Biography
I am an assistant professor in the
Department of Biostatistics at the Harvard
T.H. Chan School of Public Health.
Prior to this, I was an
NSF Mathematical Sciences Postdoctoral Research
Fellow, working on
causal inference and machine learning applied to problems with complex study
designs, especially vaccine efficacy trials. I obtained my PhD in biostatistics
from
UC Berkeley,
where I worked on non-/semi-parametric estimation and causal inference for
continuous-valued exposures and on mediation analysis. In that time, I was on
the founding core development team of the
tlverse
project, an open-source software ecosystem for
targeted learning, and I enjoyed
eclectic scientific collaborations with the
Fred Hutchinson Cancer
Center,
the
Bill and Melinda Gates
Foundation,
and
Netflix Research.
My research interests combine causal inference and machine learning, aiming to formulate robust, efficient, and assumption-lean methods for statistical inference from a translational perspective, tailoring these to specific questions and issues arising in collaborative science. Broadly, I am motivated by methodological topics from non- and semi-parametric (or model-agnostic) inference and corresponding efficiency theory, high-dimensional inference, (targeted) loss-based estimation, the handling of biased sampling procedures, and adaptive experimental designs. While I maintain diverse scientific interests, I have recently been captivated by problems, both statistical and substantive, that arise in the study of infectious diseases and in their epidemiology. I am also deeply interested in high-performance statistical computing and in the development of open-source software for the promotion of reproducibility, transparency, and “data analytic hygiene” in the practice of applied statistics and statistical data science.
Interests
- causal machine learning and causal inference, missing data
- non- and semi-parametric inference and efficiency theory
- high-dimensional inference and bias correction procedures
- statistical machine learning, nonparametric estimation
- statistical computing and reproducible data science
Education
-
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