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 lies at the intersection of applied mathematics, optimization theory, and artificial intelligence, with a focus on developing principled algorithms that advance both theoretical foundations and practical applications in modern machine learning. Specifically, I work on:
- Algorithm design for gradient-based, gradient-free (zeroth-order), and primal–dual methods for large-scale nonconvex optimization, including problems on Riemannian manifolds.
- Convergence theory for deterministic and stochastic minimax and bilevel optimization, with applications to machine learning and operations research.
- Distributed, decentralized and federated optimization algorithms for training large-scale AI systems.
- Theoretical foundations of reinforcement learning, especially policy-based methods, and their role in aligning large language models (LLMs).
- Efficient pre-training and fine-tuning methods for LLMs, bridging optimization principles with practical deployment.
News
August 2025: Starting my new job as Research Scientist at Meta!
February 2025: paper “Problem-Parameter-Free Decentralized Nonconvex Stochastic Optimization” is accepted by Pacific Journal of Optimization.
- January 2025: One paper is accepted by ICLR 2025 (Spotlight). Congratulations to all my collaborators!
- Joint Demonstration and Preference Learning Improves Policy Alignment with Human Feedback
January 2025: Paper “Riemannian Bilevel Optimization” is accepted by Journal of Machine Learning Research!
November 2024: Paper “A Riemannian ADMM” is accepted by Mathematics of Operations Research!
- September 2024: Two 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 2024: I’m very happy to receive the INFORMS Computing Society Prize!
August 2024: Paper “Zeroth-order Riemannian Averaging Stochastic Approximation Algorithms” is accepted by SIAM Journal on Optimization!
July 2024: A 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 2024: Paper “Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark” is accepted by ICML 2024. Congratulations to all my collaborators!