About Me

I am a postdoc at Gladstone Institutes in Barbara Engelhardt’s group. My work combines statistics and machine learning to develop probabilistic models and decision-making tools, with a focus on unsupervised learning, graphical models, nonparametric methods, sequential processes, and biomedical applications.

In May 2025, I completed my PhD in Statistical Science at Duke, supervised by David Dunson and Amy Herring. Previously, I obtained my master’s degree from McGill University in Mathematics and Statistics, supervised by Russell Steele. I was also an undergraduate at McGill, where I received a bachelor’s degree in Mathematics.

Outside of statistics, I like running, the NYT crossword, playing guitar, and watching the Premier League.

Recent News

09/16/2025: Check out Learning discrete Bayesian networks with hierarchical Dirichlet shrinkage, a new preprint written with David Dunson about graphical modeling of categorical data.

08/28/2025: Bayesian learning of clinically meaningful sepsis phenotypes in northern Tanzania has been published in the Annals of Applied Statistics.

06/24/2025: I gave a talk at BNP 14 on discrete Bayesian networks.

04/30/2025: Product centred Dirichlet processes for Bayesian multiview clustering has been published in the Journal of the Royal Statistical Society, Series B.

03/03/2025: I successfully defended my dissertation, titled Bayesian Inference for Discrete Structures.