معرفی
Tianyi Lin serves as an Assistant Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia Engineering, Columbia University, a position he assumed in 2024. He holds dual affiliations as a verified Data Science Institute (DSI) Member and an Affiliated Member of both the Financial and Business Analytics Center and the Foundations of Data Science Center.
His academic credentials include:
- Ph.D. in Electrical Engineering and Computer Science, UC Berkeley
- Postdoctoral Researcher, Laboratory for Information & Decision Systems (LIDS), MIT (2023-2024)
- M.S. in Operations Research, UC Berkeley
- M.S. in Pure Mathematics and Statistics, University of Cambridge
- B.S. in Mathematics, Nanjing University
Dr. Lin's research spans optimization theory, game-theoretic models, and machine learning algorithms, with emphasis on nonconvex minimax problems, variational inequalities, and data science applications. His work bridges theoretical guarantees with practical implementations in high-dimensional settings, particularly focusing on convergence properties and computational efficiency in complex systems.
Analysis of his 15 most recent publications (2022-2025) reveals dominant themes in high-order optimization methods, no-regret learning in games, and optimal transport algorithms. His contributions demonstrate consistent innovation in developing doubly optimal algorithms for monotone games, spectral regularization techniques for policy optimization, and structure-driven approaches for nonconvex problems, reflecting strong interdisciplinary connections between operations research, computer science, and applied mathematics.
No scientific awards or honors were documented in the provided source material.
Information regarding student advising and research grants remains unspecified in the current documentation, though his center affiliations suggest active participation in collaborative research initiatives.
Dr. Lin maintains significant interdisciplinary engagement through his affiliations with Columbia's Data Science Institute and specialized research centers, positioning his work at the intersection of theoretical optimization and real-world data science applications.
