David B. Dunson
Arts and Sciences Professor of Statistical Science
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
Li, C, Srivastava, S, and Dunson, DB. "Simple, scalable and accurate posterior interval estimation." BIOMETRIKA 104.3 (September 2017): 665-680. Full Text
Lock, EF, and Dunson, DB. "Bayesian genome- and epigenome-wide association studies with gene level dependence." Biometrics 73.3 (September 2017): 1018-1028. Full Text
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-. Full Text Open Access Copy
Zhu, B, and Dunson, DB. "Bayesian Functional Data Modeling for Heterogeneous Volatility." Bayesian Analysis 12.2 (June 2017): 335-350. Full Text
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. Full Text
Durante, D, Paganin, S, Scarpa, B, and Dunson, DB. "Bayesian modelling of networks in complex business intelligence problems." Journal of the Royal Statistical Society: Series C (Applied Statistics) 66.3 (April 2017): 555-580. 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
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. "DECOrrelated feature space partitioning for distributed sparse regression." January 1, 2016.
Wang, X, Dunson, D, and Leng, C. "No penalty no tears: Least squares in high-dimensional linear models." 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, X, Guo, F, Heller, KA, and Dunson, DB. "Parallelizing MCMC with random partition trees." 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
Durante, D, and Dunson, DB. "Bayesian logistic Gaussian process models for dynamic networks." January 1, 2014.