I am a Ph.D. student in the Division of Biostatistics at UC Berkeley. I am jointly advised by Mark van der Laan and Alan Hubbard. As of 2017, I am a fellow of the Biomedical Big Data training program at UC Berkeley. Prior to this, in 2017, I was awarded an M.A. in Biostatistics, and, in 2015, a B.A., with a triple major in Molecular & Cell Biology, Psychology, and Public Health, both by UC Berkeley. A list of my graduate school course work may be viewed here.
My research currently centers around nonparametric statistics, causal inference, and machine learning, focusing on the development of robust techniques for both estimation and inference in problems arising in precision medicine, survival analysis, and computational biology. Of late, I have become quite keenly interested in software development for applied statistics – and, by extension, in data science.
My research interests vary widely, encompassing data-adaptive and nonparametric procedures for estimation and hypothesis testing, causal inference, survival analysis, targeted minimum loss-based estimation, computational biology, reproducible research, and scientific computing. While my work is driven by problems in the biomedical sciences and public health, I harbor a passion for applying statistics to helping solve impactful problems and promoting social good; accordingly, I enjoy providing statistical support to a diversity of applied sciences.
I try to keep myself busy with a combination of