I am currently an assistant professor at HEC Montreal and Montreal Institute for Learning Algorithms (MILA). 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.
New!! I’m looking for students (Postdoc, Ph.D., MSc., and interns)!! Students who are interested in working with me please apply through Mila admission (https://mila.quebec/en/admission/) or send an email to me directly.
- 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!! 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!! Zhiqing Sun, Jian Tang, Pan Du, Zhi-Hong Deng and Jian-Yun Nie. “DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases.” To appear at the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 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”. To appear at 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.” To appear at 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.” To appear at 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, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, Jian Tang. “AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks.” arXiv:1810.11921 codes
- 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
- Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. DeepInf: Social Influence Prediction with Deep Learning.. In Proceedings of the Twenty-Fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’18)
- Quanyu Dai, Qiang Li, Jian Tang, and Dan Wang. Adversarial Network Embedding, in Proc. of 2018 AAAI Conf. on Artificial Intelligence (AAAI’18), New Orleans, LA, Feb. 2018
- Meng Qu, Jian Tang, and Jiawei Han, Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning, in Proc. of 2018 ACM Int. Conf. on Web Search and Data Mining (WSDM’18), Los Angeles, CA, Feb. 2018
- 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)