Biography
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.
Interests
- causal machine learning and model-free causal inference
- non/semi-parametric inference and assumption-lean methods
- high-dimensional inference and bias-correction techniques
- nonparametric estimation and statistical machine learning
- statistical computing and reproducible data science
Education
-
PhD in Biostatistics (designated emphasis in Computational & Genomic Biology), 2021
University of California, Berkeley
-
MA in Biostatistics, 2017
University of California, Berkeley
-
BA with a triple major in Molecular & Cell Biology (em. Neurobiology), Psychology, and Public Health, 2015
University of California, Berkeley