I am an assistant professor in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. Prior to this, I was an NSF Mathematical Sciences Postdoctoral Research Fellow, working on causal inference and machine learning applied to problems with complex study designs, especially vaccine efficacy trials. I obtained my PhD in biostatistics from UC Berkeley, where I worked on non-/semi-parametric estimation and causal inference for continuous-valued exposures and on mediation analysis. In that time, I was on the founding core development team of the tlverse project, an open-source software ecosystem for targeted learning, and I was lucky to enjoy diverse scientific collaborations with the Fred Hutchinson Cancer Center, the Bill and Melinda Gates Foundation, and Netflix Research.

My research interests lie primarily in unifying statistical methodology for causal inference and machine learning under the central aim of developing efficient and robust, assumption-lean inferential techniques tailored for the applied sciences. Broadly speaking, I am often motivated by methodological topics from non- and semi-parametric inference (that is, from an assumption-lean or model-agnostic perspective), high-dimensional inference, applications of (targeted or minimum) loss-based estimation, corrections for the usage of biased sampling procedures, and the design of adaptive experiments. While my applied science interests are diverse, I have recently been captivated by problems that commonly arise in the study of infectious diseases and in their epidemiology, including clinical trials of these. I am also deeply interested in high-performance statistical computing and open-source software development to promote reproducibility, transparency, and data analysis “hygiene” in applied statistics and statistical data science.