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 diverse collaborations with the
Bill and
Melinda Gates
Foundation,
the
Fred Hutchinson Cancer
Center,
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, tailored to issues arising naturally in collaborative science. Broadly, I am motivated by methodological topics from nonparametric (or distribution-free) inference, semiparametric-efficient inference, high-dimensional inference, (targeted) loss-based estimation, uses of biased sampling procedures, and adaptive experimentation. While I maintain eclectic scientific interests, I have recently been captivated by problems – both statistical and substantive – arising from the analysis of clinical trials of infectious diseases and in their epidemiology. I am also deeply interested in high-performance statistical/numerical computing and in the development of open source software for promoting reproducibility and transparency in the practice of applied statistics and statistical data science.
Interests
- causal machine learning, causal inference, missing data
- non/semi-parametric inference, high-dimensional inference
- statistical machine learning, nonparametric estimation
- adaptive experimental designs, biased sampling methods
- statistical computing, 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