Tianyi Lin

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Welcome to my homepage!

I am an assistant professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia University. My research interests lie in game theory, machine learning, optimization and social network.

I obtained my Ph.D. in Electrical Engineering and Computer Science at UC Berkeley, where I was advised by Professor Michael I. Jordan and was associated with the Berkeley Artificial Intelligence Research (BAIR) group. From 2023 to 2024, I was a postdoctoral researcher at the Laboratory for Information & Decision Systems (LIDS) at Massachusetts Institute of Technology, working with Professor Asuman Ozdaglar. Prior to that, I received a B.S. in Mathematics from Nanjing University, a M.S. in Pure Mathematics and Statistics from University of Cambridge and a M.S. in Operations Research from UC Berkeley.

Email: tl3335 [at] columbia [dot] edu

Office: 535B Mudd Building, 500 West 120th Street, New York, NY 10027


news

Jul 01, 2024 I am happy to announce that I am starting a new position as an Assistant Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia University!
Mar 28, 2024 The paper, Doubly optimal no-regret online learning in strongly monotone games with bandit feedback, coauthored with Wenjia Ba, Jiawei Zhang and Zhengyuan Zhou was accepted to Operations Research.
Feb 14, 2024 The paper, Perseus: A simple and optimal high-order method for variational inequalities, coauthored with Michael. I. Jordan was accepted to Mathematical Programming (Series A).

selected publications

  1. Two-timescale gradient descent ascent algorithms for nonconvex minimax optimization
    Tianyi Lin, Chi Jin, and Michael I Jordan
    Preprint, 2024
  2. Perseus: A simple and optimal high-order method for variational inequalities
    Tianyi Lin, and Michael I Jordan
    Mathematical Programming (Series A), 2024
  3. On the efficiency of entropic regularized algorithms for optimal transport
    Tianyi Lin, Nhat Ho, and Michael I Jordan
    Journal of Machine Learning Research, 2022