Publications

Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, Xiaojie Guo, "Graph Neural Networks: Foundation, Frontiers and Applications", In the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI'22) Tutorial. [Website][Slides]

Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, Xiaojie Guo, "Graph Neural Networks: Foundation, Frontiers and Applications", In The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22) Tutorial. [Website][Slides]

Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, Xiaojie Guo, "Graph Neural Networks: Foundation, Frontiers and Applications", In the 31th International Joint Conference on Artificial Intelligence (IJCAI'22) Tutorial. [Website][Slides]

Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li and Bang Liu, "Deep Learning on Graphs for Natural Language Processing", In the 31st Conference in the International World Wide Web Conference (TheWebConf'22) Tutorial. [Website][Slides][Video][Codes][Demos]

Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li and Bang Liu, "Deep Learning on Graphs for Natural Language Processing", In the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI'22) Tutorial. [Website][Slides][Video][Codes][Demos]

Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li and Bang Liu, "Deep Learning on Graphs for Natural Language Processing", In the 30th International Joint Conference on Artificial Intelligence (IJCAI'21) Tutorial. [Website] [Slides][Video][Codes][Demos]

Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li and Bang Liu, "Deep Learning on Graphs for Natural Language Processing", In the Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'21) Tutorial. [Website] [Slides][Codes][Demos]

Lingfei Wu, Yu Chen, Heng Ji, and Bang Liu, "Deep Learning on Graphs for Natural Language Processing", In the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'21) Tutorial. [Website] [Slides][Video][Codes][Demos]

Lingfei Wu, Yu Chen, Heng Ji, and Yunyao Li, "Deep Learning on Graphs for Natural Language Processing", In the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL'21) Tutorial. [Website] [Slides][Video][Codes][Demos]

Yu Rong, Wenbing Huang, Tingyang Xu, Hong Cheng, Junzhou Huang, Yao Ma, Yiqi Wang, Tyler Derr, Lingfei Wu and Tengfei Ma, “Deep Graph Learning: Foundations, Advances and Applications”, In The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'20) Tutorial. [Website][Slides-P1, Slides-P2] [Video]

Lingfei Wu, "Deep Learning on Graphs in Natural Language Processing and Computer Vision", In the DIRA workshop at CVPR 2020, Seattle, WA, June 2020. [Website][Slides][Video]

Yao Ma, Wei Jin, Jiliang Tang, Lingfei Wu and Tengfei Ma, "Graph Neural Networks: Models and Applications", The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) Tutorial, New York, New York, USA, February 2020. [Website][PDF] 

Lingfei Wu, "Graph-to-Sequence Learning in Natural Language Processing", In the International Workshop on Machine Learning & Artificial Intelligence, Paris, France, October 2019. [Website] [PDF] [Video] [Codes]

Lingfei Wu, "Unsupervised Feature Representation Learning via Random Features for Structured Data: Theory, Algorithm, and Applications", IEEE Big Data 2018, Seattle, USA, December 2018. [Website] [PDF] [Video] [Codes]

Lingfei Wu, "Exploring the Potential of the PRIMME Eigensolver", SIAM Conference on Computational Science and Engineering, Atlanta, Georgia, USA, February 2017. [Website][PDF] [Video] [Codes]

Zhizhi Yu, Di Jin, Jianguo Wei, Ziyang Liu, Yue Shang, Yun Xiao, Jiawei Han, Lingfei Wu, "TeKo: Text-Rich Graph Neural Networks with External Knowledge". [PDF]

Di Jin, Cuiying Huo, Jianwu Dang, Peican Zhu, Weixiong Zhang, Witold Pedrycz, Lingfei Wu, "Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning". [PDF]

Cuiying Huo, Di Jin, Chundong Liang, Dongxiao He, Tie Qiu, Lingfei Wu, "TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature". [PDF]

Yangkai Du, Tengfei Ma, Lingfei Wu, Yiming Wu, Xuhong Zhang, Bo Long, Shouling Ji, "Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning". [PDF]

Xueying Zhang, Kai Shen, Chi Zhang, Xiaochuan Fan, Yun Xiao, Zhen He, Bo Long and Lingfei Wu, "Scenario-based Multi-product Advertising Copywriting Generation for E-Commerce". [PDF]

Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu C. Aggarwal, Chang-Tien Lu, "Bridging the Gap between Spatial and Spectral Domains: A Theoretical Framework for Graph Neural Networks". [PDF]

Qi Shen*, Lingfei Wu*,Yitong Pang, Yiming Zhang, Zhihua Wei, Fangli Xu,  and Bo Long (*Equally Contributed), "Multi-behavior Graph Contextual Aware Network for Session-based Recommendation ". [PDF]

Yiming Zhang*, Lingfei Wu*, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu,  Ethan Chang, and Bo Long (*Equally Contributed), "Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation". [PDF]

Devendra Singh Sachan, Lingfei Wu, Mrinmaya Sachan, William Hamilton, "Stronger Transformers for Neural Multi-Hop Question Generation". [PDF]

Yu Chen, Lingfei Wu** and Mohammed J Zaki (**Corresponding Author), "Toward Subgraph Guided Knowledge Graph Question Generation with Graph Neural Networks". [PDF]

Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio, Veronika Thost, Lingfei Wu, Kenneth Forbus and Achille Fokoue, "Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling". [PDF]

Lingfei Wu*, Ian En-Hsu Yen*, Fangli Xu, Pradeep Ravikumar and Michael Witbrock (*Equally Contributed), "D2KE: From Distance to Kernel and Embedding". [PDF] 

Kun Xu*, Lingfei Wu*, Zhiguo Wang, Yansong Feng, Michael Witbrock and Vadim Sheinin (*Equally Contributed), "Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks". [PDF] [Code]

