Department of Statistics

Since the Department's founding in 1984, we have had a consistent vision of the discipline of Statistics and of our role in shaping it through our activities in education, in methodological and applied research, and in support of subject area research, a vision that is consistent with the newly emerged field of Data Science. Throughout its history, the Department has emphasized that the discipline of Statistics derives its importance from applications, but also requires a strong theoretical foundation. The Department has always valued data driven research, consulting, and collaboration, and has long held communication and computing skills as crucial for success. These values are apparent not only in individual faculty members’ research programs but also in our undergraduate and graduate curriculums, and through our consulting and research unit, the  Applied Statistics and Data Science Group.

33
Master's Students
32
Doctoral Students
9
Graduate Degrees Awarded
 
 

Research Supervisors

Name Research Interests
Auger-Methe, Marie Fisheries sciences; Statistics; Zoology; Animal movement; Polar ecology; Statistical Ecology
Bloem-Reddy, Benjamin developing methods for evolving networks whose history is unobserved; distributional limits of preferential attachment networks; uses of symmetry in statistics, computation, and machine learning
Bouchard-Cote, Alexandre machine/statistical learning; mathematical side of the subject as well as in applications in linguistics and biology
Campbell, Trevor automated, scalable Bayesian inference algorithms; Bayesian nonparametrics; streaming data; Bayesian theory; Probabilistic Inference; computational statistics; large-scale data
Chen, Jiahua Statistics; asymptotic theory; empirical likelihood; finite mixture model; sample survey
Cohen Freue, Gabriela statistical genomics (focus in proteomics), robust estimation and inference, linear models with endogeneity
Gustafson, Paul Statistics; meta-analysis; Parametric and Non-Parametric Inference; Theoretical Statistics; Pharmacoepidemiology; Bayesian statistical methods; Biostatistics and Epidemiology; Causal inference; Evidence synthesis; Partial Identification
Heckman, Nancy Statistics; Statistics and Probabilities; functional data analysis; smoothing; splines
Joe, Harry Sue Wah Statistics; Statistics and Probabilities; copula construction; dependence modelling; extreme value inference; non-normal time series; parsimonous high-dimensional dependence
Korthauer, Keegan Bioinformatics; Genomics; Statistics; Epigenomics; Single-cell analysis; Statistical genomics
McDonald, Daniel High dimensional data analysis; Computational methods in statistics; Statistical theory and modeling; Machine learning; Estimation and quantification of prediction risk; Evaluating the predictive abilities of complex dependent data; Application of statistical learning techniques to time series prediction problems; Investigations of cross-validation and the bootstrap for risk estimation
Nolde, Natalia Statistics; Statistics and Probabilities; Applications in finance, insurance, geosciences; Multivariate extreme value theory; Risk assessment
Park, Yongjin Other basic medicine and life sciences; High dimensional data analysis; Biostatistical methods; Bioinformatics; single-cell genomics; Computational Biology; Causal inference; Bayesian machine learning
Salibian-Barrera, Matias S-regression estimationg, robust statistics, functional principal component analysis, bootstrap estimators, rgam, clustering algorithm
Welch, William , Design of experiments, experiments with computer models, data mining, drug discovery, quality improvement
Wu, Lang Biostatistics
Zamar, Ruben Data mining and text mining, Modeling data quality, Development of new robust procedures, Statistical computing, Bioinformatics

Stories

 
 

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