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
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
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
Pages
Lin, L., et al. “Extrinsic Gaussian processes for regression and classification on manifolds.” Bayesian Analysis, vol. 14, no. 3, Jan. 2019, pp. 887–906. Scopus, doi:10.1214/18-BA1135. Full Text