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).
Nonparametric Bayes Methods for Big Data in Neuroscience awarded by National Institutes of Health (Mentor). 2014 to 2019
Air Quality by Genomics Interactions in a Cardiovascular Disease Cohort awarded by Health Effects Institute (Co Investigator). 2014 to 2017
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
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
Guhaniyogi, R, Qamar, S, and Dunson, DB. "Bayesian tensor regression." Journal of Machine Learning Research 18 (August 1, 2017): 1-31.
Zhu, B, and Dunson, DB. "Bayesian Functional Data Modeling for Heterogeneous Volatility." Bayesian Analysis 12.2 (June 2017): 335-350. 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
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, 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. Biological sciences 284.1855 (May 2017). 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
Durante, D, and Dunson, DB. "Bayesian logistic Gaussian process models for dynamic networks." January 1, 2014.
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.