Biography
I am a PhD candidate in biostatistics, with a designated emphasis in computational and genomic biology, working with Mark van der Laan and Alan Hubbard. I am a founding core developer of the tlverse, the software ecosystem for Targeted Learning, and a workshop instructor with Software Carpentry. At UC Berkeley, I am affiliated with the Center for Computational Biology and the NIH Biomedical Big Data initiative. I have also served in biostatistical collaborations with the Bill & Melinda Gates Foundation and the Kaiser Permanente Division of Research.
My research interests primarily concern the development of robust and efficient statistical techniques at the intersection of machine learning and causal inference, aiming to facilitate data adaptive estimation and robust nonparametric inference for complex target estimands with data from observational or randomized studies. Broadly, my interests span nonparametric estimation, semiparametric theory, high-dimensional inference, targeted learning, statistical computing, and computational biology. Of late, my methodological work has touched upon stochastic treatment regimes, causal mediation analysis, robust inference under two-phase sampling, and data-adaptive conditional density estimation. I am also quite keenly interested in the design of open source software and the use of automated testing for the promotion of reproducibility in applied statistical and 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-present
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