Theory, Methods, and Computation

collage in circle

For decades, the Department has been known as a leading center of statistical science, and as the premier center worldwide for research and education in Bayesian methods. Statistical Science at Duke helped advance and popularize the Bayesian statistical paradigm, which offers a prescriptive framework for using probabilistic models to make inferences about scientific and social phenomena. Nowadays, Bayesian methods are used in nearly every field of inquiry. In recent years, we have expanded our research and education portfolios in areas such as causal inference, data privacy, data science, machine learning, and optimization. We continue to be a center of intellectual leadership in Bayesian methods and to grow our reputation as a top-tier department at the forefront of research and education in statistical science more broadly.

Faculty Research Focus
Photo of Filippo Ascolani
  Filippo Ascolani, Assistant Professor of Statistical Science

Bayesian nonparametric theory and modelling, Theory of Bayesian computation and MCMC, Model-based clustering. Read More


David L. Banks, Professor of the Practice of Statistical Science
Models for dynamic text networks, statistical inference for agent-based models, and adversarial risk analysis. Read More


James O. Berger, Arts and Sciences Distinguished Professor Emeritus of Statistical Science
Objective Bayesian analysis, model uncertainty, foundations of statistics, uncertainty quantification. Read More


Mine Çetinkaya-Rundel, Professor of the Practice of Statistical Science
Statistics and data science pedagogy, computation, reproducible research, student-centered learning, and open-source education. Read More


Merlise Clyde, Professor of Statistical Science
Model uncertainty, Bayesian model averaging, Wavelets and non-parametric function estimation. Read More


David B. Dunson, Arts and Sciences Distinguished Professor of Statistical Science
Statistical and machine learning for complex and high dimensional data; Flexible Bayesian and probabilistic modeling. Read More

Alex Fisher
  Alexander Fisher,  Assistant Professor of the Practice of Statistical Science

Bayesian modeling, hierarchical models, scalable inference machinery, statistical computing. Read More


Alan E. Gelfand, James B. Duke Distinguished Emeritus Professor of Statistical Science
Spatial and spatio-temporal modeling, applications to environmental and ecological statistics. Read More

  Amy H. Herring, Sara and Charles Ayres Distinguished Professor of Statistical Science
Longitudinal and multivariate data, Hierarchical models, Latent variables, Bayesian methods, Missing and mismeasured data. Read More
  Peter D. Hoff, Professor of Statistical Science

Multivariate analysis, multilinear modeling, small area inference, hierarchical modeling. decision theory.  Read More

  Edwin S. Iversen, Research Professor of Statistical Science

Problems at the interface between statistics and molecular biology, genetics, personalized medicine and epidemiology. Read More

  Yue Jiang, Assistant Professor of the Practice of Statistical Science

Mediation analysis, survival analysis, statistical education, quantitative literacy, public health. Read More

Eric Laber
  Eric Laber, Professor of Statistical Science and Biostatistics and  Bioinformatics
Precision medicine, reinforcement learning, causal inference, non-standard asymptotics.  Read More
  Fan Li, Professor of Statistical Science

Causal inference in observational data, missing data, Bayesian variable selection and imaging analysis. Read More

  Li Ma, Professor of Statistical Science
Nonparametric methods, high-dimensional inference, scalable inference, Bayesian modeling. Read More
  Simon Mak, Assistant Professor of Statistical Science

Reduction of big and high-dimensional data, scalable Bayesian methods, computer experiments, and Monte Carlo and Quasi-Monte Carlo sampling. Read More


Sayan Mukherjee, Professor of Statistical Science and Mathematics
Bayesian methodology, Inference for dynamical systems, Machine learning, Stochastic geometry and topology. Read More

  Galen Reeves, Associate Professor of Electrical and Computer Engineering and Statistical Science

Signal processing, statistics, and information theory, with applications in high-dimensional statistical inference, compressed sensing, and machine learning. Read More

Photo of Jerry Reiter
  Jerome P. Reiter, Professor of Statistical Science

Data privacy and confidentiality, missing data, multiple imputation, data integration. Read More

  Colin Rundel, Associate Professor of the Practice of Statistical Science
Computing in statistics and data science education; Bayesian spatial methodologies. Read More
  Scott C. Schmidler, Associate Professor of Statistical Science

Computational biology and biophysics, Monte Carlo algorithms, Convergence rates of Markov chains, Statistical shape theory. Read More

  Rebecca C. Steorts, Associate Professor of Statistical Science
Entity resolution (record linkage or deduplication), statistical machine learning, and Small area estimation. Read More
Photo of Maria Tackett
  Maria Tackett, Assistant Professor of the Practice of Statistical Science
Statistics pedagogy, writing interventions to help students understand complex statistical concepts, forensic evidence. Read More
  Surya T. Tokdar, Professor of Statistical Science

Nonparametric Bayesian analysis, asymptotic theory, regression smoothing, quantile regression. Read More

  Alexander Volfovsky, Associate Professor of Statistical Science

Causal inference, high dimensional data, network analysis. Read More

  Mike West, Arts and Sciences Distinguished Professor of Statistics and Decision Sciences

Bayesian analysis, decision theory and casual prediction. Multivariate time series and dynamic modelling. Business, economic, finance and governmental applications.
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  Robert L. Wolpert, Emeritus Professor of Statistical Science

Spatial statistics, extreme events, stochastic processes, non-parametric Bayesian analysis, statistical synthesis of information. Read More


Jason Qian Xu, Assistant Professor of Statistical Science
Stochastic modeling and processes, Statistical/Machine Learning, Large-scale Optimization, Expectation Maximization and Majorization-Minimization. Read More