News

• 4/10/2023: New working paper on arXiv developing semiparametric Bayesian framework for causal inference with recurrent event outcomes.
• Upcoming Summer 2023 talks: 5/18/2023 - IRSA; 8/5/2023 - JSM Toronto; 8/1/2023 - EcoStat Tokyo.
• 1/5/2023: I’ll be giving a talk at ICHPS on Bayesian methods for sequential treatment strategies.
• 12/15/2022: New paper developing a hierarchical Bayesian bootstrap for causal estimation.
• 11/15/2022: New working paper on arXiv developing semiparametric Bayesian methods for sequential treatment decisions.

Bio

I am currently an Assistant Professor of Biostatistics 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, nonparametric Bayesian modeling, and efficient computation to build systems for data-driven decision making.

Many of my motivating applications are in oncology - e.g. Bayesian nonparametric cost and cost-efficacy estimation for endometrial cancer treatments; Developing a Bayesian bootstrap procedure for assessing treatment effect heterogeneity of photon therapy across cancer types. 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.