# David B. Dunson

- Arts and Sciences Professor of Statistical Science
- Professor of Statistical Science
- Professor in the Department of Electrical and Computer Engineering (Secondary)
- Professor in the Department of Mathematics (Secondary)
- 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).

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

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

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

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

## Pages

Dunson, DB, Bhattacharya, A, and Griffin, JE. "Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels." *Bayesian Statistics 9.* January 19, 2012.
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Schaich Borg, J, Srivastava, S, Lin, L, Heffner, J, Dunson, D, Dzirasa, K, and de Lecea, L. "Rat intersubjective decisions are encoded by frequency-specific oscillatory contexts." *Brain and behavior* 7.6 (June 2017): e00710-.
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Zhu, B, and Dunson, DB. "Bayesian Functional Data Modeling for Heterogeneous Volatility." *Bayesian Analysis* 12.2 (June 2017): 335-350.
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Moffitt, AB, Ondrejka, SL, McKinney, M, Rempel, RE, Goodlad, JR, Teh, CH, Leppa, S, Mannisto, S, Kovanen, PE, Tse, E, Au-Yeung, RKH, Kwong, Y-L, Srivastava, G, Iqbal, J, Yu, J, Naresh, K, Villa, D, Gascoyne, RD, Said, J, Czader, MB, Chadburn, A, Richards, KL, Rajagopalan, D, Davis, NS, Smith, EC, Palus, BC, Tzeng, TJ, Healy, JA, Lugar, PL, Datta, J, Love, C, Levy, S, Dunson, DB, Zhuang, Y, Hsi, ED, and Dave, SS. "Enteropathy-associated T cell lymphoma subtypes are characterized by loss of function of SETD2." *The Journal of experimental medicine* 214.5 (May 2017): 1371-1386.
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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.
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Dunson, DB. "Toward Automated Prior Choice." *Statistical Science* 32.1 (February 2017): 41-43.
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Johndrow, JE, Bhattacharya, A, and Dunson, DB. "Tensor decompositions and sparse log-linear models." *The Annals of Statistics* 45.1 (February 2017): 1-38.
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Lock, EF, and Dunson, DB. "Bayesian genome- and epigenome-wide association studies with gene level dependence." *Biometrics* (January 12, 2017).
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Lin, L, Rao, V, and Dunson, D. "Bayesian nonparametric inference on the Stiefel manifold." *Statistica Sinica* (2017).
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Wang, L, Durante, D, Jung, RE, and Dunson, DB. "Bayesian network-response regression." *Bioinformatics (Oxford, England)* (January 2017).
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Datta, J, and Dunson, DB. "Bayesian inference on quasi-sparse count data." *Biometrika* 103.4 (December 2016): 971-983.
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## Pages

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.

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

Guo, F, and Dunson, DB. "Uncovering systematic bias in ratings across categories: A Bayesian approach." September 16, 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, Y, and Dunson, D. "Probabilistic curve learning: Coulomb repulsion and the electrostatic Gaussian process." 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

Durante, D, and Dunson, DB. "Bayesian logistic Gaussian process models for dynamic networks." January 1, 2014.