David B. Dunson
Arts and Sciences Professor of Statistical Science
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.
Pages
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.