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).
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
Structured nonparametric methods for mixtures of exposures awarded by National Institutes of Health (Principal Investigator). 2018 to 2022
Scalable probabilistic inference for huge multi-domain graphs awarded by Alibaba Innovative Research (Principal Investigator). 2017 to 2020
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
An Integrated Nonparametric Bayesian and Deep Neural Network Framework for Biologically-Inspired Lifelong Learning awarded by Defense Advanced Research Projects Agency (Co Investigator). 2018 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
Wheeler, MW, Dunson, DB, and Herring, AH. "Bayesian Local Extremum Splines." Biometrika 104.4 (December 2017): 939-952.
Minsker, S, Srivastava, S, Lin, L, and Dunson, DB. "Robust and scalable bayes via a median of subset posterior measures." Journal of Machine Learning Research 18 (December 1, 2017): 1-40.
Wheeler, MW, Dunson, DB, and Herring, AH. "Bayesian local extremum splines." Biometrika 104.4 (December 1, 2017): 939-952.
Durante, D, Dunson, DB, and Vogelstein, JT. "Rejoinder: Nonparametric Bayes Modeling of Populations of Networks." Journal of the American Statistical Association 112.520 (October 2, 2017): 1547-1552. Full Text
Reddy, A, Zhang, J, Davis, NS, Moffitt, AB, Love, CL, Waldrop, A, Leppa, S, Pasanen, A, Meriranta, L, Karjalainen-Lindsberg, M-L, Nørgaard, P, Pedersen, M, Gang, AO, Høgdall, E, Heavican, TB, Lone, W, Iqbal, J, Qin, Q, Li, G, Kim, SY, Healy, J, Richards, KL, Fedoriw, Y, Bernal-Mizrachi, L, Koff, JL, Staton, AD, Flowers, CR, Paltiel, O, Goldschmidt, N, Calaminici, M, Clear, A, Gribben, J, Nguyen, E, Czader, MB, Ondrejka, SL, Collie, A, Hsi, ED, Tse, E, Au-Yeung, RKH, Kwong, Y-L, and Srivastava, G et al. "Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma." Cell 171.2 (October 2017): 481-494.e15. Full Text
Li, C, Srivastava, S, and Dunson, DB. "Simple, scalable and accurate posterior interval estimation." Biometrika 104.3 (September 1, 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
Guhaniyogi, R, Qamar, S, and Dunson, DB. "Bayesian tensor regression." Journal of Machine Learning Research 18 (August 1, 2017): 1-31.
Shang, Y, Dunson, D, and Song, J-S. "Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics." Operations Research (June 23, 2017). Full Text
van den Boom, W, Schroeder, RA, Manning, MW, Setji, TL, Fiestan, G-O, and Dunson, DB. "Effect of A1C and Glucose on Postoperative Mortality in Noncardiac and Cardiac Surgeries." February 13, 2018. 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
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.
Van Den Boom, W, Dunson, D, and Reeves, G. "Quantifying uncertainty in variable selection with arbitrary matrices." January 1, 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.
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