[AI Magazine 2023] Xiaojie Guo, Shugen Wang, Hanqing Zhao, Shiliang Diao, Jiajia Chen, Zhuoye Ding, Zhen He, Jianchao Lv, Yun Xiao, Bo Long, Han Yu and Lingfei Wu** (Corresponding Author), "Intelligent Online Selling Point Extraction for E-Commerce Recommendation", In the Thirty-Sixth AAAI Conference on Artificial Intelligence. [PDF] [Media Coverage]: <Yahoo News> <MarketWatch> <AP News> <PR Newswire> <Seeking Alpha> <Finanzen.net> <Benzinga> <Markets Insider> <Morningstar> <Hawaii NewsNow> <69-WFMZ> <The Time Weekly> <JD Retail Tech Blog> <JD Retail Tech Blog>

[AI Magazine 2023] Yanyan Zou, Xueying Zhang, Hainan Zhang, Jing Zhou, Shiliang Diao, Jiajia Chen, Zhuoye Ding, Zhen He, Xueqi He, Yun Xiao, Bo Long, Han Yu and Lingfei Wu** (Corresponding Author), "Automatic Product Copywriting for E-Commerce", In the Thirty-Sixth AAAI Conference on Artificial Intelligence. [PDF] [Media Coverage]: <Yahoo News> <MarketWatch> <AP News> <PR Newswire> <Seeking Alpha> <Finanzen.net> <Benzinga> <Markets Insider> <Morningstar> <Hawaii NewsNow> <69-WFMZ> <The Time Weekly> <JD Retail Tech Blog> <JD Retail Tech Blog>

[COSE 2023] Xiang Ling, Lingfei Wu, Jiangyu Zhang, Zhenqing Qu, Wei Deng, Xiang Chen, Chunming Wu, Shouling Ji, Tianyue Luo, Jingzheng Wu, Yanjun Wu, "Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art", Computers & Security (2023). [PDF]

[MIR 2023] Jing Hu, Lingfei Wu, Yu Chen, Po Hu and Mohammed J. Zaki, "GraphFlow+: Exploiting Conversation Flow in Conversational Machine Comprehension with Graph Neural Networks", Machine Intelligence Research (2023). [PDF]

[FnT-MAL 2022] Lingfei Wu*, Yu Chen*, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, and Bo Long (*Equally Contributed). "Graph Neural Networks for Natural Language Processing: A Survey", Foundations and Trends® in Machine Learning (2023). [PDF]

[KBS 2022] Yutong Qu, Wei Emma Zhang , Jian Yang , Lingfei Wu and Jia Wu, "Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures", Knowledge-Based Systems (2022). [PDF]

[TCHI 2022] April Yi Wang, Dakuo Wang, Jaimie Drozdal, Michael J. Muller, Soya Park, Justin D. Weisz, Xuye Liu, Lingfei Wu, Casey Dugan, "Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks", ACM Transactions on Computer-Human Interaction (2022). [PDF]

[TNNLS 2022] Xiaojie Guo, Lingfei Wu and Liang Zhao, "Deep Graph Translation", IEEE Transactions on Neural Networks and Learning Systems. [PDF]

[TNNLS 2022] Xiang Ling*, Lingfei Wu*, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, and Shouling Ji (*Equally Contributed), "Hierarchical Graph Matching Networks for Deep Graph Similarity Learning", IEEE Transactions on Neural Networks and Learning Systems. [PDF] [Code]

[TKDE 2022] Yuyang Gao*, Tanmoy Chowdhury*, Lingfei Wu, and Liang Zhao (*Equally Contributed), "Modeling Health Stage Development of Patients with Dynamic Attributed Graphs in Online Health Communities", IEEE Transactions on Knowledge and Data Engineering (2021). [PDF] [Code]

[TKDD 2021] Hanlu Wu, Tengfei Ma, Lingfei Wu, Shouling Ji, "Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing", The ACM Transactions on Knowledge Discovery from Data (2021).  [PDF] [Code]

[TKDD 2021] Xiang Ling*, Lingfei Wu*, Saizhuo Wang, Gaoning Pan, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji (*Equally Contributed), "Deep Graph Matching and Searching for Semantic Code Retrieval", The ACM Transactions on Knowledge Discovery from Data (2021). [PDF] [Code]

[EML 2020] Zhao Qin*, Lingfei Wu*, Hui Sun*, Siyu Huo, Tengfei Ma, Eugene Lim, Pin-Yu Chen, Benedetto Marelli and Markus J. Buehler (*Equally Contributed), "Artificial intelligence method to design and fold alpha-helix structural proteins from the primary amino acid sequence", Extreme Mechanics Letters (2020). [PDF] [Media Coverage]: < MIT News > <PHYSORG>

[TKDE 2019] Qingzhe Li, Amir Alipour-Fanid, Martin Slawski,Yanfang Ye, Lingfei Wu, Kai Zeng and Liang Zhao, "Large-scale Cost-aware Classification Using Feature Computational Dependency Graph", IEEE Transactions on Knowledge and Data Engineering (2019). [PDF]

[SISC 2019] Lingfei Wu, Fei Xue and Andreas Stathopoulos, "TRPL+K: Thick-Restart Preconditioned Lanczos+K Method for Large Symmetric Eigenvalue Problems”, SIAM J. Sci. Comput. (2019), 41(2), pp. S1013–1040. [Flagship Journal in Scientific Computing] [PDF] 

[SISC 2017] Lingfei Wu, Eloy Romero and Andreas Stathopoulos, “PRIMME_SVDS: A High-Performance Preconditioned SVD Solver for Accurate Large-scale Computations”, SIAM J. Sci. Comput. (2017), 39(5), pp. S248–S271. [Flagship Journal in Scientific Computing] [PDF] [Media Coverage: <SIAM NEWS>]

