I am a PhD student in the Group in Biostatistics at UC Berkeley, supervised jointly by Mark van der Laan and Alan Hubbard. I am lucky to also work under the supportive guidance of David Benkeser and Nicholas Jewell. I am affiliated with the Center for Computational Biology through the designated emphasis program in computational and genomic biology, and I am a fellow (2017-2018) of the UC Berkeley NIH Biomedical Big Data training program. I hold an M.A. (2017) in Biostatistics and a B.A. (2015) with a triple major in Molecular & Cell Biology, Psychology, and Public Health – both awarded by UC Berkeley. I maintain a list of my graduate school course work here.
My current research lies at the intersection of three major topics: (1) causal inference; (2) nonparametric statistical estimation and machine learning; and (3) statistical computing, software design, reproducible research, and data science. The primary focus of my work is the development of robust inferential techniques for use in a diversity of problem settings, with applications commonly arising in precision medicine, computational biology, vaccine efficacy trials, and public policy.
My research interests vary quite widely, encompassing aspects of causal inference, censored data models, and semiparametric theory; targeted minimum loss-based estimation; survival analysis; nonparametric inference and hypothesis testing; data-adaptive estimation, machine learning, and optimization; statistical computing, software design, reproducible research, and data science. If you like any of these, we can probably find something interesting to chat about.
While my work is driven primarily by problems in the biomedical and health sciences, I harbor a passion for applying statistics to helping solve impactful problems and promoting social good. Accordingly, I frequently enjoy working in teams towards developing statistical solutions to well motivated applied problems.
I try to keep myself busy with a combination of