David B. Dunson

David B. Dunson
  • Arts and Sciences Distinguished Professor of Statistical Science
  • Professor of Statistical Science
  • Professor in the Department of Mathematics (Secondary)
  • Faculty Network Member of the Duke Institute for Brain Sciences
External address: 218 Old Chemistry Bldg, Durham, NC 27708
Internal office address: Box 90251, Durham, NC 27708-0251
Phone: (919) 684-8025

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

A Planetary Inventory of Life - a New Synthesis Built on Big Data Combined with Novel Statistical Methods awarded by European Research Council (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

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

Scalable probabilistic inference for huge multi-domain graphs awarded by (Principal Investigator). 2017 to 2020

Predicting Performance from Network Data awarded by (Principal Investigator). 2016 to 2020

Probabilistic learning of structure in complex data awarded by Office of Naval Research (Principal Investigator). 2017 to 2020

Pages

Gelman, A., et al. Bayesian data analysis, third edition. 2013, pp. 1–646.

Dunson, D. B., et al. “Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels.” Bayesian Statistics 9, vol. 9780199694587, 2012. Scopus, doi:10.1093/acprof:oso/9780199694587.003.0005. Full Text

Weinberg, C. R., and D. B. Dunson. “Some issues in assessing human fertility.” Statistics in the 21st Century, 2001, pp. 42–49.

Tikhonov, Gleb, et al. “Computationally efficient joint species distribution modeling of big spatial data..” Ecology, Nov. 2019. Epmc, doi:10.1002/ecy.2929. Full Text

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

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

Johndrow, J. E., et al. “MCMC for Imbalanced Categorical Data.” Journal of the American Statistical Association, vol. 114, no. 527, July 2019, pp. 1394–403. Scopus, doi:10.1080/01621459.2018.1505626. 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

Wang, L., et al. “Symmetric Bilinear Regression for Signal Subgraph Estimation.” Ieee Transactions on Signal Processing, vol. 67, no. 7, Apr. 2019, pp. 1929–40. Scopus, doi:10.1109/TSP.2019.2899818. Full Text

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

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

Miller, Jeffrey W., and David B. Dunson. “Robust Bayesian inference via coarsening..” Journal of the American Statistical Association, vol. 114, no. 527, Jan. 2019, pp. 1113–25. Epmc, doi:10.1080/01621459.2018.1469995. Full Text

Pages

van den Boom, Willem, et al. “Effect of A1C and Glucose on Postoperative Mortality in Noncardiac and Cardiac Surgeries..” Diabetes Care, vol. 41, no. 4, 2018, pp. 782–88. Pubmed, doi:10.2337/dc17-2232. Full Text

Wang, X., et al. “No penalty no tears: Least squares in high-dimensional linear models.” 33rd International Conference on Machine Learning, Icml 2016, vol. 4, 2016, pp. 2685–706.

Wang, X., et al. “DECOrrelated feature space partitioning for distributed sparse regression.” Advances in Neural Information Processing Systems, 2016, pp. 802–10.

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.

Guo, F., and D. B. Dunson. “Uncovering systematic bias in ratings across categories: A Bayesian approach.” Recsys 2015  Proceedings of the 9th Acm Conference on Recommender Systems, 2015, pp. 317–20. Scopus, doi:10.1145/2792838.2799683. Full Text

Srivastava, S., et al. “WASP: Scalable Bayes via barycenters of subset posteriors.” Journal of Machine Learning Research, vol. 38, 2015, pp. 912–20.

Van Den Boom, W., et al. “Quantifying uncertainty in variable selection with arbitrary matrices.” 2015 Ieee 6th International Workshop on Computational Advances in Multi Sensor Adaptive Processing, Camsap 2015, 2015, pp. 385–88. Scopus, doi:10.1109/CAMSAP.2015.7383817. Full Text

Wang, X., et al. “On the consistency theory of high dimensional variable screening.” Advances in Neural Information Processing Systems, vol. 2015-January, 2015, pp. 2431–39.

Wang, X., et al. “Parallelizing MCMC with random partition trees.” Advances in Neural Information Processing Systems, vol. 2015-January, 2015, pp. 451–59.

Pages

Wang, L., et al. “Common and individual structure of brain networks.” Annals of Applied Statistics, vol. 13, no. 1, 1 Jan. 2019, pp. 85–112. Scopus, doi:10.1214/18-AOAS1193. Full Text

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