[JCOMP 2016] Lingfei Wu, Andreas Stathopoulos, Jesse Laeuchli, Vassilis Kalantzis and Efstratios Gallopoulos, “Estimating the Trace of the Matrix Inverse by Interpolating from the Diagonal of An Approximate Inverse”, Journal of Computational Physics (2016), pp. 828-844. [PDF]

[TBD 2016] Lingfei Wu, Kesheng Wu, Alex Sim, Michael Churchill, Jong Y. Choi, Andreas Stathopoulos, Cs Chang and Scott Klasky, “Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma”, IEEE Transactions on Big Data (2016), pp. 262-275. [PDF]

[SISC 2015] Lingfei Wu and Andreas Stathopoulos, “A Preconditioned Hybrid SVD Method for Computing Accurately Singular Triplets of Large Matrices”, SIAM J. Sci. Comput. (2015), 37(5), pp. S365-S388. [Flagship Journal in Scientific Computing] [PDF] [Media Coverage: <SIAM NEWS>]

[CJSA 2009] Lingfei Wu, Max Q.-H. Meng and Huawei Liang, “A Collinearity-Based Localization Algorithm for Wireless Sensor Networks”, Chinese Journal of Sensors and Actuators, 2009, 22(5):722-727. [PDF]

[WWW 2023] Zhizhi Yu, Di Jin, Cuiying Huo, Zhiqiang Wang, Xiulong Liu, Heng Qi, Jia Wu and Lingfei Wu, "KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks", In the 32st conference in the International World Wide Web Conference series. [PDF]

[AAAI 2023] Cuiying Huo, Di Jin, Yawen Li, Dongxiao He, Yu-Bin Yang and Lingfei Wu, "T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation", In the Thirty-Seventh AAAI Conference on Artificial Intelligence. [PDF]

[AAAI 2023] Dongjie Wang, Lingfei Wu, Denghui Zhang, Jingbo Zhou, Leilei Sun and Yanjie Fu, "Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning", In the Thirty-Seventh AAAI Conference on Artificial Intelligence. [PDF]

[WSDM 2023] Zhendong Chu, Hongning Wang, Yun Xiao, Bo Long and Lingfei Wu, "Meta Policy Learning for Cold-Start Conversational Recommendation", In The 16th ACM International Conference on Web Search and Data Mining. [PDF]

[EMNLP 2022] Xiaoqiang Wang, Bang Liu, Siliang Tang and Lingfei Wu, "QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance", In the 2022 Conference on Empirical Methods in Natural Language Processing. [PDF] 

[EMNLP 2022] Peng Lin, Yanyan Zou, Lingfei Wu, Mian Ma, Zhuoye Ding and Bo Long, "Automatic Scene-based Topic Channel Construction System for E-Commerce", In the 2022 Conference on Empirical Methods in Natural Language Processing. [PDF]

[EMNLP 2022] Ziyang Liu, Chaokun Wang, Hao Feng, Lingfei Wu and Liqun Yang, "Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search", In the 2022 Conference on Empirical Methods in Natural Language Processing. [PDF]

[AACL-IJCNLP 2022] Hanning Gao, Lingfei Wu, Po Hu, Zhihua Wei, Fangli Xu and Bo Long, "Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph", In the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing. [PDF]

[KDD 2022] Xiaojie Guo, Qingkai Zeng, Meng Jiang, Yun Xiao, Bo Long and Lingfei Wu** (Corresponding Author), "Automatic Controllable Product Copywriting for E-Commerce", In the Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. [PDF] [Code]

[KDD 2022] Xiaochuan Fan, Chi Zhang, Yong Zhang, Yue Shang, Xueying Zhang, Zhen He, Yun Xiao, Bo Long and Lingfei Wu** (Corresponding Author), "Automatic Generation of Product-Image Sequence in E-commerce", In the Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. [PDF] [Code]

[ICML 2022] Dong Chen, Lingfei Wu, Siliang Tang, Yun Xiao, Bo Long and Yueting Zhang, "Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile", In the Thirty-Ninth International Conference on Machine Learning. [PDF] [Code]

[ICML 2022] Jiayin Jin, Zeru Zhang, Yang Zhou and Lingfei Wu, "Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing", In the Thirty-Ninth International Conference on Machine Learning. [PDF] [Code]

[ACL 2022] Xiaoqiang Wang, Bang Liu, Fangli Xu, Bo Long, Siliang Tang**, and Lingfei Wu** (Corresponding Author), "Feeding What You Need by Understanding What You Learned", In the 60th annual meeting of the Association for Computational Linguistics. [PDF] 

[WWW 2022] Yiming Zhang*, Lingfei Wu*, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Bo Long and Jian Pei (*Equally Contributed), "Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation", In the 31st conference in the International World Wide Web Conference series. [PDF]

[WWW 2022] Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo and Chuan Shi, "Compact Graph Structure Learning via Mutual Information Compression", In the 31st conference in the International World Wide Web Conference series. [PDF]

[INFOCOM 2022] Xiang Ling, Lingfei Wu, Wei Deng, Zhengqing Qu, Jiangyu Zhang, Sheng Zhang, Tengfei Ma, Bin Wang, Chunming Wu, and Shouling Ji , "MalGraph: Hierarchical Graph Neural Networks for Robust Windows Malware Detection", In the INFOCOM 2022: International Conference on Computer Communications. [PDF]

[AAAI/IAAI 2022] Xiaojie Guo, Shugen Wang, Hanqing Zhao, Shiliang Diao, Jiajia Chen, Zhuoye Ding, Zhen He, Jianchao Lv, Yun Xiao, Bo Long, Han Yu and Lingfei Wu** (Corresponding Author), "Intelligent Online Selling Point Extraction for E-Commerce Recommendation", In the Thirty-Sixth AAAI Conference on Artificial Intelligence. [PDF] [Media Coverage]: <Yahoo News> <MarketWatch> <AP News> <PR Newswire> <Seeking Alpha> <Finanzen.net> <Benzinga> <Markets Insider> <Morningstar> <Hawaii NewsNow> <69-WFMZ> <The Time Weekly> <JD Retail Tech Blog> <JD Retail Tech Blog>

