I am currently an assistant professor at Mila-Quebec AI Institute and HEC Montreal. Prior to that, I was a Postdoc at University of Michigan and Carnegie Mellon University. I also worked at Microsoft Research Asia as an associate researcher between 2014-2016. For more information, please check my CV.
I am looking for very strong students and have multiple positions in my group (including Postdocs, PhDs, MSc, and interns). Students who are interested in working with me please apply through Mila admission or send me an email directly. In particular, I am looking for students with the following background:
- Graph representation learning, Graph Neural Networks, Knowledge graphs
- Deep generative models
- Bioinformatics, medicine
- New!! 3 papers are accepted to NeurIPS’2020!!
- New!! Received a Tencent AI Lab Rhino-Bird Gift Fund. Thanks Tencent!
- New!! 4 papers accepted to ICML’20! Congratulations to all my students and collaborators!
- New!! Received a Amazon Faculty Research Award. Thanks Amazon!
- New!! Received a Microsoft-Mila collaboration grant on “Towards Combining Statistical Relational Learning and Graph Neural Networks for Reasoning”. Thanks Microsoft!
- New!! Received a Collaborative Research and Development Grant on “Intelligent Design through Graph Generation with Deep Generative Models and Reinforcement Learning” from National Research Council Canada (NRC). Thanks NRC!
- New!! I am teaching a new course Deep Learning and Applications this semester!
- New!! Two papers on graph representation learning for drug discovery are accepted to ICLR’2020!!
- New!! We released the codes of the pLogicNet model in our NeurIPS’19 paper “Probabilistic Logic Neural Networks for Reasoning”
- New!! We just released our GraphVite system, which is super effecient and only takes one minute to learn the node embeddings of a graph with one million nodes. It now supports three different tasks including node embeddings, knowledge graph embeddings, and graph&high-dimensional data visualization. For more information, check this link
- New!! We just released a library of recommender systems with deep neural networks including session-based recommendation, feature-based recommendation, and social recommenddation. For more information, check this link
- We just released the source codes of our RotatE model. The codes are available at link.
- I am quite honoured to be named to the first cohort of Canada CIFAR Artificial Intelligence Chairs (CCAI Chair).CIFAR News1 CIFAR News2
- Tutorial “Graph representation learning” by William L. Hamilton and me has been accepted by AAAI’19. See you at Hawaii!! Slides (Part 0, Part I, Part II, Part III)
- Deep learning, deep generative models, reinforcement learning
- Graph representation learning, Graph Neural Networks
- Natural language understanding and reasoning, Knowledge graphs
- Drug discovery
- Recommender systems
- New!! Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang. “A Graph to Graphs Framework for Retrosynthesis Prediction”, ICML’20.
- New!! Meng Qu, Tianyu Gao, Louis-Pascal AC Xhonneux, Jian Tang. “Few-shot Relation Extraction via Bayesian Meta-learning on Task Graphs”, ICML’20.
- New!! Louis-Pascal AC Xhonneux, Meng Qu, Jian Tang. “Continuous Graph Neural Networks”, ICML’20.
- New!! Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Haoran Wei, Yashaswi Pathak, Shengchao Liu, Simon Blackburn, Karam Thomas, Connor Coley, Jian Tang, Sarath Chandar, Yoshua Bengio. “Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning”, ICML’20.
- New!! Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang. “GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation”. To appear at the International Conference on Learning Representations 2020 (ICLR’20), Addis Ababa, Ethiopia, Apr.26-Apr. 30, 2020
- New!! Fan-Yun Sun, Jordan Hoffmann, Jian Tang.”InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization .” To appear at the International Conference on Learning Representations 2020 (ICLR’20, Spotlight), Addis Ababa, Ethiopia, Apr.26-Apr. 30, 2020
- New!! Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhiyuan Liu, Juanzi Li, Jian Tang. “KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation”, arXiv:1911.06136.
