# David B. Dunson

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

### 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

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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Reddy, A, Zhang, J, Davis, NS, Moffitt, AB, Love, CL, Waldrop, A, Leppa, S, Pasanen, A, Meriranta, L, Karjalainen-Lindsberg, M-L, Nørgaard, P, Pedersen, M, Gang, AO, Høgdall, E, Heavican, TB, Lone, W, Iqbal, J, Qin, Q, Li, G, Kim, SY, Healy, J, Richards, KL, Fedoriw, Y, Bernal-Mizrachi, L, Koff, JL, Staton, AD, Flowers, CR, Paltiel, O, Goldschmidt, N, Calaminici, M, Clear, A, Gribben, J, Nguyen, E, Czader, MB, Ondrejka, SL, Collie, A, Hsi, ED, Tse, E, Au-Yeung, RKH, Kwong, Y-L, and Srivastava, G et al. "Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma." *Cell* 171.2 (October 2017): 481-494.e15.
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Li, C, Srivastava, S, and Dunson, DB. "Simple, scalable and accurate posterior interval estimation." *BIOMETRIKA* 104.3 (September 2017): 665-680.
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Lock, EF, and Dunson, DB. "Bayesian genome- and epigenome-wide association studies with gene level dependence." *Biometrics* 73.3 (September 2017): 1018-1028.
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Srivastava, S, Engelhardt, BE, and Dunson, DB. "Expandable factor analysis." *BIOMETRIKA* 104.3 (September 2017): 649-663.
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Guhaniyogi, R, Qamar, S, and Dunson, DB. "Bayesian tensor regression." *Journal of Machine Learning Research* 18 (August 1, 2017): 1-31.

Lin, L, St. Thomas, B, Zhu, H, and Dunson, DB. "Extrinsic Local Regression on Manifold-Valued Data." *Journal of the American Statistical Association* 112.519 (July 3, 2017): 1261-1273.
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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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Wang, L, Durante, D, Jung, RE, and Dunson, DB. "Bayesian network-response regression." *Bioinformatics (Oxford, England)* 33.12 (June 2017): 1859-1866.
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Ovaskainen, O, Tikhonov, G, Dunson, D, Grøtan, V, Engen, S, Sæther, B-E, and Abrego, N. "How are species interactions structured in species-rich communities? A new method for analysing time-series data." *Proceedings of the Royal Society B: Biological Sciences* 284.1855 (May 31, 2017): 20170768-20170768.
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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

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

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.

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.

## Pages

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