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. I received my PhD in Biostatistics from the University of Pennsylvania under the supervision of Jason Roy and Nandita Mitra. 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.
On this site you will find a link to my CV and a selection of some past and current research, talks, etc. If you are a ScM or PhD students at Brown University who is considering working with me, please see this page
I sometimes blog about statistics, Bayesian methods, computation/MCMC, and other things I happen to stumble upon during research.
- Bayesian nonparametrics
- Causal Inference
- Missing Data
- Sequential decision-making
- Oncology
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PhD in Biostatistics, 2021
University of Pennsylvania
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MS in Biostatistics, 2018
University of Pennsylvania
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BA in Quantitative Economics, 2013
Providence College
Projects
Collaborative Causal and Bayesian Modeling
Collaborative data analysis projects using Bayesian and/or Causal methods.
Non-parametric Bayes
Bayesian modeling - flexiblilty, uncertainty quantification, full posterior inference.
Bayesian Causal Inference
Shrinkage, partial pooling, nonparametrics, and sensitivity analysis via priors - just some of the value Bayesian modeling can add to causal inference.
ChiRP
R Package for Dirichlet Process Mixtures of zero-inflated, logistic, and linear regressions.
