About
I am a PhD student in theoretical chemistry at the University of Chicago, advised by Aaron R. Dinner and Jonathan Weare.
My research develops generative and probabilistic sampling methods for molecular simulation and statistical physics. I am particularly interested in diffusion/flow-based molecular sampling, Gibbs sampling, posterior inference, and path-integral quantum statistics.
Recent projects include generative Gibbs sampling for recovering quantum statistics from classical simulations and inference-time composition of diffusion priors with explicit physical contexts.
Download CV / Google Scholar / GitHub / ORCID
Selected Research
Generative Gibbs Sampling for Path-Integral Quantum Statistics
A generative sampling framework that uses flow-matching models and Gibbs sampling to recover quantum statistics from classical molecular dynamics data.
Composing Diffusion Priors with Explicit Physical Context
An inference-time framework for reusing pretrained diffusion priors under explicit physical constraints through an augmented target distribution, without retraining.
Research Interests
- Generative models for molecular simulation
- Probabilistic sampling and posterior inference
- Statistical mechanics and path-integral methods
- Enhanced sampling and rare-event molecular dynamics
- Physics-aware AI for Science
News
- May 2026: Awarded the Josef Fried Graduate Fellowship by the Department of Chemistry at the University of Chicago.
- May 2026: New preprint on composing diffusion priors with explicit physical context via generative Gibbs sampling.
- January 2026: Preprint on quantum statistics from classical simulations via generative Gibbs sampling.
Honors and Fellowships
- Josef Fried Graduate Fellowship, Department of Chemistry, University of Chicago, 2026-2027. Awarded in recognition of excellence in research.
- Neubauer Family Distinguished Doctoral Fellow, University of Chicago, 2024-present.