David B Dunson

David B Dunson is a Professor in the Department of Statistical Science at Duke University, specializing in Bayesian statistics, high-dimensional data analysis, and computational methods. His research spans applications in genomics, environmental health, and machine learning, with a focus on nonparametric Bayes, factor models, and scalable inference algorithms. He has advised numerous PhD students, including Alexander Dombowsky and James Johndrow, contributing to advancements in statistical genetics and large-scale data problems.

His recent publications address interpretable network discovery, robust covariance modeling, and environmental mixtures analysis. Dunson's methodological work includes empirical Bayes approaches, constrained Bayesian inference, and novel algorithms for handling imbalanced data. These methods have theoretical foundations and practical implementations in R packages like infinitefactor and bfa.

• Key Research Areas:
  • Bayesian nonparametric modeling
  • High-dimensional factor analysis
  • Computational scalability in MCMC
  • Environmental exposure interactions