Jerry Li

Jerry Li is an associate professor at the University of Washington's Paul G. Allen School of Computer Science & Engineering. Previously, he was a principal research scientist at Microsoft Research Redmond and was the VMware Research Fellow at the Simons Institute in Fall 2018.

Li completed his Ph.D. at MIT under the supervision of Ankur Moitra and his master's degree at MIT under Nir Shavit. As an undergraduate, he also attended the University of Washington, where he worked on complexity of branching programs and hardness of learning problems in database theory and AI.

His primary research interests focus on learning theory broadly defined, with specific expertise in quantum information theory, large foundation models, and high-dimensional statistics. He has a particular interest in applying analysis and analytic techniques to theoretical computer science problems. His recent work spans quantum computing, robust machine learning, and theoretical foundations of deep learning, showing a clear trend toward bridging quantum information theory with statistical learning theory.

Li has made significant contributions to the fields of robust statistics, quantum computing, and theoretical machine learning, with numerous publications in top-tier conferences and journals including FOCS, STOC, NeurIPS, ICML, and Science. His work often bridges theoretical guarantees with practical applications in machine learning systems, particularly in the areas of robustness and quantum advantage.

• George M. Sprowls Award for outstanding Ph.D. theses in EECS at MIT
• Best Artifact Award at PPoPP 2015 for "The SprayList: A Scalable Relaxed Priority Queue"
• Communications of the ACM Research Highlights for "Robust Estimators in High Dimensions without the Computational Intractability"
• Invited to special issues of SIAM Journal on Computing for FOCS 2023 and STOC 2022
• Spotlight Presentations at NeurIPS 2019
• Notable top 5% paper at ICLR 2023

Li advises several Ph.D. students including Ziyun Chen (co-advised with Shayan Oveis Gharan) and numerous research interns. He has served on program committees for major conferences including STOC, SODA, and ITCS, and is co-organizing the FOCS 2024 Workshop on Recent Advances in Quantum Learning. His teaching includes courses such as CSE 422: Toolkit for Modern Algorithms and CSE 599-M: Robustness in Machine Learning, for which he has created publicly available video lectures.