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
Banks

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

Berger

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

Cetinkaya-Rundel

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

Clyde

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

Dasmohapatra
Sudipta Dasmohapatra, Lecturer, Senior of Statistical Science

Data science and statistical models in marketing applied to retail and environmental products. Read More

Dunson

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

Gelfand

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

Herring
Amy H. Herring, Sara and Charles Ayres Distinguished Professor
Longitudinal and multivariate data, Hierarchical models, Latent variables, Bayesian methods, Missing and mismeasured data. Read More
Hoff
Peter D. Hoff, Professor of Statistical Science
Analysis of vector-, matrix- and tensor-valued data; social network analysis, spatio-temporal modeling, small area inference, and multigroup analysis. Read More
Iversen
Edwin S. Iversen, Research Professor of Statistical Science

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

Jiang
Yue Jiang, Assistant Professor of the Practice of Statistical Science
Statistical education, quantitative literacy, effect size measures for mediation analysis in complex data, biostatistics. Read More
Li
Fan Li, Associate Professor of Statistical Science

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

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

Mukherjee

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

Reeves
Galen Reeves, Associate Professor in the Department of Electrical and Computer Engineering

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

Reiter
Jerome P. Reiter, Professor of Statistical Science

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

Rundel
Colin Rundel, Assistant Professor of the Practice of Statistical Science
Computing in statistics and data science education; Bayesian spatial methodologies. Read More
Santo
Shawn Santo, Assistant Professor of the Practice of Statistical Science

Statistics pedagogy, computing, quantifying statistical reasoning.  High-dimensional longitudinal data. Read More

Schmidler
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

Steorts
Rebecca C. Steorts, Assistant Professor of Statistical Science
Entity resolution (record linkage or deduplication), statistical machine learning, Small area estimation. Read More
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
Todkar
Surya T. Tokdar, Associate Professor of Statistical Science

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

Volfovsky
Alexander Volfovsky, Assistant Professor

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

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

Bayesian decision analysis and decision theory, Multivariate time series and dynamic modelling, Computational scalability. Read More

Wolpert
Robert L. Wolpert, Professor of Statistical Science

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

Wu
Hau-Tieng Wu, Associate Professor of Mathematics

Data science for medical data analysis, high dimensional time series, mathematical and statistical foundations. Read More

Xu

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