[AAAI/IAAI 2022] Xueying Zhang, Yanyan Zou, Hainan Zhang, Jing Zhou, Shiliang Diao, Jiajia Chen, Zhuoye Ding, Zhen He, Xueqi He, Yun Xiao, Bo Long, Han Yu and Lingfei Wu** (Corresponding Author), "Automatic Product Copywriting for E-Commerce", In the Thirty-Sixth AAAI Conference on Artificial Intelligence. [PDF] [Media Coverage]: <Yahoo News> <MarketWatch> <AP News> <PR Newswire> <Seeking Alpha> <Finanzen.net> <Benzinga> <Markets Insider> <Morningstar> <Hawaii NewsNow> <69-WFMZ> <The Time Weekly> <JD Retail Tech Blog> <JD Retail Tech Blog>

[WSDM 2022] Yitong Pang*, Lingfei Wu*, Qi Shen, Yiming Zhang, Zhihua Wei, Fangli Xu,  Ethan Chang, and Bo Long (*Equally Contributed), "Heterogeneous Global Graph Neural Networks for Personalized  Session-based Recommendation", In The 15th ACM International Conference on Web Search and Data Mining. [PDF]

[NeurIPS 2021] Shen Kai*, Lingfei Wu*, Siliang Tang, Yueting Zhuang, Zhen He, Zhuoye Ding, Yun Xiao, and Bo Long (*Equally Contributed), "Learning to Generate Visual Questions with Noisy Supervision", In the Thirty-Fifth annual conference on Neural Information Processing Systems. [PDF] [Video]

[NeurIPS 2021] Zeru Zhang, Jiayin Jin, Zijie Zhang, Yang Zhou, Xin Zhao, Jiaxiang Ren, Ji Liu, Lingfei Wu, Ruoming Jin, Dejing Dou, "Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory", In the Thirty-Fifth annual conference on Neural Information Processing Systems. [PDF]

[EMNLP 2021] Zeru Zhang, Zijie Zhang, Yang Zhou, Lingfei Wu, Sixing Wu, Xiaoying Han, Dejing Dou, Tianshi Che and Da Yan, "Adversarial Attack against Cross-lingual Knowledge Graph Alignment", In the 2021 Conference on Empirical Methods in Natural Language Processing. [PDF]

[EMNLP 2021] Manling Li, Tengfei Ma, Mo Yu, Lingfei Wu, Tian Gao, Heng Ji and Kathleen McKeown, "Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport", In the 2021 Conference on Empirical Methods in Natural Language Processing. [PDF]

[EMNLP 2021] Xuye Liu, Dakuo Wang, April Yi Wang, Yufang Hou and Lingfei Wu, "HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks", In the 2021 Conference on Empirical Methods in Natural Language Processing. [PDF]

[EMNLP 2021] Yangkai Du, Tengfei Ma, Lingfei Wu, Fangli Xu, Xuhong Zhang, Bo Long and Shouling Ji, "Constructing Contrastive samples via Summarization for Text Classification with limited annotations", In the 2021 Conference on Empirical Methods in Natural Language Processing. [PDF] [Video]

[IJCAI 2021 Demo] Xuye Liu, Dakuo Wang, April Yi Wang, Lingfei Wu, "Graph-Augmented Code Summarization in Computational Notebooks", In the 30th International Joint Conference on Artificial Intelligence. [PDF]

[IJCAI 2021 TUT] Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li and Bang Liu, "Deep Learning on Graphs for Natural Language Processing", In the 30th International Joint Conference on Artificial Intelligence. [Website] 

[CIKM 2021] Dadong Miao, Yanan Wang, Guoyu Tang, Lin Liu, Sulong Xu, Bo Long, Yun Xiao, Lingfei Wu and Yunjiang Jiang, "Sequential Search with Off-Policy Reinforcement Learning", In the 30th ACM International Conference on Information and Knowledge Management. [PDF]

[ACM MM2021] Shiwei Wu, Joya Chen, Tong Xu*, Liyi Chen, Lingfei Wu, Yao Hu, Enhong Chen, "Linking the Characters: Video-oriented Social Graph Generation via Hierarchical-cumulative GCN", In Proceedings of the 29th ACM International Conference on Multimedia. [PDF]

[KDD 2021 OW] Lingfei Wu, Jian Pei, Jiliang Tang, and Yinglong Xia, "Deep Learning on Graphs: Methods and Applications (DLG-KDD’21)", In the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Organized Workshop. [Website] 

[KDD 2021 OW] Jianpeng Xu, Lingfei Wu, Linsey Pang, Mohit Sharma, Dawei Yin, George Karypis, Justin Basilico, and Philip S. Yu, "Industrial Recommendation Systems (IRS-KDD’21)", In the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Organized Workshop. [Website] 

[KDD 2021 TUT] Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li and Bang Liu, "Deep Learning on Graphs for Natural Language Processing", In the Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. [Website]

[KDD 2021] Yao Ma, Suhang Wang, Tyler Derr, Lingfei Wu, and Jiliang Tang, "Attacking Graph Convolutional Networks via Rewiring". In the Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.[PDF] [Code]

[ICML 2021 WS] Pengwei Xing, Songtao Lu, Lingfei Wu and Han Yu, "BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning", In the Thirty-eighth International Conference on Machine Learning Workshop on Federated Learning for User Privacy and Data Confidentiality. [PDF] [Code] 

