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Jian Tang

Appointment

  • Canada CIFAR AI Chair
  • Pan-Canadian AI Strategy

Connect

HEC Montreal

Google Scholar

About

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.

Awards

  • Best paper nomination, World Wide Web Conference, 2016
  • Most-Cited Paper, World Wide Web Conference, 2015
  • Best Paper Award, ICML, 2014

Relevant Publications

  • 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.

Institution

  • HEC Montréal
  • Mila
  • Université de Montréal

Department

Decision Sciences, Information and Research Operations

Education

  • PhD (Computer Science), Peking University
  • Visiting Student, PhD, University of Michigan

Country

  • Canada

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