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.
My research currently centers around nonparametric statistics, machine learning, and causal inference, focusing on the development of robust methods for inference in problem settings arising in survival analysis, precision medicine, and computational biology. Of late, I have become quite keenly interested in software development for applied statistics – and, perhaps 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.
Outside of work, I greatly enjoy medium- and long-distance trail running, cooking, performances of live music and stand-up comedy, and reading (currently limited to fiction and philosophy).