David B. Dunson

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

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 Alibaba Innovative Research (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

An Integrated Nonparametric Bayesian and Deep Neural Network Framework for Biologically-Inspired Lifelong Learning awarded by Defense Advanced Research Projects Agency (Co Investigator). 2018 to 2020

BIGDATA:F: Scalable Bayes uncertainty quantification with guarantees awarded by National Science Foundation (Principal Investigator). 2015 to 2019

Predicting Performance from Network Data awarded by U.S. Army Research Institute for the Behavioral and Social Sciences (Principal Investigator). 2016 to 2019

Pages

Gelman, A, Carlin, JB, Stern, HS, Dunson, DB, Vehtari, A, and Rubin, DB. Bayesian data analysis, third edition. January 1, 2013.

Weinberg, CR, and Dunson, DB. "Some issues in assessing human fertility." Statistics in the 21st Century. January 1, 2001. 42-49.

Canale, A, Durante, D, and Dunson, DB. "Convex mixture regression for quantitative risk assessment." Biometrics 74.4 (December 2018): 1331-1340. Full Text

Zhao, S, Engelhardt, BE, Mukherjee, S, and Dunson, DB. "Fast Moment Estimation for Generalized Latent Dirichlet Models." Journal of the American Statistical Association 113.524 (October 2, 2018): 1528-1540. Full Text

Duan, LL, Johndrow, JE, and Dunson, DB. "Scaling up data augmentation MCMC via calibration." Journal of Machine Learning Research 19 (October 1, 2018).

Srivastava, S, Li, C, and Dunson, DB. "Scalable Bayes via barycenter in Wasserstein space." Journal of Machine Learning Research 19 (August 1, 2018): 1-35.

Guhaniyogi, R, Qamar, S, and Dunson, DB. "Bayesian Conditional Density Filtering." Journal of Computational and Graphical Statistics 27.3 (July 3, 2018): 657-672. Full Text

van den Boom, W, Mao, C, Schroeder, RA, and Dunson, DB. "Extrema-weighted feature extraction for functional data." Bioinformatics (Oxford, England) 34.14 (July 2018): 2457-2464. Full Text

Miller, JW, and Dunson, DB. "Robust Bayesian Inference via Coarsening(Accepted)." Journal of the American Statistical Association (January 1, 2018). Full Text

Johndrow, JE, Smith, A, Pillai, N, and Dunson, DB. "MCMC for Imbalanced Categorical Data(Accepted)." Journal of the American Statistical Association (January 1, 2018). Full Text

Dunson, D, and Fryzlewicz, P. "Report of the editors-2016." Journal of the Royal Statistical Society. Series B: Statistical Methodology 79.1 (January 1, 2017): 3-4. Full Text