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. I received my Ph.D. degree from EECS, Peking University in July 2014.
- 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!!
- I’m looking for Ph.Ds, masters,and interns to work with me in the fields of deep learning and reinforcement learning with various applications. If you’re interested, please send me an email or apply through the MILA recruitment page
- Deep learning, reinforcement learning, adversarial learning
- Graph representation learning, Graph Neural Networks
- Natural language understanding and reasoning
- Drug discovery
- Recommender systems
- 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.
- 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
- New!! Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang and Jian Tang. “Session-based Social Recommendation via Dynamic Graph Attention Networks.” To appear at the 12th ACM International Conference on Web Search and Data Mining (WSDM’19), Melbourne, Australia, February 11-15, 2019.
- 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)
- Minjeong Kim, Minsuk Choi, Sunwoong Lee, Jian Tang, Haesun Park, and Jaegul Choo. PixelSNE: Pixel-Aligned Stochastic Neighbor Embedding for Efficient 2D Visualization with Screen-Resolution Precision. In 20th EG / VGTC Conference on Visualization (EuroVis’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
- Luchen Liu, Jianhao Shen, Ming Zhang, Zichang Wang, and Jian Tang. Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Event Prediction, 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
- Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han. An Attention-based Collaboration Framework for Multi-View Network Representation Learning, in Proc. of 2017 ACM Int. Conf. on Information and Knowledge Management (CIKM’17), Singapore, Nov. 2017
- Jian Tang, Yue Wang, Kai Zheng and Qiaozhu Mei. End-to-end learning for short text expansion. KDD’17
- 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)