Jian Tang is a Canada CIFAR AI Chair at Mila and an assistant professor at HEC Montréal and an adjunct professor at Université de Montréal. He worked as a research associate at Microsoft Research Asia between 2014-2016.
Tang’s research interests include deep learning, graph representation learning, graph neural networks, deep generative models, reinforcement learning, knowledge graphs, drug discovery and recommender systems.
- Best paper nomination, World Wide Web Conference, 2016
- Most-Cited Paper, World Wide Web Conference, 2015
- Best Paper Award, ICML, 2014
Sun, Z., Deng, Z. H., Nie, J. Y., & Tang, J. (2019). Rotate: Knowledge graph embedding by relational rotation in complex space.
Tang, J., Liu, J., Zhang, M., & Mei, Q. (2016, April). Visualizing large-scale and high-dimensional data. In Proceedings of the 25th international conference on world wide web (pp. 287-297).
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015). Line: Large-scale information network embedding. In Proceedings of the 24th international conference on world wide web (pp. 1067-1077).
Tang, J., Qu, M., & Mei, Q. (2015). Pte: Predictive text embedding through large-scale heterogeneous text networks. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1165-1174).
Tang, J., Meng, Z., Nguyen, X., Mei, Q., & Zhang, M. (2014). Understanding the limiting factors of topic modeling via posterior contraction analysis. In International Conference on Machine Learning (pp. 190-198). PMLR.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.