David B. Dunson
- Arts and Sciences Professor of Statistical Science
- Professor of Statistical Science
- Professor in the Department of Electrical and Computer Engineering (Secondary)
- Professor in the Department of Mathematics (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).
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
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
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
Lock, EF, and Dunson, DB. "Bayesian genome- and epigenome-wide association studies with gene level dependence." Biometrics (January 12, 2017). Full Text
Lin, L, Rao, V, and Dunson, D. "Bayesian nonparametric inference on the Stiefel manifold." Statistica Sinica (2017). Full Text
Kunihama, T, Herring, AH, Halpern, CT, and Dunson, DB. "Nonparametric Bayes modeling with sample survey weights." Statistics & Probability Letters 113 (June 2016): 41-48. Full Text
Rao, V, Lin, L, and Dunson, DB. "Data augmentation for models based on rejection sampling." Biometrika 103.2 (June 2016): 319-335.
Guhaniyogi, R, and Dunson, DB. "Compressed Gaussian process for manifold regression." Journal of Machine Learning Research 17 (May 1, 2016).
Kabisa, ST, Dunson, DB, and Morris, JS. "Online Variational Bayes Inference for High-Dimensional Correlated Data." Journal of Computational and Graphical Statistics 25.2 (April 2, 2016): 426-444. 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. "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, Y, and Dunson, D. "Probabilistic curve learning: Coulomb repulsion and the electrostatic Gaussian process." January 1, 2015.
Wang, X, Guo, F, Heller, KA, and Dunson, DB. "Parallelizing MCMC with random partition trees." 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
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.