About
I am a Ph.D. student in Statistics at Columbia University, advised by David Blei. Before that, I received a B.S. in Mathematics and Statistics from Rice University, where I worked with Daniel Kowal.
I develop methods and theory for probabilistic inference at scale. See my papers. Recent themes include:
- Scaling and generalizing empirical Bayes
- Approximate posterior sampling via optimal transport
- Learning causality from heterogeneous data
News
- June 2026 Started as a research intern on Netflix's Machine Learning and Inference Research (MLIR) team.
- May 2026 Gave a talk about empirical Bayes at the Bayes Reading Group, Flatiron Institute.
- April 2026 Received the ISBA Junior Travel Award to attend the 2026 ISBA World Meeting in Nagoya.
- Jan 2026 Gave a talk at the Online Monte Carlo Seminar. Video.