David B. Dunson

David B. Dunson
  • Arts and Sciences Professor of Statistical Science
  • Professor of Statistical Science
  • Faculty Network Member of the Duke Institute for Brain Sciences
External address: 218 Old Chemistry Bldg, Durham, NC 27708
Internal office address: Box 90251, Durham, NC 27708-0251
Phone: (919) 684-8025

Development of Bayesian statistical methods and approaches for uncertainty quantification motivated by applications with complex and high-dimensional data.  A particular interest is in high-dimensional low sample size data in which it is necessary to incorporate dimensional reduction through carefully designed prior distributions and challenges arise in efficiently computing posterior approximations. Ongoing focus areas include new algorithms for approximating posterior distributions in big data settings, nonparametric Bayes probability modeling allowing for uncertainty in distributional assumptions, analysis of network data, incorporating physical and geometric prior knowledge in modeling and novel models for dimension reduction for "object data" (functions, tensors, shapes, etc).  Primary application areas include genomics, neurosciences, epidemiology, and reproductive studies but with much broader interests in developing new methods motivated by difficult applications (in art, music, radar, imaging processing, etc).

Education & Training
  • Ph.D., Emory University 1997

  • B.S., Pennsylvania State University 1994

Selected Grants

Reproducibility and Robustness of Dimensionality Reduction awarded by National Institutes of Health (Investigator). 2017 to 2022

Postdoctoral Training in Genomic Medicine Research awarded by National Institutes of Health (Mentor). 2017 to 2022

Structured nonparametric methods for mixtures of exposures awarded by National Institutes of Health (Principal Investigator). 2018 to 2022

Scalable probabilistic inference for huge multi-domain graphs awarded by Alibaba Innovative Research (Principal Investigator). 2017 to 2020

Probabilistic learning of structure in complex data awarded by Office of Naval Research (Principal Investigator). 2017 to 2020

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

Predicting Performance from Network Data awarded by U.S. Army Research Institute for the Behavioral and Social Sciences (Principal Investigator). 2016 to 2019

Network motifs in cortical computation awarded by University of California - Los Angeles (Principal Investigator). 2016 to 2019


Dunson, DB, Bhattacharya, A, and Griffin, JE. "Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels." Bayesian Statistics 9. January 19, 2012. Full Text

Shterev, ID, Dunson, DB, Chan, C, and Sempowski, GD. "Bayesian Multi-Plate High-Throughput Screening of Compounds." Scientific Reports 8.1 (June 22, 2018): 9551-null. Full Text

Johndrow, JE, Lum, K, and Dunson, DB. "Theoretical limits of microclustering for record linkage." Biometrika 105.2 (June 2018): 431-446. Full Text

Zhang, Z, Descoteaux, M, Zhang, J, Girard, G, Chamberland, M, Dunson, D, Srivastava, A, and Zhu, H. "Mapping population-based structural connectomes." Neuroimage 172 (May 2018): 130-145. Full Text

Sarkar, A, Chabout, J, Macopson, JJ, Jarvis, ED, and Dunson, DB. "Bayesian Semiparametric Mixed Effects Markov Models With Application to Vocalization Syntax." Journal of the American Statistical Association (January 19, 2018): 1-13. Full Text

Bertrán, MA, Martínez, NL, Wang, Y, Dunson, D, Sapiro, G, and Ringach, D. "Active learning of cortical connectivity from two-photon imaging data." Plos One 13.5 (January 2018): e0196527-null. Full Text

Dunson, DB. "Statistics in the big data era: Failures of the machine (Accepted)." Statistics and Probability Letters (January 1, 2018). Full Text

Wheeler, MW, Dunson, DB, and Herring, AH. "Bayesian Local Extremum Splines." Biometrika 104.4 (December 2017): 939-952.

Minsker, S, Srivastava, S, Lin, L, and Dunson, DB. "Robust and scalable bayes via a median of subset posterior measures." Journal of Machine Learning Research 18 (December 1, 2017): 1-40.

Wheeler, MW, Dunson, DB, and Herring, AH. "Bayesian local extremum splines." Biometrika 104.4 (December 1, 2017): 939-952.

Shang, Y, Dunson, D, and Song, J-S. "Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics." Operations Research 65.6 (December 2017): 1574-1588. Full Text


van den Boom, W, Schroeder, RA, Manning, MW, Setji, TL, Fiestan, G-O, and Dunson, DB. "Effect of A1C and Glucose on Postoperative Mortality in Noncardiac and Cardiac Surgeries." April 2018. Full Text

Wang, X, Dunson, D, and Leng, C. "No penalty no tears: Least squares in high-dimensional linear models." January 1, 2016.

Wang, X, Dunson, D, and Leng, C. "DECOrrelated feature space partitioning for distributed sparse regression." January 1, 2016.

Van Den Boom, W, Dunson, D, and Reeves, G. "Quantifying uncertainty in variable selection with arbitrary matrices." January 1, 2015. Full Text

Srivastava, S, Cevher, V, Tran-Dinh, Q, and Dunson, DB. "WASP: Scalable Bayes via barycenters of subset posteriors." January 1, 2015.

Wang, X, Leng, C, and Dunson, DB. "On the consistency theory of high dimensional variable screening." January 1, 2015.

Wang, X, Guo, F, Heller, KA, and Dunson, DB. "Parallelizing MCMC with random partition trees." January 1, 2015.

Yin, R, Dunson, D, Cornelis, B, Brown, B, Ocon, N, Daubechies, I, Yin, R, Dunson, D, Cornelis, B, Brown, B, Ocon, N, and Daubechies, I. "Digital cradle removal in X-ray images of art paintingsDigital cradle removal in X-ray images of art paintings (PublishedPublished)." January 28, 2014. Full Text


Wang, L, Zhengwu Zhang, , and Dunson, . "COMMON AND INDIVIDUAL STRUCTURE OF MULTIPLE NETWORKS." (Working Paper)

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