I’m an assistant professor of biostatistics at McGill University with a joint appointment in the Department of Epidemiology, Biostatistics, and Occupational Health and the Department of Medicine.
I’m broadly interested in the development of assumption-lean statistical methods and their application to quantitative problems in the health and life sciences. Assumption-lean procedures combine causal inference and machine learning techniques to avoid unjustified assumptions about the data-generating process, encouraging dependable statistical inference. I’m also committed to the development open-source statistical software and, more broadly, to the adoption of reproducible research practices. My most recent work has focused on causal machine learning methods for treatment effect modifier discovery in gene expression data collected during oncology trials.
Prior to joining McGill, I completed a PhD in Biostatistics at the University of California, Berkeley under the supervision of Sandrine Dudoit. Much of my time was spent studying nonparametric procedures for high-dimensional data analysis. I also frequently collaborated with epidemiologists, biologists, and clinicians, guiding experimental design and analyzing ’omic data.