Hi, welcome to my page! I am a postdoctoral associate at the Department of Electrical and Computer Engineering, University of Minnesota, advised by Prof. Mingyi Hong and Prof. Shuzhong Zhang. I obtained my Ph.D. degree in Applied Mathematics at the Department of Mathematics, UC Davis, advised by Prof. Krishna Balasubramanian and Prof. Shiqian Ma. Prior to this, I received my B.S. degree in Mathematics from Zhejiang University.
My research interests are optimization problems arising in machine learning, operations research and other applications. I am working on the following topics:
- Nonconvex Optimization: Computational complexities for gradient-based and gradient-free (zeroth-order) algorithms, optimization on Riemannian manifolds and primal-dual optimization algorithms.
- Variational Inequalities: Convergence theory for variational inequality with applications in minimax problems.
- Distributed Optimization: Convergence theory for distributed optimization algorithms, including decentralized and federated learning.
- Reinforcement Learning: Theories for reinforcement learning algorithms, especially for multi-armed bandits and convergence of policy-based methods.
I am also interested in theory and computation for supervised and unsupervised learning in general.
Professional Services
- Paper Reviewer
- Journal: INFORMS Journal on Computing, Information and Inference: A Journal of the IMA, JMLR
- Conferences: AISTATS (2021, 2022, 2023), ICML (2022, 2024), NeurIPS (2023), ICLR (2023)
- Invited and Contributed Talks
- INFORMS Optimization Society Conference, Houston, March 2024
- INFORMS Optimization Society Conference, Greenville, March 2022
- Modeling and Optimization: Theory and Applications (MOPTA), August 2021
- SIAM Conference on Optimization, July 2021
Experiences
Applied Scientist Intern at Amazon.com, Prime Science team.
June 2023 - September 2023, Seattle, WASoftware Engineer Intern, Machine Learning at Meta Platforms, Ads Core Machine Learning team.
June 2022 - September 2022, Menlo Park, CA