I am currently an associate 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.

Hiring in 2024 Fall!! Our group has multiple PhD positions in Fall 2024. In particular, we are looking for students to work on the following two directions:

• Geometric Deep Learning, Generative Models (e.g., diffusion models, flow matching) for drug discovery, in particular protein design;

• Large Language Models (LLMs) for Multiomics (genomoics, single-cell RNA-seq, proteomics, etc.).

We are collaborating with many leading labs in biology across the world and also have access to thousands of GPUs through our industry collaborators. Join us to make imact in tacking real-wold challenging biological problems with generative AI.

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.

What’s New

• New!! Three papers are accepted to ICLR’2022. Congrats to Shengchao Liu, Meng Qu, Minkai Xu, Huiyu Cai, Chence Shi.
• New!! Received new grant from IBM/Mila collaboration for “Pretraining molecular and protein representation with 3D structures” proposal (Oct. 1st 2021).
• New!! Five papers are accepted to NeurIPS’2021. Congrats to Chence Shi, Zhaocheng Zhu, Louis-Pascal Xhonneux, Minghao Xu, Andreea Deac.
• New!! Our paper “Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data”, collaborated with Prof. Yue Li at McGill Univesity, has been accepted to Nature Communications. Congrats to the leading author Yifan Zhao and Huiyu Cai.
• New!! We just released our machine learning for drug discovery platform, TorchDrug. For more information about TorchDrug, please visit here.
• New!! I gave an invited talk on “Geometric Deep Learning for Drug Disscovery” at ByteDance. Slides are available here.
• New!! I will serve as an area chair at AKBC’21 (Automated Knowledge Base Construction).
• New!! I gave a guest lecture on “Logic Reasoning for Knowledge Graphs” at the advanced machine learning class offered by Jie Tang at Tsinghua University.
• 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 Discovery” at the Yale University.
• New!! I gave a talk on “Graph Representation Learning for Drug Discovery” 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)

Research Interests

• Graph Representation Learning, Graph Neural Networks
• Reasoning in Knowledge Graphs and Natural Language
• Deep Generative Models
• Drug Discovery
• Material Discovery

Recent Papers

• New!! Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang. [Structured Multi-Task Learning for Molecular Property Predictions], to appear at AISTATS’22.
• New!! Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang. Pre-training Molecular Graph Representation with 3D Geometry, to appear at ICLR’22.
• New!! Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal A. C. Xhonneux, Jian Tang. Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction, to appear at NeurIPS’21.
• New!! Shitong Luo, Chence Shi, Minkai Xu, Jian Tang. Predicting Molecular Conformation via Dynamic Graph Score Matching, to appear at NeurIPS’21.
• New!! Minghao Xu, Meng Qu, Bingbing Ni, Jian Tang. Joint Modeling of Visual Objects and Relations for Scene Graph Generation, to appear at NeurIPS’21.
• New!! Andreea Deac, Petar Veličković, Ognjen Milinković, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic. Neural Algorithmic Reasoners are Implicit Planners, to appear at NeurIPS’21.
• New!! Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Veličković, Jian Tang.How to transfer algorithmic reasoning knowledge to learn new algorithms?, to appear at NeurIPS’21.
• 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

Selected Publications

• 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, 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)