Overview
Welcome to my website! My name is Joe Feldman and I am a postdoctoral associate in the Department of Statistical Science at Duke University where I am supervised by Jerry Reiter. I completed my Ph.D. in May of 2023 at Rice University in the Department of Statistics, and my adviser was Dan Kowal.
I am interested in developing Bayesian methodology for complex and high-dimensional data sets, with applications into public health, sociology, and economics. Broadly, my research has three themes:
Analysis of Missing Data: Missing values are commonplace in modern data sets, especially when they are built by linking information across multiple sources. We are broadly interested in developing flexible, Bayesian joint models for imputation of missing values which are compatible with mixed data types and potentially nonignorable missing values.
Data Privacy: We aim to make information obtained from confidential data sources accessible to the broader public without jeopardizing privacy. One way to accomplish this is through data synthesis, which learns a probabilistic, generative model on confidential data, and simulates from that model to create synthetic data. Ideally, the model is flexible to capture univariate and multivariate features of the data, while the synthetic data maintains no correspondence with observations in the original data set
Interpretable Machine Learning: We leverage Bayesian decision theory to provide simple, “near optimal” summaries of black-box predictive models. We can also use these techniques to perform high-dimensional variable selection, which enables inference into the relationships between covariates and response.
