Statistics

Research interests of the faculty include biostatistics, environmetrics, mathematical modelling of biological systems, computational statistics, data mining, machine learning, theory of statistical inference, asymptotics, multivariate analysis, robustness, nonparametrics, design of experiments, smoothing, Bayesian methods, computational molecular biology, gene expression, and microarrays.

 

Explore our Programs in Statistics

 
 

Faculty Members in Statistics

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
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
 
 
 
 

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