I am a second year Ph.D. student in the Department of Mathematics at the University of California, Berkeley. My research interests lie in optimization, with focus on stochastic optimization and nonlinear optimization. I am interested in designing algorithms with rigorous theoretical guarantees and conducting algorithm analysis. I am also interested in applying state-of-the-art optimization algorithms to solve problems in operations research, high-dimensional statistics, machine learning and finance.
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
- numerical linear algebra
- high-dimensional statistics
- statistical learning
- mathematical finance
News
- 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: 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.