I am a 5th year biostatistics PhD candidate at the University of Pennsylvania, an Associate Fellow at the LDI, and a member of the Center for Causal Inference.
My methodological research focuses on developing nonparametric Bayesian methods for estimating causal effects using complex, observational data. This first requires rigorous causal reasoning to both formulate estimands with precise causal interpretations and determine the conditions under which they are estimable with observed data. Flexible Bayesian estimation follows from constructing models with a high-dimensional set of parameters that grows with the sample size. The nonparametric Bayes procedures I develop leverage special priors over these high-dimensional spaces to do posterior causal estimation. Crucial for implementation is my work emphasizing efficient computation with these models.
Here you will find a link to my full CV and a selection of some past and current research work. I sometimes blog about statistics, bayesian methods, computation/MCMC, and other things I happen to stumble upon during research. These posts are syndicated on R-bloggers.
PhD, Biostatistics, 2021 (exp.)
University of Pennsylvania
MS, Biostatistics, 2018
University of Pennsylvania
BA, Quantitative Economics, 2013
Providence College
Bayesian modeling - flexiblilty, uncertainty quantification, full posterior inference.
Shrinkage, partial pooling, nonparametrics, and sensitivity analysis via priors - just some of the value Bayesian modeling can add to causal inference.
Collaborative projects with interesting causal and Bayesian projects.
R Package for Dirichlet Process Mixtures of zero-inflated, logistic, and linear regressions.