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David B. Dunson, Arts and Sciences Professor of Statistical Science and Mathematics and Faculty Network Member of Duke Institute for Brain Sciences

David B. Dunson

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.   

Contact Info:
Office Location:  218 Old Chemistry Bldg, Durham, NC 27708
Office Phone:  (919) 684-8025
Email Address: send me a message
Web Page:

Teaching (Fall 2018):

    LSRC A247, TuTh 03:05 PM-04:20 PM
  • STA 623.01, STAT DECISION THEORY Synopsis
    LSRC A247, TuTh 03:05 PM-04:20 PM
Teaching (Spring 2019):

  • STA 841.01, CATEGORICAL DATA Synopsis
    Old Chem 116, TuTh 01:25 PM-02:40 PM
Office Hours:

Thurs 9-10am

PhDEmory University1997
Ph.D.Emory University1997
B.S.Pennsylvania State University1994

Bayesian Statistics
Complex Hierarchical and Latent Variable Modelling
Nonparametric Statistical Modelling
Model Selection
Statistical Modeling
Research Interests: Nonparametric Bayes, Latent variable methods, Model uncertainty, Applications in epidemiology & genetics, Machine learning

Current projects: Nonparametric Bayes methods for conditional distributions, Semiparametric methods for high-dimensional predictors, New priors for functional data analysis, Borrowing information across disparate data sources, Methods for identifying gene x environmental interactions

Development of Bayesian methods motivated by applications with complex and high-dimensional data. A particular focus is on nonparametric Bayes approaches for conditional distributions and for flexible borrowing of information. I am also interested in methods for accommodating model uncertainty in hierarchical models, and in latent variable methods, including structural equation models. A recent interest has been in functional data analysis.

Areas of Interest:

Functional data analysis
Latent variable methods
Machine learning
Molecular epidemiology
Nonparametric Bayes
Order restricted inference
Model selection and averaging


Action Potentials • Algorithms • Artificial Intelligence • B-Lymphocytes • Bayes Theorem • Computer Simulation • Data Interpretation, Statistical • DNA-Binding Proteins • Electrophysiological Phenomena • Epidemiologic Methods • Gene Dosage • Genetic Association Studies • Germinal Center • Longitudinal Studies • Markov Chains • Models, Statistical • Models, Theoretical • Monte Carlo Method • Multivariate Analysis • Neurons • Pattern Recognition, Automated • Phenotype • Probability • Stochastic differential equations

Representative Publications   (More Publications)   (search)

  1. Dunson, DB, Nonparametric Bayes local partition models for random effects., Biometrika, vol. 96 no. 2 (2009), pp. 249-262, ISSN 0006-3444 [doi]  [abs]
  2. Bigelow, JL; Dunson, DB, Bayesian semiparametric joint models for functional predictors, Journal of the American Statistical Association, vol. 104 no. 485 (2009), pp. 26-36, ISSN 0162-1459 [doi]  [abs]
  3. Dunson, DB; Xing, C, Nonparametric Bayes Modeling of Multivariate Categorical Data., Journal of the American Statistical Association, vol. 104 no. 487 (2009), pp. 1042-1051, ISSN 0162-1459 [doi]  [abs]
  4. Dunson, DB; Park, J-H, Kernel stick-breaking processes, Biometrika, vol. 95 no. 2 (2008), pp. 307-323, ISSN 0006-3444 [doi]  [abs]
  5. Dunson, DB; Herring, AH; Engel, SM, Bayesian selection and clustering of polymorphisms in functionally related genes, Journal of the American Statistical Association, vol. 103 no. 482 (2008), pp. 534-546, ISSN 0162-1459 [doi]  [abs]
Recent Grant Support

  • Reproducibility and Robustness of Dimensionality Reduction, National Institutes of Health, 2017/09-2022/07.      
  • Structured nonparametric methods for mixtures of exposures, National Institutes of Health, 1R01-ES028804-01, 2018/03-2022/02.      
  • CRCNS: Geometry-based Brain Connectome Analysis, National Institutes of Health, 1R01-MH118927-01, 2018/09-2021/06.      
  • Scalable probabilistic inference for huge multi-domain graphs, Alibaba Innovative Research, 2017/11-2020/10.      
  • Probabilistic learning of structure in complex data, Office of Naval Research, N00014-17-1-2844, 2017/09-2020/08.      
  • An Integrated Nonparametric Bayesian and Deep Neural Network Framework for Biologically-Inspired Lifelong Learning, Defense Advanced Research Projects Agency, 2018/02-2020/02.      
  • BIGDATA:F: Scalable Bayes uncertainty quantification with guarantees, National Science Foundation, 1546130, 2015/11-2019/10.      
  • BIGDATA:F: Scalable Bayes uncertainty quantification with guarantees, National Science Foundation, 1546130, 2015/11-2019/10.      
  • Predicting Performance from Network Data, U.S. Army Research Institute for the Behavioral and Social Sciences, W911NF-16-1-0544, 2016/09-2019/09.      
  • Network motifs in cortical computation, University of California - Los Angeles, 1430 G UA755, 2016/09-2019/06.      
  • Air Quality by Genomics Interactions in a Cardiovascular Disease Cohort, Health Effects Institute, 2014/06-2017/07.      
  • LAS DO6: Theory and Methods for Coarsened Decision Making; Synthetic Data Release: The Tradeoff between Privacy and Utility of Big Data, North Carolina State University, 2016-0740-06, 2016/01-2016/12.      
  • Bayesian Methods for High-Dimensional Epidemiologic Data, University of North Carolina - Chapel Hill, 5-31825, 2011/08-2016/05. 
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320