Zaiwei Chen

Bio: I am currently a CMI postdoctoral fellow in The Computing + Mathematical Sciences (CMS) Department of California Institute of Technology, hosted by Dr. Adam Wierman and Dr. Eric Mazumdar. I obtained a Ph.D. degree in Machine Learning, an M.S. degree in Mathematics, and an M.S. degree in Operations Research from Georgia Institute of Technology, where I was advised by Dr. Siva Theja Maguluri and Dr. John-Paul Clarke. Before that, I obtained my B.S. degree in Electrical Engineering at Chu Kochen Honors College, Zhejiang University.

Honors and Awards: I was a recipient of the Simoudis Discovery Prize, and was named a PIMCO Postdoctoral Fellow in Data Science in 2022. My Ph.D. thesis won the Sigma Xi Best Ph.D. Thesis Award, and was selected as a runner-up for the 2022 SIGMETRICS Doctoral Dissertation Award. Before that, I received the ARC-TRIAD Student Fellowship in 2021, and was selected as as one of 7 nominees to represent Georgia Institute of Technology at the 2021 Schmidt Science Fellows Award Competition. A proposal based on my research received The IDEaS-TRIAD Research Scholarship in 2020.

Research Summary: My research interest is on designing data-efficient reinforcement learning algorithms with provable convergence and sample complexity guarantees. More broadly, I am interested in developing theoretical foundations of machine learning with applications in optimization and control.

Recent News

I jointly presented a 2-part tutorial on Finite-Sample Guarantees of Contractive Stochastic Approximation with Applications in Reinforcement Learning at the workshop of Structure of Constraints in Sequential Decision-Making held by Simons Institute for the Theory of Computing. Videos of Part 1 and Part 2.

Featured Work (See here for the complete list)