David B. Dunson

David B. Dunson

Arts and Sciences Professor of Statistical Science

External address: 
218 Old Chemistry Bldg, Durham, NC 27708
Internal office address: 
Box 90251, Durham, NC 27708-0251
Phone: 
(919) 684-8025

Overview

Development of novel approaches for representing and analyzing complex data.  A particular focus is on methods that incorporate geometric structure (both known and unknown) and on probabilistic approaches to characterize uncertainty.  In addition, a big interest is in scalable algorithms and in developing approaches with provable guarantees.

This fundamental work is directly motivated by applications in biomedical research, network data analysis, neuroscience, genomics, ecology, and criminal justice.   

Education & Training

  • Ph.D., Emory University 1997

  • B.S., Pennsylvania State University 1994

Selected Grants

Duke University Program in Environmental Health awarded by National Institutes of Health (Mentor). 2013 to 2024

Reproducibility and Robustness of Dimensionality Reduction awarded by National Institutes of Health (Investigator). 2017 to 2022

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

CRCNS: Geometry-based Brain Connectome Analysis awarded by National Institutes of Health (Principal Investigator). 2018 to 2021

CRCNS: Geometry-based Brain Connectome Analysis awarded by National Institutes of Health (Principal Investigator). 2018 to 2021

Scalable probabilistic inference for huge multi-domain graphs awarded by (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

Pages

Li, Cheng, et al. “On posterior consistency of tail index for Bayesian kernel mixture models.” Bernoulli, vol. 25, no. 3, Bernoulli Society for Mathematical Statistics and Probability, Aug. 2019, pp. 1999–2028. Crossref, doi:10.3150/18-bej1043. Full Text

Niu, M., et al. “Intrinsic Gaussian processes on complex constrained domains.” Journal of the Royal Statistical Society. Series B: Statistical Methodology, vol. 81, no. 3, July 2019, pp. 603–27. Scopus, doi:10.1111/rssb.12320. Full Text

Zhang, Zhengwu, et al. “Tensor network factorizations: Relationships between brain structural connectomes and traits..” Neuroimage, vol. 197, Apr. 2019, pp. 330–43. Epmc, doi:10.1016/j.neuroimage.2019.04.027. Full Text

Norberg, A., et al. “A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels.” Ecological Monographs, Jan. 2019. Scopus, doi:10.1002/ecm.1370. Full Text

Li, M., and D. B. Dunson. “Comparing and Weighting Imperfect Models Using D-Probabilities.” Journal of the American Statistical Association, Jan. 2019. Scopus, doi:10.1080/01621459.2019.1611140. Full Text

Zhang, Z., et al. “Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions.” Journal of the American Statistical Association, Jan. 2019. Scopus, doi:10.1080/01621459.2019.1574582. Full Text

Rao, V., et al. “Bayesian inference for Matérn repulsive processes.” Journal of the Royal Statistical Society. Series B: Statistical Methodology, vol. 79, no. 3, June 2017, pp. 877–97. Scopus, doi:10.1111/rssb.12198. Full Text

Li, Didong, and David Dunson. Classification via local manifold approximation.

Wang, Y., et al. “Scalable geometric density estimation.” Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Aistats 2016, 2016, pp. 857–65.

Han, S., et al. “Variational Gaussian copula inference.” Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Aistats 2016, 2016, pp. 829–38.