David B. Dunson
Arts and Sciences Distinguished Professor of Statistical Science
Development of novel approaches for representing and analyzing complex data. A particular focus is on methods that incorporate geometric structure (both known and unknown) and on probabilistic approaches to characterize uncertainty. In addition, a big interest is in scalable algorithms and in developing approaches with provable guarantees.
This fundamental work is directly motivated by applications in biomedical research, network data analysis, neuroscience, genomics, ecology, and criminal justice.
New methods for quantitative modeling of protein-DNA interactions awarded by National Institutes of Health (Co Investigator). 2015 to 2020
An Integrated Nonparametric Bayesian and Deep Neural Network Framework for Biologically-Inspired Lifelong Learning awarded by Defense Advanced Research Projects Agency (Co Investigator). 2018 to 2020
BIGDATA:F: Scalable Bayes uncertainty quantification with guarantees awarded by National Science Foundation (Principal Investigator). 2015 to 2019
Network motifs in cortical computation awarded by University of California, Los Angeles (Principal Investigator). 2016 to 2019
Nonparametric Bayes Methods for Big Data in Neuroscience awarded by National Institutes of Health (Mentor). 2014 to 2019
Air Quality by Genomics Interactions in a Cardiovascular Disease Cohort awarded by (Co Investigator). 2014 to 2017
Bayesian learning for high-dimensional low sample size data awarded by Office of Naval Research (Principal Investigator). 2014 to 2017
LAS DO6: Theory and Methods for Coarsened Decision Making; Synthetic Data Release: The Tradeoff between Privacy and Utility of Big Data awarded by North Carolina State University (Co-Principal Investigator). 2016
NCRN-MN:Triangle Census Research Network awarded by National Science Foundation (Co Investigator). 2011 to 2016
Bayesian Methods for High-Dimensional Epidemiologic Data awarded by University of North Carolina - Chapel Hill (Principal Investigator). 2011 to 2016