I am an Assistant Professor in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. I recently completed a year as an NSF postdoctoral research fellow, during which I worked closely with Iván Díaz, David Benkeser, and Peter Gilbert. Just prior to this, I completed my PhD in biostatistics under the joint supervision of Mark van der Laan and Alan Hubbard at UC Berkeley. In that time, I was on the founding core development team of the tlverse project, an extensible software ecosystem for Targeted Learning, and I enjoyed scientific collaborations with the Bill & Melinda Gates Foundation and the Fred Hutchinson Cancer Center.

My research interests combine causal inference and machine learning, driven by the aim of developing assumption-lean statistical procedures tailored for efficient and robust inference about scientifically informative parameters. I am particularly motivated by methodological issues stemming from robust non/semi-parametric inference, high-dimensional inference, targeted loss-based estimation, and biased sampling designs, usually tied to applications from clinical trials or computational biology. Although my substantive scientific interests are diverse, I have been very interested in statistical problems stemming from vaccine efficacy trials and from infectious disease epidemiology and immunology. I am also deeply interested in high-performance statistical/numerical computing and the development of open source software for reproducible statistical data science and applied statistics.