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. Currently, I am working on ML model comparison with e-values and identifying biomarkers for neurodegenerative diseases. 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, artificial intelligence
- Precision medicine, biomarker discovery, transcriptomics, proteomics, drug discovery
- Game-theoretic statistics, uncertainty quantification
- Clustering, graphs, multimodal data integration, 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 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.