About Me

I am a postdoc at Stanford University and Gladstone Institutes 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 work exists at the intersection of AI, statistics, and computational biology. My dissertation research focused on Bayesian methods for 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.

Research Interests

• Probabilistic machine learning, Bayesian statistics, deep learning
• Computational biology, precision medicine, biomarker discovery, drug discovery
• Anytime-valid inference, uncertainty quantification, sampling algorithms, time series
• Model-based clustering, probabilistic graphical models, transfer learning

Recent News

03/18/2026: Check out our new preprint on Besag-Clifford e-values, a new method for hypothesis testing with e-values.

01/23/2026: I was awarded a Statistics in Epidemiology 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.

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