- New!! Vikas Verma, Meng Qu, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang. “GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning ”, arXiv:1909.11715.
- New!! Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada, Jian Tang, Jean Michel Sellier, Yoshua Bengio. “Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures ”, arXiv:1909.00949.
- New!! Meng Qu, Jian Tang. “Probabilistic Logic Neural Networks for Reasoning ”, to appear at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS‘19), Vancouver, Canada, Dec.8-15, 2019. codes
- New!! Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang. “vGraph: A Generative Model for Joint Community Detection and Node Representation Learning”, to appear at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS’19), Vancouver, Canada, Dec.8-15, 2019.
- New!! Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, Jian Tang. “AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks.” In the 28th ACM International Conference on Information and Knowledge Management (CIKM’19), November 3rd-7th, Beijing, China. codes
- New!! Meng Qu, Yoshua Bengio, and Jian Tang. “GMNN: Graph Markov Neural Networks”. To appear at the 36th International Conference on Machine Learning (ICML’19), Long Beach, California, United States. codes
- New!! Yanru Qu, Ting Bai, Weinan Zhang, Jianyun Nie, Jian Tang. “An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation”. In the first Workshop on Deep Learning Practice for High-dimensional Space data, KDD 2019, Alaska, US. (Best paper)
- New!! Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang. “Drug-Drug Adverse Effect Prediction with Graph Co-Attention”, arXiv:1905.00534, Workshop on Computational Biology at the 36th International Conference on Machine Learning (ICML 2019).
- New!! Cheng Yang, Jian Tang, Maosong Sun, Ganqu Cui, and Zhiyuan Liu.”Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks”. To appear in the International Joint Conference on Artificial Intelligence (IJCAI’19), August 10-16 2019, Macao, China.
- New!! Zhiqing Sun, Jian Tang, Pan Du, Zhi-Hong Deng and Jian-Yun Nie. “DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases.” In the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19), July 21-25, 2019, Paris, France.
- New!! Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang. “GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding”. In the Web Conference 2019 (formerly known as WWW’2019), San Francisco, CA, USA, May 13-17, 2019,
- New!! Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang. “RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space.” In the Seventh International Conference on Learning Representations (ICLR’19), New Orleans, USA. codes slides
- New!! William L. Hamilton and Jian Tang. “Graph Representation Learning”. Tutorial at the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI’19), Hawaii, USA, 2019. (Part 0, Part I, Part II, Part III)
- New!! Pengfei Liu, Shuaichen Chang, Xuanjing Huang, Jian Tang, Jackie Chi Kit Cheung. “Contextualized Non-local Neural Networks for Sequence Learning.” In the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI’19), Hawaii, USA, 2019.
- New!! Mingjie Sun, Jian Tang, Huichen Li, Bo Li, Chaowei Xiao, Yao Chen, Dawn Song. “Data Poisoning Attack against Unsupervised Node Embedding Methods.” arXiv:1810.12881
- New!! Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang and Jian Tang. “Session-based Social Recommendation via Dynamic Graph Attention Networks.” In the 12th ACM International Conference on Web Search and Data Mining (WSDM’19), Melbourne, Australia, February 11-15, 2019. codes
- Jian Tang, Jingzhou Liu, Ming Zhang and Qiaozhu Mei. Visualizing Large-scale and High-dimensional Data. WWW’16. [code][slides] (Best paper nomination 5/727)
- Jian Tang, Meng Qu, and Qiaozhu Mei. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks. KDD’15. [code]
- Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan and Qiaozhu Mei. LINE: Large-scale Information Network Embedding. WWW’15. [code] (Most cited paper in WWW’15)
- Jian Tang, Zhaoshi Meng, XuanLong Nguyen, Qiaozhu Mei and Ming Zhang. Understanding the limiting factors of topic modeling via posterior contraction analysis. In proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, June 2014. (Best paper award, 1/1500)