About

I am a postdoctoral researcher at the Simons Institute at UC Berkeley, hosted by Peter Bartlett and Bin Yu, and affiliated with the Machine Learning Research Pod and the Berkeley Statistical Machine Learning Group. I received my PhD in Statistics and Data Science from Yale University in 2025, advised by Andrew Barron, and my undergraduate and master’s degrees in Applied Mathematics and Engineering from Queen’s University, advised by Serdar Yüksel.

The main theme of my research is the convergence behaviour of stochastic processes: how error propagates through a random system, how quickly a system converges to its invariant distribution, and the properties of this distribution in downstream applications. Recently I have focused on sampling from multimodal and non-log-concave distributions, where traditional MCMC methods based on time invariant local likelihood updates can have difficulty exploring the full state space and can get trapped in local modes of the log-likelihood for long periods of time. Time varying transition rules, annealing, auxiliary random variables, and diffusion models all present interesting alternatives to produce samples for such densities beyond traditional MCMC. Earlier work addressed filter stability in hidden Markov models and its applications to robust stochastic control. I am also interested in applications of generative models in the physical sciences and to AI safety.

I currently co-organize the ML and AI Theory Seminar and reading group.

For more information, please see my Publications page, CV, or LinkedIN profile.

Upcoming News

• I am currently on the academic job market interested in Assistant Professor positions beginning September 2027 in Statistics, Computer Science, and Engineering departments with a focus on stochastic processes and machine learning.