Developing optimization algorithms that are both computationally and statistically efficient
Education
- PhD, Operations Research, Cornell University
- MS, Statistics, University of Chicago
- BS, Mathematics and Economics, Hong Kong University of Science and Technology
Research Description
My research lies at the intersection of optimization, statistics, and machine learning. My work focuses on solving fundamental challenges and application problems in data science. Specifically, I analyze the optimization conditioning of convex and nonconvex problems (such as semidefinite programming and Burer-Monteiro approach for matrix recovery) under statistical assumptions, design statistically and computationally efficient optimization algorithms for data science applications (such as phase retrieval and matrix completion), and study the interplay between model overparametrization, algorithmic regularization, and model generalization in classical and modern machine learning models (such as mixture models and neural networks).