David B. Dunson
Arts and Sciences Professor of Statistical Science

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.   


Current Research Interests

Learning of low-dimensional geometric structure in data 

Models for non-Euclidean objects - networks, trees, surfaces, etc

Scalable algorithms for huge data sets

Monte Carlo 

Bayesian methods 

Applications in neuroscience, genomics, environmental health, ecology, sports statistics

Current Appointments & Affiliations

Contact Information

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