David B. Dunson

David B. Dunson

Arts and Sciences Professor of Statistical Science

Development 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
  • Faculty Network Member of the Duke Institute for Brain Sciences

Contact Information


  • Ph.D. Emory University, 1997
  • B.S. Pennsylvania State University, 1994

Research Interests

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

Courses Taught

  • GENOME 293-1: Research Independent Study in Genome Policy
  • MATH 493: Research Independent Study
  • STA 340: Introduction to Statistical Decision Analysis
  • 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 623: Statistical Decision Theory
  • STA 993: Independent Study

In the News

Representative Publications

  • Wheeler, MW; Dunson, DB; Herring, AH, Bayesian Local Extremum Splines., Biometrika, vol 104 no. 4 (2017), pp. 939-952 [abs].
  • Reddy, A; Zhang, J; Davis, NS; Moffitt, AB; Love, CL; Waldrop, A; Leppa, S; Pasanen, A; Meriranta, L; Karjalainen-Lindsberg, M-L; Nørgaard, P; Pedersen, M; Gang, AO; Høgdall, E; Heavican, TB; Lone, W; Iqbal, J; Qin, Q; Li, G; Kim, SY; Healy, J; Richards, KL; Fedoriw, Y; Bernal-Mizrachi, L; Koff, JL; Staton, AD; Flowers, CR; Paltiel, O; Goldschmidt, N; Calaminici, M; Clear, A; Gribben, J; Nguyen, E; Czader, MB; Ondrejka, SL; Collie, A; Hsi, ED; Tse, E; Au-Yeung, RKH; Kwong, Y-L; Srivastava, G et al., Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma., Cell, vol 171 no. 2 (2017), pp. 481-494.e15 [10.1016/j.cell.2017.09.027] [abs].
  • Li, C; Srivastava, S; Dunson, DB, Simple, scalable and accurate posterior interval estimation, Biometrika, vol 104 no. 3 (2017), pp. 665-680 [10.1093/biomet/asx033] [abs].
  • Srivastava, S; Engelhardt, BE; Dunson, DB, Expandable factor analysis, Biometrika, vol 104 no. 3 (2017), pp. 649-663 [10.1093/biomet/asx030] [abs].
  • Lock, EF; Dunson, DB, Bayesian genome- and epigenome-wide association studies with gene level dependence., Biometrics, vol 73 no. 3 (2017), pp. 1018-1028 [10.1111/biom.12649] [abs].