[ICML 2021] Xin Zhao, Zeru Zhang, Zijie Zhang, Lingfei Wu, Jiayin Jin, Yang Zhou, Ruoming Jin, Dejing Dou, and Da Yan, "Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks", In the Thirty-eighth International Conference on Machine Learning. [PDF] [Code] 

[SIGIR 2021 TUT] Lingfei Wu, Yu Chen, Heng Ji, and Bang Liu, "Deep Learning on Graphs for Natural Language Processing", In the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. [Website]

[NAACL 2021] Wenhao Yu, Lingfei Wu, Yu Deng, Qingkai Zeng, Ruchi Mahindru, Sinem Guven, Meng Jiang, "Technical Question Answering across Tasks and Domains". [PDF] [Code] [Demo] 

[NAACL 2021 TUT] Lingfei Wu, Yu Chen, Heng Ji, and Yunyao Li, "Deep Learning on Graphs for Natural Language Processing", In the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics. [PDF][Website] [Media Coverage]: <AITechTalk> <Zhuanzhi><GraphRec> <NewBeeNLP>

[SANER 2021] Aakash Bansal, Zachary Eberhart, Lingfei Wu and Collin McMillan, "A Neural Question Answering System for Basic Questions about Subroutines", In the Twenty-Eighth IEEE International Conference on Software Analysis, Evolution and Reengineering. [PDF] [Code]

[SANER 2021] Sakib Haque, Aakash Bansal, Lingfei Wu and Collin McMillan, "Action Word Prediction for Neural Source Code Summarization", In the Twenty-Eighth IEEE International Conference on Software Analysis, Evolution and Reengineering. [PDF] [Code]

[AAAI 2021] Xiao Qin, Nasrullah Sheikh, Berthold Reinwald, and Lingfei Wu, "Relation-aware Graph Attention Model with Adaptive Self-adversarial Training", In the Thirty-Fifth AAAI Conference on Artificial Intelligence. [PDF] [Code]

[NeurIPS 2020] Yu Chen, Lingfei Wu** and Mohammed J Zaki (**Corresponding Author), "Iterative Deep Graph Learning for Graph Neural Networks: Better and  Robust Node Embeddings". In the Thirty-Fourth annual conference on Neural Information Processing Systems. [PDF] [Code] [Media Coverage]: <Jiangmen News> 

[EMNLP 2020] Shucheng Li*, Lingfei Wu*, Shiwei Feng, Fangli Xu, Fengyuan Xu and Sheng Zhong (*Equally Contributed), "Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem", In the 2020 Conference on Empirical Methods in Natural Language Processing.  [PDF] [Code]

[EMNLP 2020] Hanlu Wu, Tengfei Ma, Lingfei Wu, Tariro Manyumwa and Shouling Ji, "Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning", In the 2020 Conference on Empirical Methods in Natural Language Processing. [PDF] [Code] [Media Coverage]: <IBM Research News>

[EMNLP 2020 Demo] Wenhao Yu, Lingfei Wu, Yu Deng, Ruchi Mahindru, Qingkai Zeng, Sinem Guven and Meng Jiang, "A Technical Question Answering System with Transfer Learning", In the 2020 Conference on Empirical Methods in Natural Language Processing. [PDF] [Code][Demo]

[KDD 2020 WS]M. Clara De Paolis Kaluza, Lingfei Wu, Veronika Thost, Ibrahim Abdelaziz and Achille Fokoue, "Scalable Dynamic Graph Representation Learning via Incremental Graph Embedding", In the Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Workshop on Deep Learning on Graph: Methods and Applications. [PDF]

[KDD 2020 WS]Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio, Veronika Thost, Lingfei Wu, Kenneth Forbus and Achille Fokoue, "Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling", In the Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Workshop on Deep Learning on Graph: Methods and Applications. [PDF]

[KDD 2020] Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu and Yanfang Ye, "Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement", In the Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. [PDF] [Media Coverage]: <Zhuanzhi>

[KDD 2020 TUT] Yu Rong, Wenbing Huang, Tingyang Xu, Hong Cheng, Junzhou Huang, Yao Ma, Yiqi Wang, Tyler Derr, Lingfei Wu and Tengfei Ma, “Deep Graph Learning: Foundations, Advances and Applications”, In the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Tutorial. [Website]

[KDD 2020 OW] Lingfei Wu, Yinglong Xia and Jian Pei, "Deep Learning on Graphs: Methods and Applications (DLG-KDD’20)", In the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Organized Workshop. [Website] 

[IJCAI 2020 WS] Fangli Xu, Lingfei Wu, Shimeng Peng, Richcard Tong, "Predicting Students’ Performance in Online Adaptive Learning System Using Attention Extracted from EEG", In the Proceedings of the 29th International Joint Conference on Artificial Intelligence Workshop on AI-based Multimodal Analytics for Understanding Human Learning in Real-world Educational Contexts (AIMA4Edu). [PDF]

[IJCAI 2020] Hanning Gao*, Lingfei Wu*, Po Hu and Fangli Xu (*Equally Contributed), "RDF-to-Text Generation with Graph-augmented Structural Neural Encoders", In the Proceedings of the 29th International Joint Conference on Artificial Intelligence. [PDF] [CODE]

[IJCAI 2020] Kai Shen*, Lingfei Wu*, Fangli Xu, Siliang Tang, Jun Xiao and Yueting Zhuang (*Equally Contributed), "Hierarchical Attention Based Spatial-Temporal Graph-to-Sequence Learning for Grounded Video Description", In the Proceedings of the 29th International Joint Conference on Artificial Intelligence. [PDF] 

[IJCAI 2020] Yu Chen, Lingfei Wu** and Mohammed J Zaki (**Corresponding Author), "GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension", In the Proceedings of the 29th International Joint Conference on Artificial Intelligence. [PDF] [CODE]

