I am currently an Assistant Professor of Biostaistics (tenure-track) at Brown University’s Department of Biostatistics. My methodological research centers around developing Bayesian methods for flexible causal effect estimation with observational data. These methods blend principled causal reasoning, nonparmetric Bayesian modeling, and efficient computation to build systems for data-driven decision making.
Most of my motivating applications have been in oncology - e.g. Bayesian nonparametric cost and cost/efficacy of estimation for endometrial cancer treatments; assessing treatment effect heterogeneity of photon therapy across cancer types. A related interest, that is inseparable from this work, is in methods for efficient MCMC computation. My current research is partially funded by a PCORI grant and focuses on developing Bayesian semiparametric methods for estimating and optimizing effects of sequential treatment strategies with applications in leukemia.
I received my PhD in Biostatistics from the University of Pennsylvania under the supervision of Jason Roy and Nandita Mitra. Here you will find a link to my CV and a selection of some past and current research, talks, etc. I sometimes blog about statistics, Bayesian methods, computation/MCMC, and other things I happen to stumble upon during research.
PhD in Biostatistics, 2021
University of Pennsylvania
MS in Biostatistics, 2018
University of Pennsylvania
BA in Quantitative Economics, 2013
Providence College
Collaborative data analysis projects using Bayesian and/or Causal methods.
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.
R Package for Dirichlet Process Mixtures of zero-inflated, logistic, and linear regressions.