I am a Ph.D. student in the Group in Biostatistics at UC Berkeley. I am 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. Currently, I am a fellow of the UC Berkeley NIH Biomedical Big Data training program. My research centers around causal inference, nonparametric estimation and machine learning, and statistical computing. The primary focus of my work is the development of robust methods for inference and estimation in an eclectic collection of problem settings, with applications often arising in precision medicine, vaccine efficacy trials, computational biology, and public policy. My interests are broad, including aspects of causal inference, censored data models and semiparametric theory, survival analysis, data-adaptive inference, nonparametric inference and hypothesis testing, machine learning and optimization, and targeted maximum likelihood estimation. If you like any of these, we can probably find something interesting to chat about.
Ph.D. - Biostatistics, 2016-present
University of California, Berkeley
M.A. - Biostatistics, 2017
University of California, Berkeley
B.A. - Molecular & Cell Biology, Psychology, and Public Health, 2015
University of California, Berkeley
(see CV for a full list)
(see CV for a full list)