About Me

I am a postdoc at Gladstone Institutes and Stanford University in Barbara Engelhardt’s group. In May 2025, I completed my PhD in Statistical Science at Duke, and I have an MS and BS in Mathematics and Statistics from McGill University. My research exists at the intersection of AI/ML, statistics, and biomedical science, with a focus on developing ML methods that provide domain-interpretable insights. Currently, I am working on ML and generative model evaluation with e-values, as well as contrastive representation learning. My dissertation research focused on robust and multimodal clustering, sepsis subtyping, and probabilistic graphical models.

When I’m not working, I like running, skiing, reading, playing guitar, and watching the Premier League.

Recent News

01/23/2026: I was awarded an SIE Early Career Award for HiDDeN! Looking forward to presenting at JSM 2026 in Boston.

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.