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
| 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 | |
| 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 Professor Emeritus 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, James B. Duke Distinguished 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, Research 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 | |
| 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 | |
| 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 Emeritus of Statistics and Decision Sciences Bayesian analysis, decision theory and casual prediction. Multivariate time series and dynamic modelling. Business, economic, finance and governmental applications. | |
| Robert L. Wolpert, Professor Emeritus of Statistical Science Spatial statistics, extreme events, stochastic processes, non-parametric Bayesian analysis, statistical synthesis of information. Read More | |
| Jason Qian Xu, Research Assistant Professor of Statistical Science Stochastic modeling and processes, Statistical/Machine Learning, Large-scale Optimization, Expectation Maximization and Majorization-Minimization. Read More |