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.
PhD in Biostatistics (designated emphasis in Computational & Genomic Biology), 2021
University of California, Berkeley
MA in Biostatistics, 2017
University of California, Berkeley
BA with a triple major in Molecular & Cell Biology (em. Neurobiology), Psychology, and Public Health, 2015
University of California, Berkeley