Yin Tat Lee

Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he has been faculty since 2017 (initially as Assistant Professor until 2022). He also holds a position as Senior Principal Researcher at Microsoft AI since 2024, having previously served as Principal Researcher (2022-2024) and Visiting Researcher (2018-2022) at Microsoft Research. His academic career spans prestigious institutions including MIT, where he completed his PhD in Mathematics.

• 2024-Now: Member of Technical staff / Senior Principal Researcher in Microsoft AI
• 2022-2024: Principal Researcher in Microsoft Research
• 2022-Now: Associate Professor in University of Washington
• 2017-2022: Assistant Professor in University of Washington
• 2018-2022: Visiting Researcher in Microsoft Research
• 2016-2017: Postdoc in Microsoft Research

Dr. Lee received his PhD in Mathematics from MIT (2012-2016) and his undergraduate degree in Mathematics from the Chinese University of Hong Kong (2008-2012). His exceptional academic journey was recognized with the MIT Presidential Fellowship and the Charles W. and Jennifer C. Johnson Prize.

Lee's research fundamentally advances algorithms across multiple domains, particularly in convex optimization, convex geometry, spectral graph theory, and online algorithms. His work bridges continuous and discrete mathematics to develop state-of-the-art algorithms for fundamental problems in computer science and optimization. Notably, he has developed breakthrough approaches for linear programming, maximum flow problems, and optimization in high-dimensional spaces. His research has evolved from foundational theoretical work to more applied areas including differential privacy and connections to machine learning.

Analysis of his recent publications reveals a strong trajectory toward practical applications of theoretical optimization, with significant contributions to differentially private machine learning, efficient sampling methods, and connections between optimization theory and deep learning. His work consistently demonstrates how deep theoretical insights can yield practical algorithmic improvements across computer science.

Lee's exceptional contributions have been recognized with numerous prestigious awards including the Packard Fellowship, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, A.W. Tucker Prize, and multiple Best Paper Awards at top theoretical computer science conferences (FOCS, SODA, NeurIPS). He has also received the NSF CAREER Award and MIT's Sprowls Award for his doctoral thesis.

• Packard Fellowship (2020)
• Sloan Research Fellowship (2020)
• Microsoft Research Faculty Fellowship (2019)
• Best Paper Awards at FOCS, SODA, and NeurIPS
• A.W. Tucker Prize
• NSF CAREER Award

As an advisor, Lee has mentored PhD students including Haotian Jiang, whose work earned a Best Student Paper award at SODA. His research has been supported by significant grants from NSF and Microsoft Research. Lee actively contributes to the academic community through service on program committees for FOCS, SODA, and other major conferences, as well as organizing workshops on continuous approaches to discrete optimization. He has also taught graduate courses including Theory of Optimization and Continuous Algorithms and undergraduate courses on algorithms.

Lee's work bridges theoretical computer science and practical applications, with his recent research expanding into differential privacy for machine learning and connections between optimization theory and deep learning. His collaborative work spans institutions including MIT, Microsoft Research, and the University of Washington, reflecting his position at the intersection of theoretical and applied computer science.