News

• 12/11/2023: New paper published in Biostatistics on semiparametric Bayesian methods estimating causal effects of dynamic treatment rules. arxiv version here.
• 11/28/2023: Happy to announce a PCORI award of ~$1 million funding my work on Bayesian Machine Learning for Causal Inference!
• 10/20/2023: New working paper on arXiv developing causalBETA R package for Bayesian Semiparametric causal inference with survival outcomes.
• 4/10/2023: New working paper on arXiv developing semiparametric Bayesian framework for causal inference with recurrent event outcomes.

Bio

I am currently an Assistant Professor of Biostatistics at Brown University. My methodological research centers around developing Bayesian nonparametric methods for causal estimation, with a focus on analyzing sequential treatments with incomplete information. These methods blend principled causal reasoning, nonparametric Bayesian modeling, and efficient computation to build systems for data-driven decision making.

Many of my motivating applications are in oncology. Some current work is partially funded by a PCORI contract and focuses on developing Bayesian semiparametric methods for estimating (and optimizing) effects of sequential treatment strategies in acute myeloid leukemia. I have recently been awarded another PCORI contract to develop Bayesian nonparametric methods for causal estimation with incomplete covariate information.

I received my PhD in Biostatistics from the University of Pennsylvania under the supervision of Jason Roy and Nandita Mitra. On this site 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.