I am a PhD candidate in Biostatistics, working jointly with Mark van der Laan and Alan Hubbard. I am a founding core developer of the tlverse project, the software ecosystem for Targeted Learning. At UC Berkeley, I am affiliated with the Center for Computational Biology and the NIH Biomedical Big Data initiative. I have also enjoyed serving in scientific/statistical collaborations with the Bill & Melinda Gates Foundation, the Kaiser Permanente Division of Research, and the Fred Hutchinson Cancer Research Center.

My research interests sit primarily at the intersection of causal inference and machine learning, with a particular concern towards developing efficient and robust statistical procedures for evaluating complex target estimands within observational studies and randomized trials. Broadly, my work draws on ideas from non/semi-parametric estimation in large, flexible statistical models; high-dimensional inference; targeted loss-based estimation; statistical computing; computational biology; and statistical epidemiology. Of late, my methodological work has touched on causal mediation analysis, stochastic treatment regimes, robust inference in two-phase designs, and efficient estimation with sieve-type methods. I am also quite keenly interested in designing open source statistical software to promote computational reproducibility in applied scientific practice.