David B. Dunson
Arts and Sciences Distinguished 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.
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Selected Grants
A Planetary Inventory of Life - a New Synthesis Built on Big Data Combined with Novel Statistical Methods awarded by (Principal Investigator). 2020 to 2026
Duke University Program in Environmental Health awarded by National Institutes of Health (Mentor). 2013 to 2024
HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms awarded by National Science Foundation (Senior Investigator). 2019 to 2022
Reproducibility and Robustness of Dimensionality Reduction awarded by National Institutes of Health (Investigator). 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
Postdoctoral Training in Genomic Medicine Research awarded by National Institutes of Health (Mentor). 2017 to 2021
Scalable probabilistic inference for huge multi-domain graphs awarded by (Principal Investigator). 2017 to 2020
BIGDATA:F: Scalable Bayes uncertainty quantification with guarantees awarded by National Science Foundation (Principal Investigator). 2015 to 2020
Predicting Performance from Network Data awarded by (Principal Investigator). 2016 to 2020
Pages
Dunson, D. B., and J. E. Johndrow. “The Hastings algorithm at fifty.” Biometrika, vol. 107, no. 1, Mar. 2020, pp. 1–23. Scopus, doi:10.1093/biomet/asz066. Full Text
Duan, Leo L., et al. “Bayesian constraint relaxation.” Biometrika, vol. 107, no. 1, Mar. 2020, pp. 191–204. Epmc, doi:10.1093/biomet/asz069. Full Text
Tikhonov, Gleb, et al. “Computationally efficient joint species distribution modeling of big spatial data.” Ecology, vol. 101, no. 2, Feb. 2020, p. e02929. Epmc, doi:10.1002/ecy.2929. Full Text
Jauch, M., et al. “Random orthogonal matrices and the Cayley transform.” Bernoulli, vol. 26, no. 2, Jan. 2020, pp. 1560–86. Scopus, doi:10.3150/19-BEJ1176. Full Text
Ferrari, F., and D. B. Dunson. “Bayesian Factor Analysis for Inference on Interactions.” Journal of the American Statistical Association, Jan. 2020. Scopus, doi:10.1080/01621459.2020.1745813. Full Text
Dunson, D., and T. Papamarkou. “Discussion.” International Statistical Review, Jan. 2020. Scopus, doi:10.1111/insr.12375. Full Text
Camerlenghi, F., et al. “Latent nested nonparametric priors (with discussion).” Bayesian Analysis, vol. 14, no. 4, Dec. 2019, pp. 1303–56. Scopus, doi:10.1214/19-BA1169_1. Full Text
Chae, M., et al. “Bayesian sparse linear regression with unknown symmetric error.” Information and Inference, vol. 8, no. 3, Jan. 2019, pp. 621–53. Scopus, doi:10.1093/imaiai/iay022. Full Text
Mukhopadhyay, M., and D. B. Dunson. “Targeted Random Projection for Prediction From High-Dimensional Features.” Journal of the American Statistical Association, Jan. 2019. Scopus, doi:10.1080/01621459.2019.1677240. Full Text
Badea, Alexandra, et al. “Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer's Disease.” Front Neuroinform, vol. 13, 2019, p. 72. Pubmed, doi:10.3389/fninf.2019.00072. Full Text
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Thai, D. H., et al. “Locally convex kernel mixtures: Bayesian subspace learning.” Proceedings 18th Ieee International Conference on Machine Learning and Applications, Icmla 2019, 2019, pp. 272–75. Scopus, doi:10.1109/ICMLA.2019.00051. Full Text