David B. Dunson

Arts and Sciences Professor of Statistical Science

External address: 
218 Old Chemistry Bldg, Durham, NC 27708
Internal office address: 
Box 90251, Durham, NC 27708-0251
(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

Postdoctoral training in genomic medicine research awarded by National Institutes of Health (Mentor). 2017 to 2022

New methods for quantitative modeling of protein-DNA interactions awarded by National Institutes of Health (Co Investigator). 2015 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

Nonparametric Bayes Methods for Big Data in Neuroscience awarded by National Institutes of Health (Mentor). 2014 to 2019

Bayesian learning for high-dimensional low sample size data awarded by Office of Naval Research (Principal Investigator). 2014 to 2017

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


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

McKinney, M, Moffitt, AB, Gaulard, P, Travert, M, De Leval, L, Nicolae, A, Raffeld, M, Jaffe, ES, Pittaluga, S, Xi, L, Heavican, T, Iqbal, J, Belhadj, K, Delfau-Larue, MH, Fataccioli, V, Czader, MB, Lossos, IS, Chapman-Fredricks, JR, Richards, KL, Fedoriw, Y, Ondrejka, SL, Hsi, ED, Low, L, Weisenburger, D, Chan, WC, Mehta-Shah, N, Horwitz, S, Bernal-Mizrachi, L, Flowers, CR, Beaven, AW, Parihar, M, Baseggio, L, Parrens, M, Moreau, A, Sujobert, P, Pilichowska, M, Evens, AM, and Chadburn, A et al. "The Genetic Basis of Hepatosplenic T-cell Lymphoma." Cancer discovery 7.4 (April 2017): 369-379. Full Text

Johndrow, JE, Bhattacharya, A, and Dunson, DB. "Tensor decompositions and sparse log-linear models." The Annals of Statistics 45.1 (February 2017): 1-38. Full Text

Dunson, DB. "Toward Automated Prior Choice." Statistical Science 32.1 (February 2017): 41-43. Full Text

Lin, L, Rao, V, and Dunson, D. "Bayesian nonparametric inference on the Stiefel manifold." Statistica Sinica (2017). Full Text

Lin, L, St. Thomas, B, Zhu, H, and Dunson, DB. "Extrinsic Local Regression on Manifold-Valued Data." Journal of the American Statistical Association (July 20, 2016): 1-13. Full Text

Kunihama, T, Herring, AH, Halpern, CT, and Dunson, DB. "Nonparametric Bayes modeling with sample survey weights." Statistics & Probability Letters 113 (June 2016): 41-48. Full Text

Rao, V, Lin, L, and Dunson, DB. "Data augmentation for models based on rejection sampling." Biometrika 103.2 (June 2016): 319-335.

Guhaniyogi, R, and Dunson, DB. "Compressed Gaussian process for manifold regression." Journal of Machine Learning Research 17 (May 1, 2016).

Kabisa, ST, Dunson, DB, and Morris, JS. "Online Variational Bayes Inference for High-Dimensional Correlated Data." Journal of Computational and Graphical Statistics 25.2 (April 2, 2016): 426-444. Full Text


Van Den Boom, W, Dunson, D, and Reeves, G. "Quantifying uncertainty in variable selection with arbitrary matrices." January 14, 2016. Full Text

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

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

Rai, P, Wang, Y, Guo, S, Chen, G, Dunson, D, and Carin, L. "Scalable bayesian low-rank decomposition of incomplete multiway tensors." January 1, 2014.

Minsker, S, Srivastava, S, Lin, L, and Dunson, DB. "Scalable and robust Bayesian inference via the median posterior." January 1, 2014.