Biography
I am a PhD candidate in
Biostatistics, working jointly with
Mark van der Laan and
Alan
Hubbard. I am a founding core developer of
the
tlverse project, the software ecosystem for
Targeted Learning. At UC Berkeley, I am affiliated with the
Center for
Computational Biology and the
NIH Biomedical Big
Data initiative. During my time in graduate school,
I have also enjoyed scientific and statistical collaborations with the
Bill &
Melinda Gates
Foundation,
the
Kaiser Permanente Division of
Research, the
Fred
Hutchinson Cancer Research
Center,
and
Netflix.
My research interests sit primarily at the intersection of causal inference and machine learning, with a particular concern towards developing efficient and robust statistical procedures for evaluating complex target estimands within observational studies and randomized trials. Broadly, my work draws on ideas from non/semi-parametric estimation in large, flexible statistical models; high-dimensional inference; targeted loss-based estimation; statistical computing; computational biology; and statistical epidemiology. Of late, my methodological work has touched on causal mediation analysis, stochastic treatment regimes, robust inference in two-phase designs, and efficient estimation with sieve-type methods. I am also quite keenly interested in designing open source statistical software to promote computational reproducibility in applied scientific practice.
Interests
- Causal Inference and Censored Data Models
- Nonparametric Estimation and Machine Learning
- Semiparametric Theory and Robust Statistics
- High-Dimensional and Computational Biology
- Statistical Computing and Reproducible Research
Education
-
PhD in Biostatistics (designated emphasis in Computational and Genomic Biology), 2017-2021 (expected)
University of California, Berkeley
-
MA in Biostatistics, 2017
University of California, Berkeley
-
BA with a triple major in Molecular and Cell Biology (em. Neurobiology), Psychology, and Public Health, 2015
University of California, Berkeley