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. I grew up outside of Philadelphia.
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 computerization of the calculation of efficient influence functions in both unrestricted and restricted 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 bridging the results of completed efficacy trials to new settings, and have also begun to study efficient estimation methodologies under multiphase sampling designs. I have also continued my work in developing fast rates for estimators in precision medicine applications and have introduced the notion of sequential double robustness and presented two estimators attaining this property. I am also working with colleagues to prepare the statistical protocol and analysis plan for an upcoming Phase 2b HIV vaccine trial in Southern Africa.