Hi, welcome to my page! I am a Research Scientist at Meta. Prior to this, I was a postdoctoral associate at the Department of Electrical and Computer Engineering, University of Minnesota, mentored 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 CV is here.
My research focuses on the optimization foundations of machine learning model training, spanning both classical models and modern deep learning systems. I am increasingly drawn to the principles behind large language model (LLM) pretraining and alignment, where I aim to develop algorithms that bridge rigorous theory with the practical demands of modern AI.
News
- May 2026Two papers are accepted by ICML 2026. Congratulations to all my collaborators!
- A minimalist optimizer design for LLM pretraining
- A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization
- January 2026Paper Muon Outperforms Adam in Tail-End Associative Memory Learning is accepted by ICLR 2026. Congratulations to all my collaborators!
- August 2025Starting my new job as Research Scientist at Meta!
- February 2025Paper Problem-Parameter-Free Decentralized Nonconvex Stochastic Optimization is accepted by Pacific Journal of Optimization.
- January 2025Paper Joint Demonstration and Preference Learning Improves Policy Alignment with Human Feedback is accepted by ICLR 2025 (Spotlight). Congratulations to all my collaborators!
- January 2025Paper Riemannian Bilevel Optimization is accepted by Journal of Machine Learning Research!
- November 2024Paper A Riemannian ADMM is accepted by Mathematics of Operations Research!
- September 2024Two papers are accepted by NeurIPS 2024. Congratulations to all my collaborators!
- Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
- SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining
- August 2024I'm very happy to receive the INFORMS Computing Society Prize!
- August 2024Paper Zeroth-order Riemannian Averaging Stochastic Approximation Algorithms is accepted by SIAM Journal on Optimization!
- July 2024A new grant Bi-Level Optimization for Hierarchical Machine Learning Problems: Models, Algorithms and Applications is awarded from NSF. I'm excited to be the co-PI of this project with Prof Hong!
- May 2024Paper Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark is accepted by ICML 2024. Congratulations to all my collaborators!