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.

 

Faculty Members

Name Research Interests
Auger-Methe, Marie Statistical Ecology, Polar ecology, Animal movement
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 finite mixture model, empirical likelihood, asymptotic theory, sample survey
Cohen Freue, Gabriela statistical genomics (focus in proteomics), robust estimation and inference, linear models with endogeneity
Gustafson, Paul Meta-Analysis, Parametric and Non-Parametric Inference, Theoretical Statistics, Pharmacoepidemiology, Bayesian statistical methods, Causal inference, Evidence synthesis, Biostatistics and Epidemiology, Partial Identification
Heckman, Nancy Statistics and Probabilities, functional data analysis, smoothing, splines
Joe, Harry Sue Wah Statistics and Probabilities, dependence modelling, copula construction, non-normal time series, extreme value inference, parsimonous high-dimensional dependence
Korthauer, Keegan Statistical genomics, Epigenomics, Single-cell analysis
McDonald, Daniel 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
Mostafavi, Sara machine/statistical learning applied to disease genetics
Nolde, Natalia Statistics and Probabilities, Multivariate extreme value theory, Risk assessment, Applications in finance, insurance, geosciences
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