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David B. Dunson CV

Arts and Sciences Distinguished Professor of Statistical Science
Statistical Science
Box 90251, Durham, NC 27708-0251
218 Old Chemistry Bldg, Durham, NC 27708
CV

Research Interests


Bayesian methods for high-dimensional & complex data

Network data analysis 

Scalable algorithms with provable guarantees 

Methods for spatial & dynamic data

Statistical imaging 

Interpretable machine learning & artificial intelligence 

Applications in ecology/biodiversity, neuroscience & environmental health 

Selected Grants


Duke University Program in Environmental Health

Inst. Training Prgm or CMEMentor · Awarded by National Institutes of Health · 2019 - 2029

Improving inferences on health effects of chemical exposures

ResearchPrincipal Investigator · Awarded by National Institute of Environmental Health Sciences · 2023 - 2028

R01: Genetic Origins of Adverse Outcomes in African Americans with Lymphoma

ResearchCo Investigator · Awarded by National Institutes of Health · 2023 - 2028

NSF-AoF: III: Small: Autonomous biodiversity monitoring through wireless communication technologies and artificial intelligence

ResearchPrincipal Investigator · Awarded by National Science Foundation · 2025 - 2027

Clinical and Genetic of Monomorphic Epitheliotropic Intestinal T Cell Lymphoma

ResearchCo Investigator · Awarded by National Cancer Institute · 2023 - 2027

Graphical modeling of high-dimensional tabular data

ResearchPrincipal Investigator · Awarded by Office of Naval Research · 2024 - 2027

A Planetary Inventory of Life - a New Synthesis Built on Big Data Combined with Novel Statistical Methods

ResearchPrincipal Investigator · Awarded by European Research Council · 2020 - 2027

Science-Integrated Predictive modeLing (SCINPL): a novel framework for scalable and interpretable predictive scientific computing

ResearchCo-Principal Investigator · Awarded by National Science Foundation · 2022 - 2025

Calibrated uncertainty quantification in statistical learning

ResearchPrincipal Investigator · Awarded by Office of Naval Research · 2021 - 2024

HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms

ResearchSenior Investigator · Awarded by National Science Foundation · 2019 - 2023

Reproducibility and Robustness of Dimensionality Reduction

ResearchInvestigator · Awarded by National Institutes of Health · 2017 - 2023

Postdoctoral Training in Genomic Medicine Research

Inst. Training Prgm or CMEMentor · Awarded by National Institutes of Health · 2017 - 2023

Structured nonparametric methods for mixtures of exposures

ResearchPrincipal Investigator · Awarded by National Institutes of Health · 2018 - 2023

CRCNS: Geometry-based Brain Connectome Analysis

ResearchPrincipal Investigator · Awarded by National Institutes of Health · 2018 - 2022

An Integrated Nonparametric Bayesian and Deep Neural Network Framework for Biologically-Inspired Lifelong Learning

ResearchCo Investigator · Awarded by Defense Advanced Research Projects Agency · 2018 - 2022

Probabilistic learning of structure in complex data

ResearchPrincipal Investigator · Awarded by Office of Naval Research · 2017 - 2021

Scalable probabilistic inference for huge multi-domain graphs

ResearchPrincipal Investigator · Awarded by Alibaba Innovative Research · 2017 - 2021

BIGDATA:F: Scalable Bayes uncertainty quantification with guarantees

ResearchPrincipal Investigator · Awarded by National Science Foundation · 2015 - 2020

Predicting Performance from Network Data

ResearchPrincipal Investigator · Awarded by U.S. Army Research Institute for the Behavioral and Social Sciences · 2016 - 2020

New methods for quantitative modeling of protein-DNA interactions

ResearchCo Investigator · Awarded by National Institutes of Health · 2015 - 2020

Network motifs in cortical computation

ResearchPrincipal Investigator · Awarded by University of California - Los Angeles · 2016 - 2019

Nonparametric Bayes Methods for Big Data in Neuroscience

ResearchMentor · Awarded by National Institutes of Health · 2014 - 2019

Air Quality by Genomics Interactions in a Cardiovascular Disease Cohort

ResearchCo Investigator · Awarded by Health Effects Institute · 2014 - 2017

Bayesian learning for high-dimensional low sample size data

ResearchPrincipal Investigator · Awarded by Office of Naval Research · 2014 - 2017

LAS DO6: Theory and Methods for Coarsened Decision Making; Synthetic Data Release: The Tradeoff between Privacy and Utility of Big Data

ResearchCo-Principal Investigator · Awarded by North Carolina State University · 2016 - 2016

NCRN-MN:Triangle Census Research Network

ResearchCo Investigator · Awarded by National Science Foundation · 2011 - 2016

Bayesian Methods for High-Dimensional Epidemiologic Data

ResearchPrincipal Investigator · Awarded by University of North Carolina - Chapel Hill · 2011 - 2016

Predicting Treatment Futility in Refractory Diffuse Large B cell Lymphoma

ResearchStatistical Analyst · Awarded by Leukemia & Lymphoma Society · 2014 - 2015

Bayesian Methods for Assessing Gene by Environment Interactions

ResearchPrincipal Investigator · Awarded by National Institutes of Health · 2009 - 2015

Nonparametric Bayes Methods for Biomedical Studies

ResearchPrincipal Investigator · Awarded by National Institutes of Health · 2009 - 2015

Emergence of Cardiometabolic Risk Across the Lifecycle in China

ResearchPrincipal Investigator · Awarded by University of North Carolina - Chapel Hill · 2013 - 2014

Exome-wide screening for common mutations in lymphoma

ResearchInvestigator · Awarded by National Institutes of Health · 2011 - 2013

Transfer and Active Learning for Intent Recognition

ResearchInvestigator · Awarded by Office of Naval Research · 2008 - 2012

External Relationships


  • Chapman Hall/CRC Press
  • Merck Research Laboratories

This faculty member (or a member of their immediate family) has reported outside activities with the companies, institutions, or organizations listed above. This information is available to institutional leadership and, when appropriate, management plans are in place to address potential conflicts of interest.