I completed my PhD in biostatistics at UC Berkeley in 2016, under the supervision of Mark van der Laan. My dissertation, titled "Evaluating Optimal Individualized Treatment Rules", was awarded the Extraordinary Student Research Award from the Berkeley Division of Biostatistics and the Eric Lehmann Citation from the Berkeley Department of Statistics. The first three years of my graduate study were supported by the Department of Defense through the National Defense Science and Engineering Graduate (NDSEG) Fellowship. My final year was supported by the Berkeley Fellowship. I received my ScB in Applied Math from Brown University in 2012.
Research. Most of the problems I work on combine elements of nonparametric inference and causal inference. During graduate school, I developed methods to obtain inference for quantities arising in precision medicine. The techniques I developed also provide solutions to more general non-regular inference problems. I also worked on higher-order (faster than root-n rate) semiparametric inference problems, the automation of efficient statistical inference in both unrestricted and models, and a variant of the multi-armed bandit problem.
My role at Fred Hutch has exposed me to a number of exciting and challenging problems arising in infectious disease research. Thanks to this exposure, I have become interested in developing statistically rigorous methods for analyzing data from vaccine efficacy trials. I have also presented the first two sequentially doubly robust estimators in a nonparametric model, developed fast rates for estimators in precision medicine applications, and continued developing methods for automating efficient inference. I am also a statistician on the Imbokodo HIV vaccine efficacy trial in Southern Africa.