Biography
I am a PhD candidate in Biostatistics with a designated emphasis in Computational and Genomic Biology at UC Berkeley. I am co-advised by Mark van der Laan and Alan Hubbard. I am also affiliated with the Center for Computational Biology and am a former fellow of the Biomedical Big Data training program. My research interests are a bit eclectic, spanning causal inference, nonparametric (data-adaptive) inference and machine learning, targeted minimum loss-based estimation, survival analysis and censored data models, statistical computing, reproducible research, and computational biology. My work is primarily driven by the nonparametric estimation of quantities from causal inference, motivated by and applied to pressing scientific and policy problems. Substantive applications have most recently included vaccine efficacy trials, precision medicine, high-dimensional biology, epidemiology, and algorithmic fairness. I am also passionate about software development for applied statistics, including software design, automated testing, and reproducible coding practices.
Interests
- Causal Inference
- Nonparametric Inference and Machine Learning
- Targeted Minimum Loss-Based Estimation
- Semiparametric Theory
- Survival Analysis and Censored Data Models
- Statistical Computing and Reproducible Research
- Computational Biology
Education
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PhD in Biostatistics, with a designated emphasis in Computational & Genomic Biology, 2016-present
University of California, Berkeley
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MA in Biostatistics, 2017
University of California, Berkeley
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BA with a triple major in Molecular & Cell Biology, Psychology, and Public Health, 2015
University of California, Berkeley