I am a third year Ph.D. student in the Department of Mathematics at the University of California, Berkeley. I am co-advised by Prof. James Demmel and Prof. Javad Lavaei. I also work closely with Prof. Sen Na. I am interested in stochastic optimization, artificial intelligence, reinforcement learning, and high-performance computing.
Prior to Berkeley, I obtained my master’s degree in Computational and Applied Mathematics from the University of Chicago, where I was advised by Prof. Mladen Kolar and Prof. Sen Na. I also received supervision from Prof. Mihai Anitescu and Prof. Michael W. Mahoney on my master’s thesis and research projects. I obtained my bachelor’s degree in Statistics from Xiamen University with the supervision from Prof. Yingxing Li on my undergraduate thesis.
Research Interests
- stochastic nonlinear optimization
- high performance computing
- numerical linear algebra
- high-dimensional statistics
- reinforcement learning
- artificial intelligence
News
- Jan. 2026: A new paper about Math Benchmark that I participated in “QEDBench: Quantifying the Alignment Gap in Automated Evaluation of University-Level Mathematical Proofs” is submitted.
- Jan. 2026: A new paper 3DGS$^2$-TR: A Scalable Second-Order Trust-Region Method for 3D Gaussian Splatting is submitted.
- Jan. 2026: A new paper Why is Normalization Preferred? A Worst-Case Complexity Theory for Stochastically Preconditioned SGD under Heavy-Tailed Noise is submitted.
- Jan. 2026: A new paper TRSVR: An Adaptive Stochastic Trust-Region Method with Variance Reduction is available on arXiv and submitted.
- Oct. 2025: I gave a talk at NFORMS Annual Meeting, with the title “High-Probability Complexity Bounds of Trust-Region Stochastic Sequential Quadratic Programming with Heavy-Tailed Noise”
- Mar. 2025: A new paper High Probability Complexity Bounds of Trust-Region Stochastic Sequential Quadratic Programming with Heavy-Tailed Noise is available on arXiv and submitted.
- Sep. 2024: A new paper Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models is available on arXiv and submitted.
- Jun. 2024: The paper Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems is published online.
- Jan. 2024: The paper Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems is accepted by SIAM Journal on Optimization.
- Aug. 2023: I start my new journey at UC Berkeley.
- Apr. 2023: The paper Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems is under major revision at SIAM Journal on Optimization.
- Dec. 2022: I am graduating from UChicago, but the past year and a half will stay with me forever. This city has given me the happiest days of my life. I am deeply grateful for my supervisors and friends—brilliant and warm, like sunshine illuminating my journey in this lakeside city. I will never forget those moments in Regents Park, gazing at the endless expanse of Lake Michigan, lost in its beauty. Time moves forward, never looking back, but the memories will always remain.
- Dec. 2022: Present at Higher-Order Optimization in Machine Learning (HOOML) 2022 NeurIPS Workshop.
- Nov. 2022: A new paper Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems is available on arXiv and submitted.
- Nov. 2022: Present at Computational and Applied Mathematics (CAM) Seminar at UChicago.
- Oct. 2022: Two papers are accepted by the NeurIPS Workshop Higher-Order Optimization in Machine Learning (HOOML) 2022.