Yin Tat Lee

Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, and a Senior Principal Researcher at Microsoft AI. His research spans Convex Optimization, Convex Geometry, Graph Algorithms, Online Algorithms, and Differential Privacy, with a focus on designing theoretically optimal algorithms for combinatorial and convex problems.

Lee earned a Ph.D. in Mathematics (2016) and B.S. in Mathematics (2012) from MIT and The Chinese University of Hong Kong, respectively. His academic appointments include

• 2022–Present: Associate Professor, University of Washington
• 2017–2022: Assistant Professor, University of Washington
• 2018–2022: Visiting Researcher, Microsoft Research
• 2016–2017: Postdoctoral Researcher, Microsoft Research

Lee’s research has revolutionized algorithmic efficiency, particularly in linear and semidefinite programming, graph algorithms, and differential privacy. Key contributions include the first nearly-linear-time algorithm for linear programs with small treewidth (STOC 2021), solving linear programs as fast as linear systems (STOC 2019), and optimal distributed non-smooth optimization (NeurIPS 2018). His work integrates techniques from convex geometry, spectral graph theory, and stochastic processes.

His publications span topics like Riemannian Hamiltonian Monte Carlo (NeurIPS 2022), Bandit Convex Optimization (STOC 2017), and K-server Problem (STOC 2018), with a recurring emphasis on

• Algorithm Design
• High-Dimensional Sampling
• Privacy-Preserving Computation
• Matrix and Graph Theory

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Major awards include

• Packard Fellowship
• Sloan Research Fellowship
• NSF CAREER Award
• Best Paper Awards at FOCS, SODA, NeurIPS
• Sprowls Award (MIT)
• A.W. Tucker Prize

His students include Haotian Jiang, who won the Best Student Paper at SODA 2014.