David B. Dunson
Arts and Sciences Professor of Statistical ScienceDevelopment of Bayesian statistical methods and approaches for uncertainty quantification motivated by applications with complex and high-dimensional data. A particular interest is in high-dimensional low sample size data in which it is necessary to incorporate dimensional reduction through carefully designed prior distributions and challenges arise in efficiently computing posterior approximations. Ongoing focus areas include new algorithms for approximating posterior distributions in big data settings, nonparametric Bayes probability modeling allowing for uncertainty in distributional assumptions, analysis of network data, incorporating physical and geometric prior knowledge in modeling and novel models for dimension reduction for "object data" (functions, tensors, shapes, etc). Primary application areas include genomics, neurosciences, epidemiology, and reproductive studies but with much broader interests in developing new methods motivated by difficult applications (in art, music, radar, imaging processing, etc).
Appointments and Affiliations
- Arts and Sciences Professor of Statistical Science
- Professor of Statistical Science
- Professor in the Department of Electrical and Computer Engineering
- Professor in the Department of Mathematics
- Faculty Network Member of the Duke Institute for Brain Sciences
- Office Location: 218 Old Chemistry Bldg, Durham, NC 27708
- Office Phone: (919) 684-8025
- Email Address: email@example.com
- Ph.D. Emory University, 1997
- B.S. Pennsylvania State University, 1994
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.
Awards, Honors, and Distinctions
- W.J. Youden Award in Interlaboratory Testing. American Statistical Association. 2012
- COPSS Award: President's Award. American Statistical Association. 2010
- ASA Fellows. American Statistical Association. 2007
- GENOME 293-1: Research Independent Study Genome Policy
- GENOME 293: Research Independent Study Genome Sciences
- STA 360: Bayesian Inference and Modern Statistical Methods
- STA 393: Research Independent Study
- STA 440: Case Studies in the Practice of Statistics
- STA 493: Research Independent Study
- STA 531: Advanced Bayesian Inference and Stochastic Modeling
- STA 601: Bayesian and Modern Statistical Data Analysis
- STA 790: Special Topics in Statistics
- STA 993: Independent Study
In the News
- Creative People Have Better-Connected Brains (Feb 20, 2017)
- Dunson Awarded Carnegie Centenary Professorship (Feb 15, 2017)
- Two Duke Teams Attempting to Map LinkedIn Universe (Jun 1, 2015 | Duke Research Blog )
- Mice sing just like birds, but we can’t hear them (Apr 1, 2015 | The Washington Post )
- Mice Sing Like Songbirds to Woo Mates (Apr 1, 2015)
- Squeaky serenade: male mice woo females with song, scientists discover (Apr 1, 2015 | The Guardian )
- How long can you wait to have a baby? (Jun 28, 2013 | The Atlantic )
- Exploring a New World of Social Data (Jun 4, 2013)
- Johndrow, JE; Bhattacharya, A; Dunson, DB, Tensor decompositions and sparse log-linear models, Annals of statistics, vol 45 no. 1 (2017), pp. 1-38 [10.1214/15-AOS1414] [abs].
- Lock, EF; Dunson, DB, Bayesian genome- and epigenome-wide association studies with gene level dependence., Biometrics (2017) [10.1111/biom.12649] [abs].
- Kunihama, T; Herring, AH; Halpern, CT; Dunson, DB, Nonparametric Bayes modeling with sample survey weights, Statistics & Probability Letters, vol 113 (2016), pp. 41-48 [10.1016/j.spl.2016.02.009] [abs].
- Rao, V; Lin, L; Dunson, DB, Data augmentation for models based on rejection sampling., Biometrika, vol 103 no. 2 (2016), pp. 319-335 [abs].
- Guhaniyogi, R; Dunson, DB, Compressed Gaussian process for manifold regression, Journal of machine learning research : JMLR, vol 17 (2016) [abs].