ICC2019 paper entitled “DQ Scheduler: Deep Reinforcement Learning Based Controller Synchronization in Distributed SDN,” has won the BEST Paper Award! Big congrats to the team!

05/2019: One paper entitled "Similarity Preserving Representation Learning for Time Series Clustering" has been accepted by IJCAI 2019! Please see sister work on time-series embedding (RWS at AIStats 2018)

05/2019: Honored to be invited and delivered another talk about Graph-to-Sequence Learning in Natural Language Processing on SMSD Seminar Series at MIT !

05/2019: Our GNN workshop "Deep Graph Learning: Methodologies and Applications (DGLMA’19)" has been accepted by IEEE BigData 2019! Welcome to submit your works to our workshop!

04/2019: Two papers about efficient graph-level embeddings from node embeddings and scalable string embeddings for symbolic sequence inputs are accepted by KDD 2019!

04/2019: Honored to be invited and delivered another talk about Graph-to-Sequence Learning in Natural Language Processing on CSCE Graduate Seminar at TAMU!

04/2019: One paper entitled "Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications" is accepted by ICML 2019!

04/2019: Honored to be invited and delivered a talk about Graph-to-Sequence Learning in Natural Language Processing on CSE Lecture Series at MSU!

04/2019: Our paper on Scalable Attributed Graph Embeddings is accepted by the ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds!

04/2019: Our SysML19 paper on text classifier adversarial attacks featured by Venturebeat, TechTalks, and jiqizhixin. 

03/2019: Our KDD workshop "Deep Learning on Graphs: Methods and Applications (DLG'19)" has been accepted by KDD 2019! See you all on Deep Learning Day at KDD 2019!

03/2019: Delivered a talk for our SysML'19 paper entitled "Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification" at Stanford University!

03/2019: Delivered a seminar talk entitled "Graph-to-Sequence Learning in Natural Language Processing" in the Computer Science and Engineering Department at University of Notre Dame!

2/2019: Two papers about Text Style Transfer and Memory Networks for KBQA are accepted by NAACL'19!

1/2019: One paper entitled "DQ Scheduler: Deep Reinforcement Learning Based Controller Synchronization in Distributed SDN" is accepted by ICC 2019!

1/2019: RWS code was just released in Github! RWS is a simple code for generating the vector representation of time-series for classification and clustering. See our paper for more details. 

1/2019: RC_RB code was just released in Github! RC_RB is a simple code for scaling up spectral clustering on large-scale datasets using Random Binning and PRIMME. See our paper and IBM Research AI Blog for more details!

1/2019: One paper entitled "Discrete Attacks and Submodular Optimization with Applications to Text Classification" is accepted by SYSML 2019!

1/2019: Our MIT-IBM research project (Co-PI) - AI-Based Novel Protein Design is listed as featured projects.

12/2018: Our tutorial entitled "Unsupervised Feature Representation Learning via Random Features for Structured Data: Theory, Algorithm, and Applications" was delivered on IEEE Big Data 2018! 

11/2018: WME code code was just released in Github! WME is a simple code for generating universal text embedding for sentence, paragraph, and document. See our paper and IBM Research AI Blog for details!

11/2018: Graph2Seq code was just released in Github! Graph2Seq is a simple code for generalizing celebrated Seq2Seq model for Graph Inputs. See our arxiv paper and two EMNLP'18 papers for details!

11/2018: One paper entitled "From Node Embedding to Graph Embedding: Scalable Global Graph Kernel via Random Features" is accepted as an oral talk by NIPS18' workshop on Relational Representation Learning! 

11/2018: One work in EMNLP'18 on universal sentence/document embedding is featured on IBM Research AI Blog!

11/2018: Two of our EMNLP'18 works on semantic parsing and natural language generation are featured on IBM Research AI Blog!

10/2018: One paper entitled "Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting" is accepted by AAAI'19!

09/2018: RB_GEN codes were just released in Github! RB_GEN is a simple package for generating random binning features for solving large-scale kernel classification, regression, and clustering. See our KDD16 and KDD18 papers for more details!

09/2018: Honored to be selected as one of the top 30% highest-scoring reviewers at NIPS'18!

09/2018: Our KDD work on an end-to-end approach for scaling up spectral clustering is featured on IBM Research AI Blog!

08/2018: Three papers about unsupervised document embedding, SQL-to-Text, Text-to-logicform are accepted by EMNLP'18! 

08/2018: Received IBM Second level plateau Invention Achievement Award, 2018

05/2018: One paper entitled "Scalable Spectral Clustering Using Random Binning Features" gets accepted as an oral paper by KDD'18!

05/2018: Received IBM Manager's Choice Award, 2018!

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