[ACL 2020] Ying Lin, Heng Ji, Fei Huang and Lingfei Wu, "A Joint End-to-End Neural Model for Information Extraction with Global Features", In the 58th annual meeting of the Association for Computational Linguistics. [PDF] [CODE]

[ACL 2020] Rajarshi Haldar, Lingfei Wu, JinJun Xiong and Julia Hockenmaier, "A Multi-Perspective Architecture for Semantic Code Search", In the 58th annual meeting of the Association for Computational Linguistics. [PDF] [CODE]

[ACL 2020] Wenhao Yu, Lingfei Wu, Qingkai Zeng, Shu Tao, Yu Deng and Meng Jiang, "Crossing Variational Autoencoders for Answer Retrieval", In the 58th annual meeting of the Association for Computational Linguistics. [PDF]

[ACL 2020] Luyang Huang, Lingfei Wu and Lu Wang, "Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward", In the 58th annual meeting of the Association for Computational Linguistics. [PDF] [CODE]

[ICPC 2020] Alexander LeClair, Sakib Haque, Lingfei Wu and Collin McMillan, "Improved Code Summarization via a Graph Neural Network", In The 28th IEEE/ACM International Conference on Program Comprehension. [PDF] [CODE]

[MSR 2020] Sakib Haque, Alexander LeClair, Lingfei Wu and Collin McMillan, "Improved Automatic Summarization of Subroutines via Attention to File Context", In The 17th International Conference on Mining Software Repositories. [PDF] [CODE]

[ICLR 2020] Yu Chen, Lingfei Wu** and Mohammed J. Zaki (**Corresponding Author), "Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation", In the Eighth International Conference on Learning Representations. [PDF] [CODE]

[AAAI 2020 WS] Yu Chen, Lingfei Wu** and Mohammed J. Zaki (**Corresponding Author), "Deep Iterative and Adaptive Learning for Graph Neural Networks", In the Thirty-Fourth AAAI Conference on Artificial Intelligence Workshop on Deep Learning on Graphs: Methodologies and Applications. [PDF][Best Student Paper Award] 

[AAAI 2020 WS] Wenhao Yu, Lingfei Wu, Shu Tao, Yu Deng, Qingkai Zeng and Meng Jiang, "Generating Helpful Responses for Intelligent Tech Support", In the Thirty-Fourth AAAI Conference on Artificial Intelligence Workshop on Reasoning for Complex QA. 

[AAAI 2020 TUT] Yao Ma, Wei Jin, Jiliang Tang, Lingfei Wu and Tengfei Ma. “Graph Neural Networks: Models and Applications”, In the Thirty-Fourth AAAI Conference on Artificial Intelligence Tutorial. [Website][PDF] [Highlight: attract more than 300+ attendees] [Media Coverage]: <Zhuanzhi>

[AAAI 2020 OW] Lingfei Wu, Jian Tang, Yinglong Xia and Charu Aggarwal, "Deep Learning on Graphs: Methodologies and Applications ​(DLGMA’20)", In the Thirty-Fourth AAAI Conference on Artificial Intelligence Organized Workshop. [Website] [Highlight: attract more than 200+ attendees]

[EMNLP 2019 WS] Siyu Huo, Tengfei Ma, Chen Jie, Maria Chang, Lingfei Wu and Michael Witbrock, "Graph Enhanced Cross-Domain Text-to-SQL Generation", In 2018 Conference on Empirical Methods in Natural Language Processing Workshop on Graph-Based Methods for Natural Language Processing. [PDF]

[NeurIPS 2019 WS] Yu Chen, Lingfei Wu** and Mohammed J. Zaki (**Corresponding Author), "Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model", In the Thirty-third annual conference on Neural Information Processing Systems workshop on Graph Representation Learning. [PDF]

[NeurIPS 2019 WS] Kaylin Hagopian, Qing Wang, Tengfei Ma, Yupeng Gao and Lingfei Wu, "Learning Logical Representations from Natural Languages with Weak Supervision and Back Translation". In the Thirty-third annual conference on Neural Information Processing Systems workshop on Knowledge Representation & Reasoning Meets Machine Learning. [PDF]

[NeurIPS 2019] Zhen Zhang, Yijian Xiang, Lingfei Wu, Bing Xue and Arye Nehorai, "KerGM: Kernelized Graph Matching", In the Thirty-third annual conference on Neural Information Processing Systems. [spotlight talk, acceptance rate: 3% (200/6743)]. [PDF]

[BigData 2019 OW] Lingfei Wu, Jiliang Tang, Liang Zhao and Tyler Derr, "Deep Graph Learning: Methodologies and Applications (DGLMA’19)", In the 2019 IEEE International Conference on Big Data Organized Workshop. [Website]

[CIKM 2019] Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng and Liang Zhao, "Multi-stage Deep Classifier Cascades for Open World Recognition", In the The 28th ACM International Conference on Information and Knowledge Management. [PDF]

[ICDM 2019] Qingzhe Li, Liang Zhao, Yi-Ching Lee, Yanfang Ye, Jessica Lin and Lingfei Wu, "Contrast Feature Dependency Pattern Mining for Controlled Experiments with Application to Driving Behavior", In the 19th IEEE International Conference on Data Mining. [PDF]

[ICDM 2019] Yuyang Gao, Lingfei Wu, Houman Homayoun and Liang Zhao, "DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums", In the 19th IEEE International Conference on Data Mining. [PDF]

[KDD 2019 OW] Lingfei Wu, Yinglong Xia, Jian Pei and Hongxia Yang, "Deep Learning on Graphs: Methods and Applications (DLG’19)", In the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Organized Workshop. [Website] [Highlight: attract more than 500+ attendees]

[KDD 2019 WS] Hanning Gao, Lingfei Wu, Po Hu and Fangli Xu, "Exploiting Graph Neural Networks with Context Information for RDF-to-Text Generation", In the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Workshop on Deep Learning on Graphs: Methods and Applications. [PDF][Best Paper Award] [Media Coverage]: <Yahoo News> <Leiphone> <QbitAI>

