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).
Li, D, Longnecker, MP, and Dunson, DB. "Lipid adjustment for chemical exposures: accounting for concomitant variables." Epidemiology 24.6 (November 2013): 921-928. Full Text
Zhu, B, Ashley-Koch, AE, and Dunson, DB. "Generalized admixture mapping for complex traits. (Published online)" G3 (Bethesda) 3.7 (July 8, 2013): 1165-1175. Full Text Open Access Copy
Murray, JS, Dunson, DB, Carin, L, and Lucas, JE. "Bayesian Gaussian Copula Factor Models for Mixed Data." Journal of the American Statistical Association 108.502 (June 2013): 656-665. Full Text Open Access Copy
Zhang, J, Grubor, V, Love, CL, Banerjee, A, Richards, KL, Mieczkowski, PA, Dunphy, C, Choi, W, Au, WY, Srivastava, G, Lugar, PL, Rizzieri, DA, Lagoo, AS, Bernal-Mizrachi, L, Mann, KP, Flowers, C, Naresh, K, Evens, A, Gordon, LI, Czader, M, Gill, JI, Hsi, ED, Liu, Q, Fan, A, Walsh, K, Jima, D, Smith, LL, Johnson, AJ, Byrd, JC, Luftig, MA, Ni, T, Zhu, J, Chadburn, A, Levy, S, Dunson, D, and Dave, SS. "Genetic heterogeneity of diffuse large B-cell lymphoma." Proc Natl Acad Sci U S A 110.4 (January 22, 2013): 1398-1403. Full Text
Chen, B, Polatkan, G, Sapiro, G, Blei, D, Dunson, D, and Carin, L. "Deep Learning with Hierarchical Convolutional Factor Analysis." IEEE Trans Pattern Anal Mach Intell (January 9, 2013).
Petralia, F, Vogelstein, J, and Dunson, DB. "Multiscale dictionary learning for estimating conditional distributions." Advances in Neural Information Processing Systems (January 1, 2013). Open Access Copy
Durante, D, Scarpa, B, and Dunson, DB. "Locally adaptive bayesian multivariate time series." Advances in Neural Information Processing Systems (January 1, 2013).
Kunihama, T, and Dunson, DB. "Bayesian modeling of temporal dependence in large sparse contingency tables." Journal of the American Statistical Association 108.504 (January 2013): 1324-1338. Full Text
Zhu, B, and Dunson, DB. "Locally Adaptive Bayes Nonparametric Regression via Nested Gaussian Processes." Journal of the American Statistical Association 108.504 (January 2013). Full Text