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 (students working with me will be affiliated with UdeM) or send me an email directly.
- New!! Four papers related to Drug Discovery are accepted to ICML’21. Congrats to Chence Shi, Minkai Xu, Shitong Luo, Minghao and other interns and collaborators.
- New!! I joined a panel discussion on ‘High Impact in Practice’ at ICLR’s Geometric and Topological Representation Learning workshop.
- New!! I gave a Guest Lecture on “Graph Representation Learning for Drug Discover” at the Yale University.
- New!! I gave a talk on “Graph Representation Learning for Drug Discover” at the first workshop on “AI + Medicine” at Institute for AI Industry Research, Tsinghua University. The slides are available here
- New!! I will serve as an area chair in NeurIPS’2021!
- New!! I gave an invited talk “Deep generative models for molecular conformation generation” at Samsung Seminar.
- New!! I gave an invited Talk “Graph Representation Learning for Drug Discovery” at the research seminar of School of Biomedical Informatics, University of Texas School of Biomedical Informatics. slides are available.
- New!! I gave a keynote talk on “Learning Symbolic Logic Rules for Reasoning on Knowledge Graphs” at the International Workshop on Deep Learning on Graphs in AAAI’2021, February, 2021.Slides and video are available.
- New!! 3 papers are accepted to ICLR’2021!!
- New!! I introduced our recent work on “Deep Genereative Models for Molecular Conformation Genereation” at the MIT AI Cures Drug Discovery Conference, November, 2020. Slides and video are available.
- New!! I gave a tutorial on “Neural and Symbolic Logical Reasoning on Knowledge Graphs” at the Summer School of Chinese Information Processing Society of China (CIPS), November, 2020. Slides are available here.
- New!! I will serve as an area chair in ICML’2021!
- 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)
- Graph Representation Learning, Graph Neural Networks
- Reasoning in Knowledge Graphs and Natural Language
- Deep Generative Models
- Drug Discovery
- Material Discovery
- New!! Chence Shi, Shitong Luo, Minkai Xu, Jian Tang. “Learning Gradient Fields for Molecular Conformation Generation”, to appear at ICML’21.
- New!! Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, Jian Tang. “An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming”, to appear at ICML’21.
- New!! Hangrui Bi, Hengyi Wang, Chence Shi, Connor Coley, Jian Tang, Hongyu Guo. “Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction”, to appear at ICML’21.
- New!!Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, Jian Tang. “Self-supervised Graph-level Representation Learning with Local and Global Structure ”, to appear at ICML’21.
- New!! Meng Qu, Junkun Chen, Louis-Pascal AC Xhonneux, Yoshua Bengio, Jian Tang. “RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs”, ICLR’2021
- New!! Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang. “Learning Neural Generative Dynamics for Molecular Conformation Generation”, ICLR’2021
- New!! Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Benjamin Muller, Meng Qu, victor schmidt, Pierre-luc St-charles, hannah alsdurf, Olexa Bilaniuk, david buckeridge, gaetan caron, pierre luc carrier, Joumana Ghosn, satya ortiz gagne, Christopher Pal, Irina Rish, Bernhard Schölkopf, abhinav sharma, Jian Tang, andrew williams. “Predicting Infectiousness for Proactive Contact Tracing”. ICLR’2021
- 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”, TACL 2020.
- New!! Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan, Marc-Alexandre Côté, Zhiyuan Liu, Jian Tang. “Graph Policy Network for Transferable Active Learning on Graphs”. NeurIPS’2020
- New!! Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikuláš Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, William L Hamilton. “Learning dynamic knowledge graphs to generalize on text-based games”, NeurIPS’2020
- New!! Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun, Chenyan Xiong, Jian Tang. “Towards Interpretable Natural Language Understanding with Explanations as Latent Variables”, NeurIPS’2020
- Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang. “A Graph to Graphs Framework for Retrosynthesis Prediction”, ICML’20.
- Meng Qu, Tianyu Gao, Louis-Pascal AC Xhonneux, Jian Tang. “Few-shot Relation Extraction via Bayesian Meta-learning on Task Graphs”, ICML’20.
- Louis-Pascal AC Xhonneux, Meng Qu, Jian Tang. “Continuous Graph Neural Networks”, ICML’20.
- 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.
- 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
- 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
- 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)