I am a PhD candidate in Applied and Computational Mathematics at Institute for Computational and Mathematical Engineering, Stanford University. I am privileged to be supervised by Prof. Lexing Ying (Applied Mathematics) and Prof. Grant M. Rotskoff (Computational Chemistry).

My research interests lie in the intersection of machine learning, stochastic analysis, and numerical analysis. I am now working on the mathematical foundations and algorithmic design of flow and diffusion-based models, e.g., inference-time scaling of diffusion models (ICLR 2026), fast solver for discrete diffusion (NeurIPS 2025), and parallel diffusion sampling (NeurIPS 2024 Spotlight).

During Winter 2026, I am a Student Researcher at ByteDance Seed. During Summer 2025, I was a Visiting Researcher at Flatiron Institute, Simons Foundation, hosted by Dr. Jiequn Han, working on the inference-time scaling of diffusion models. During Summer 2023 and 2024, I was an Applied Scientist Intern at Amazon Science, working on multi-objective LLM fine-tuning, and multi-objective optimization. I obtained my BS in Computational Mathematics from School of Mathematical Sciences, Peking University, supervised by Prof. Ruo Li.