[KDD 2019 WS] Shucheng Li, Lingfei Wu, Shiwei Feng, Fangli Xu, Fengyuan Xu and Sheng Zhong, "An Empirical Study of Graph Neural Networks Based Semantic Parsing", In the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Workshop on Deep Learning on Graphs: Methods and Applications. [PDF] [Best Student Paper Award] [Media Coverage]: <Yahoo News> <Leiphone> <QbitAI>

[KDD 2019 WS] Xiaojie Guo, Lingfei Wu and Liang Zhao, "Deep Graph Translation", In the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Workshop on Deep Learning on Graphs: Methods and Applications. [PDF]

[KDD 2019] Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji and Charu Aggarwal, "Efficient Global String Kernel with Random Features: Beyond Counting Substructures", In the Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. [oral paper, acceptance rate: 9.2% (110/1200)]. [PDF] [CODE]

[KDD 2019] Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia and Charu Aggarwal, "Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding", In the Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. [oral paper, acceptance rate: 9.2% (110/1200)]. [PDF] [CODE]

[AIAED 2019] Shucheng Li, Lingfei Wu, Shiwei Feng, Yulong Tian, Fengyuan Xu, Fangli Xu and Sheng Zhong, "Toward Automated Queries Generation from Natural Language Description Using Graph Neural Networks", In the Proceedings of the 3rd International Conference on AI + Adaptive Education. [Best Student Paper Award] 

[IJCAI 2019 WS] Fangli Xu, Lingfei Wu, Wei Wang, KP Thai and Richcard Tong, "MUTLA: A Large-Scale Dataset for Multimodal Teaching and Learning Analytics", In the Proceedings of the 28th International Joint Conference on Artificial Intelligence Workshop on AI-based Multimodal Analytics for Educational Contexts. [PDF][PPT]

[IJCAI 2019] Qi Lei, Jinfeng Yi, Roman Vaculin, Lingfei Wu and Inderjit S. Dhillon, "Similarity Preserving Representation Learning for Time Series Clustering", In the Proceedings of the 28th International Joint Conference on Artificial Intelligence. [PDF]

[ICML 2019 WS] Yu Chen, Lingfei Wu** and Mohammed J. Zaki (**Corresponding Author), "GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension", In the International Conference on Machine Learning Workshop on Learning and Reasoning with Graph-Structured Data. [PDF]

[ICML 2019] Pin-Yu Chen, Lingfei Wu, Sijia Liu and Indika Rajapakse, "Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications", In The 36th International Conference on Machine Learning. [oral (long) paper, acceptance rate: 22.5%]. [PDF] 

[ICLR 2019 WS] Lingfei Wu, Zhen Zhang, Arye Nehorai, Liang Zhao and Fangli Xu, "SAGE: Scalable Attributed Graph Embeddings for Graph Classification", In the International Conference on Learning Representations Workshop on Representation Learning on Graphs and Manifolds. [PDF]

[NAACL 2019] Hongyu Gong, Suma Bhat, Lingfei Wu, JinJun Xiong and Wen-mei Hwu, "Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus", In the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics. [oral paper, acceptance rate: 9.5% (81/198)] [PDF] [Code]

[NAACL 2019] Yu Chen, Lingfei Wu and Mohammed Zaki, "Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases", In the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics. [oral paper, acceptance rate: 9.5% (81/198)] [PDF] [Code]

[ICC 2019] Ziyao Zhang, Liang Ma, Konstantinos Poularakis, Kin K. Leung and Lingfei Wu, "DQ Scheduler: Deep Reinforcement Learning Based Controller Synchronization in Distributed SDN", In the IEEE International Conference on Communications 2019. [PDF][Best Paper Award (1/2590)][Media Coverage: <IBM Research AI Blog>]

[SYSML 2019] Qi Lei*, Lingfei Wu*, Pin-Yu Chen, Alexandros G Dimakis, Inderjit S Dhillon and Michael Witbrock (*equally contributed), "Discrete Attacks and Submodular Optimization with Applications to Text Classification". [oral paper, acceptance rate: 16.9% (32/189)]. [PDF] [Code] [Media Coverage]: <NatureNews> <Venturebeat> <TechTalks> <SyncedReview>

[AAAI 2019] Yuyang Gao, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong and Chaowei yang, "Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting", in The Thirty-Third AAAI Conference on Artificial Intelligence. [PDF]

[BigData 2018 TUT] Lingfei Wu and Ian Yen, "Unsupervised Feature Representation Learning via Random Features for Structured Data: Theory, Algorithm, and Applications", 2018 IEEE International Conference on Big Data Tutorial. [PPT]

[NIPS 2018 WS] Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Liang Zhao, Yinglong Xia and Michael Witbrock, "From Node Embedding to Graph Embedding: Scalable Global Graph Kernel via Random Features", Neural Information Processing Systems Workshop on Relational Representation Learning. [oral paper, acceptance rate: 5% (4/80)]. [PDF]

[D4GX 2018] Wenyu Zhang, Raya Horesh, Karthikeyan N. Ramamurthy, Lingfei Wu, Jinfeng Yi, Kryn Anderson and Kush R. Varshney, "Financial Forecasting and Analysis for Low-Wage Workers". [PDF]

[EMNLP 2018] Lingfei Wu, Ian E.H. Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar and Michael J. Witbrock, "Word Mover's Embedding: From Word2Vec to Document Embedding", In 2018 Conference on Empirical Methods in Natural Language Processing. [PDF] [CODE] [Python CODE] [Media Coverage]: <IBM Research News>

