I am a PhD student in the Group in Biostatistics at UC Berkeley, under the joint supervision of Mark van der Laan and Alan Hubbard. I am lucky to also work under the supportive guidance of David Benkeser and Nicholas Jewell. Currently, I am a fellow of the UC Berkeley NIH Biomedical Big Data training program. Prior to this, I was awarded an M.A. (2017) in Biostatistics and a B.A. (2015) with a triple major in Molecular & Cell Biology, Psychology, and Public Health – both by UC Berkeley. Here is a list of my graduate school course work.
My current research lies at the intersection of three major topics: (1) robust causal inference, (2) nonparametric statistical estimation and machine learning, and (3) statistical computing. The primary focus of my work is the development of robust techniques for both estimation and inference in a wide variety of problem settings, with applications commonly arising in precision medicine, computational biology, and public policy. Of late, I have become quite keenly interested in software development for applied statistics – and, by extension, in data science.
My research interests vary quite widely, encompassing nonparametric estimation and hypothesis testing, data-adaptive inference, robust causal inference, targeted maximum likelihood estimation, missing and censored data problems, survival analysis, machine learning, optimization, computational biology, reproducible research, and statistical computing. 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 enjoy developing statistical solutions to a diversity of applied problems. If you like any of these, we can probably find something interesting to chat about.
I try to keep myself busy with a combination of