I am an assistant professor of biostatistics at the Harvard Chan School of Public Health, where I lead and organize the NSH Lab (pronounced like “niche”)—a (bio)statistical science research group that is focused on developing novel theory, methods, algorithms, and open-source software tools for causal inference and causal (or debiased or targeted) machine learning, non-parametric statistics, statistical machine learning, model-agnostic inference, and applied semi-parametric theory for causal machine learning. My statistical methods research is motivated by data-driven, real-world questions that arise from collaborations with applied biomedical and public health scientists. Specific areas of focus have included infectious disease sciences (especially the evaluation of investigational therapeutic agents and immune correlates of vaccine protection), the study of chronic diseases (e.g., Long COVID), and cancer science—and comparative effectiveness studies and implementation science in these areas.
Prior to joining the faculty in the Department of Biostatistics at the Harvard Chan School of Public Health in 2022, I held an NSF Mathematical Sciences Postdoctoral Research Fellowship, during which I co-developed novel techniques for causal mediation analysis while serving concurrently as a core member of the COVID-19 Prevention Network’s biostatistics response team, contributing statistical methods and the design and implementation of an open-source software platform for the evaluation of candidate immune correlates of vaccine protection. In 2021, I obtained my PhD in biostatistics from UC Berkeley, for work on statistical and causal inference approaches to assessing the impacts of stochastic treatment regimes. In this period, I also co-founded and served as a core developer for the TLverse project, an open-source software ecosystem for targeted machine learning. Way back in 2015, I completed my undergraduate studies—also at UC Berkeley—in beautiful Berkeley, CA.
John Tukey once famously remarked that “the best thing about being a statistician is that you get to play in everyone’s backyard.” He got it right. My past collaborations have spanned diverse areas of the biomedical and public health sciences—from molecular toxicology and computational biology to environmental health and nutritional epidemiology. In the last few years, the rich statistical science problems that abound in the infectious disease sciences, especially in efforts to study investigational therapeutics and preventives, have captured my attention. My work in these areas has contributed novel methods and insights for identifying immune correlates of protection (or surrogate endpoints) in vaccine efficacy trials of HIV and COVID-19; for comparing the efficacy of therapeutics for MDR-TB in key populations and synthesizing evidence to support tailoring normative treatment guidance for people living with HIV; and for characterizing and shedding further light on Long COVID.
Despite well-meaning recommendations against doing so, I continue to try to write code for statistics research…collateral damage from this ill-advised pastime is logged on GitHub and visualized below.
Finally, here are a few reflections on science and statistics that I wish I’d come up with first.
“Far better an approximate answer to the right question, which is often vague, than the exact answer to the wrong question, which can always be made precise.” –John Tukey
“Although this may seem a paradox, all exact science is dominated by the idea of approximation.” –Bertrand Russell
“Everyone is sure of this [that errors are normally distributed]…since the experimentalists believe that it is a mathematical theorem, and the mathematicians that it is an experimentally determined fact.” –Henri Poincare
“The anarchy of guess and intuition has given way to a benevolent tyranny of statisticians.” –Donald Fredrickson
An abbreviated version of this biography, in third-person, may be found here.