[EMNLP 2018] Kun Xu, Lingfei Wu, Zhiguo Wang, Mo Yu, Liwei Chen, Vadim Sheinin, "Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model", In 2018 Conference on Empirical Methods in Natural Language Processing. [oral paper, acceptance rate: 9.5% (81/198)].  [PDF] [CODE] [Media Coverage]: <IBM Research News>

[EMNLP 2018] Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng and Vadim Sheinin, "SQL-to-Text Generation with Graph-to-Sequence Model", In 2018 Conference on Empirical Methods in Natural Language Processing. [oral paper, acceptance rate: 9.5% (81/198)]. [PDF] [CODE] [Media Coverage]: <IBM Research News>

[KDD 2018] Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia and Charu Aggarwal, "Scalable Spectral Clustering Using Random Binning Features", In the Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. [oral paper, acceptance rate: 10.8% (107/983)]. [PDF] [CODE] [PPT] [Media Coverage]: <IBM Research News>

[ECCV 2018] Zhiqiang Tang, Xi Peng, Shijie Geng, Shaoting Zhang, Lingfei Wu and Dimitris Metaxas, "Quantized Densely Connected U-Nets for Efficient Landmark Localization", In the Proceedings of the 15th European Conference on Computer Vision. [PDF]

[AISTATS 2018] Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei and Michael Witbrock, "Random Warping Series: A Random Features Method for Time-Series Embedding", In the 21st International Conference on Artificial Intelligence and Statistics. [oral paper, acceptance rate: 5.9% (38/645)].  [PDF] [CODE] [PPT] 

[IAAI 2018] Jonathan Galsurkar, Moninder Singh, Lingfei Wu, Aditya Vempaty, Mikhail Sushkov, Devika Iyer, Serge Kapto and Kush Varshney, "Assessing National Development Plans for Alignment with Sustainable Development Goals via Semantic Search", In the Proceeding of the 13th Conference on Innovative Applications of Artificial Intelligence. [PDF]

[ICDM 2017] Pin-Yu Chen and Lingfei Wu, "Revisiting Spectral Graph Clustering with Generative Community Models", In the Proceeding of the 17th IEEE International Conference on Data Mining. [oral paper, acceptance rate: 9% (72/778)]. [PDF]

[KDD 2016] Lingfei Wu*, Ian E.H. Yen*, Jie Chen and Rui Yan (*equally contributed), “Revisiting Random Binning Feature: Fast Convergence and Strong Parallelizability”, In the Proceeding of the 22th SIGKDD conference on Knowledge Discovery and Data Mining. [oral paper, acceptance rate: 9% (70/784)]. [PDF] [CODE] [PPT]

[ICASSP 2016] Jie Chen*, Lingfei Wu*, Kartik Audhkhasi, Brian Kingsbury and Bhuvana Ramabhadran (*equally contributed), “Efficient One-VS-One Kernel Ridge Regression for Speech Recognition”, The 41st IEEE International Conference on Acoustics, Speech and Signal Processing. [PDF]

[SC15] Lingfei Wu and Andreas Stathopoulos, “High-Performance Algorithms for Large-Scale Singular Value Problems and Big Data Applications”, In The International Conference for High Performance Computing, Networking, Storage, and Analysis, 2015. [PDF]

[SC15] Lingfei Wu, Andreas Stathopoulos and Eloy Romero, “A High-Performance Preconditioned SVD Solver for Accurately Computing Large-Scale Singular Value Problems in PRIMME”, In The International Conference for High Performance Computing, Networking, Storage, and Analysis, 2015. [PDF]

[BDAC-14] Lingfei Wu, Kesheng Wu, Alex Sim, Michael Churchill, Jong Y. Choi, Andreas Stathopoulos, Cs Chang and Scott Klasky, “High-Performance Outlier Detection Algorithm for Finding Blob-Filaments in Plasma”, In Proceedings of 5rd International Workshop on Big Data Analytics: Challenges and Opportunities (BDAC-14), held in conjunction with SC14. [PDF]

[SC14] Lingfei Wu, Kesheng Wu, Alex Sim and Andreas Stathopoulos, “Real-Time Outlier Detection Algorithm for Finding Blob-Filaments in Plasma”, In The International Conference for High Performance Computing, Networking, Storage, and Analysis, 2014. [PDF]

[IEEE ROBIO 2009] Lingfei Wu, Max Q.-H. Meng, Zijing Lin, Wu He, Chao Peng and Huawei Liang, “A Practical Evaluation of Radio Signal Strength for Mobile Robot Localization”, In Proceedings of the 2 IEEE International Conference on Robotics and Biomimetics, 2009. [PDF]

[IEEE ICICTA 2009] Lingfei Wu, Max Q.-H. Meng, Zhenzhong Dong and Huawei Liang, “An Empirical Study of DV-Hop Localization Algorithm in Random Sensor Networks”, In Proceedings of the IEEE International Conference on Intelligent Computation Technology and Automation, 2009. [PDF]

[IEEE ICMA 2009] Lingfei Wu, Max Q.-H. Meng and Huawei Liang, “A Beacon Selected Localization Algorithm for Ad-Hoc Networks of Sensors”, In Proceedings of the IEEE International Conference on Mechatronics and Automation, 2009. [Best Student Paper Nomination (4/926)] [PDF]

[IEEE ICAL 2009] Lingfei Wu, Max Q.-H. Meng, Huawei Liang and Wen Gao, “Accurate Localization in Combination with Wireless Sensor Networks and Laser Localization”, In Proceedings of the IEEE International Conference on Automation and Logistics, 2009. [PDF]

[IEEE ICIA 2009] Lingfei Wu, Max Q.-H. Meng, Jian Huang, Huawei Liang and Zijing Lin, “An Improvement of DV-Hop Algorithm Based on Collinearity”, In Proceedings of the IEEE International Conference on Information and Automation, 2009. [PDF]