I am a PhD candidate in Applied and Computational Mathematics at the Institute for Computational and Mathematical Engineering, Stanford University. I am fortunate to work with Prof. Lexing Ying (Applied Mathematics) and Prof. Grant M. Rotskoff (Computational Chemistry).

I work at the intersection of machine learning, stochastic analysis, and numerical analysis. My current research focuses on the mathematical foundations and algorithmic design of flow and diffusion-based models, e.g., a unified approach to analysis and design of denoising Markov models (JMLR), inference-time scaling of diffusion models (ICLR 2026), and parallel diffusion sampling (NeurIPS 2024 Spotlight).

Previously, I was a

• Student Researcher at ByteDance Seed, working on LLM research (Winter 2026).
• Visiting Researcher at Flatiron Institute, Simons Foundation, hosted by Dr. Jiequn Han, working on the inference-time scaling of diffusion models (Summer 2025).
• Applied Scientist Intern at Amazon Science, working on multi-objective LLM fine-tuning and multi-objective optimization (Summer 2023 & 2024).

I obtained my BS in Computational Mathematics from the School of Mathematical Sciences, Peking University, supervised by Prof. Ruo Li.