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
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).
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
Predicting Treatment Futility in Refractory Diffuse Large B cell Lymphoma awarded by Leukemia & Lymphoma Society (Statistical Analyst). 2014 to 2015
Bayesian Methods for Assessing Gene by Environment Interactions awarded by National Institutes of Health (Principal Investigator). 2009 to 2015
Nonparametric Bayes Methods for Biomedical Studies awarded by National Institutes of Health (Principal Investigator). 2009 to 2015
Emergence of Cardiometabolic Risk Across the Lifecycle in China awarded by University of North Carolina - Chapel Hill (Principal Investigator). 2013 to 2014
Exome-wide screening for common mutations in lymphoma awarded by National Institutes of Health (Investigator). 2011 to 2013
Transfer and Active Learning for Intent Recognition awarded by Office of Naval Research (Investigator). 2008 to 2012
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
Ovaskainen, O, Tikhonov, G, Norberg, A, Guillaume Blanchet, F, Duan, L, Dunson, D, Roslin, T, and Abrego, N. "How to make more out of community data? A conceptual framework and its implementation as models and software." Ecology letters 20.5 (May 2017): 561-576. Full Text
Tikhonov, G, Abrego, N, Dunson, D, and Ovaskainen, O. "Using joint species distribution models for evaluating how species-to-species associations depend on the environmental context." Ed. D Warton. Methods in Ecology and Evolution 8.4 (April 2017): 443-452. 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
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
Lin, L, Rao, V, and Dunson, D. "Bayesian nonparametric inference on the Stiefel manifold." Statistica Sinica (2017). Full Text
Bhattacharya, A, Dunson, DB, Pati, D, and Pillai, NS. "Sub-optimality of some continuous shrinkage priors." Stochastic Processes and their Applications 126.12 (December 2016): 3828-3842. 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.
Wang, X, Peng, P, and Dunson, DB. "Median selection subset aggregation for parallel inference." January 1, 2014.
Johndrow, JE, Lum, K, and Dunson, DB. "Diagonal orthant multinomial probit models." January 1, 2013.
Banerjee, A, Murray, J, and Dunson, DB. "Bayesian learning of joint distributions of objects." January 1, 2013.
Fyshe, A, Fox, E, Dunson, D, and Mitchell, T. "Hierarchical latent dictionaries for models of brain activation." January 1, 2012.