Statistics
Master of Science
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.
Graduate Degree Programs
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 | Statistical theory and modeling; empirical likelihood; finite mixture model; sample survey; asymptotic theory; imputation |
| Cohen Freue, Gabriela | statistical genomics (focus in proteomics), robust estimation and inference, linear models with endogeneity |
| Gao, Lucy | Statistics; Selective Inference; Inference x Unsupervised Learning; Statistics x Optimization |
| 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 |
| 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; Epidemiology (except nutritional and veterinary epidemiology); Methods and models for epidemiological forecasting; 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 |
| Pleiss, Geoffrey | Statistical theory and modeling; Machine learning; Computational methods in statistics; Spatial statistics; Numerical analysis; Machine Learning; neural networks; Gaussian processes; Bayesian optimization; reliable deep learning |
| Salibian-Barrera, Matias | S-regression estimationg, robust statistics, functional principal component analysis, bootstrap estimators, rgam, clustering algorithm |
| Welch, William | Computational methods in statistics; Computer experiments; Design and analysis of experiments; Statistical machine learning; Environmental modellign |
| Wu, Lang | Biostatistical methods; Longitudinal data analysis, mixed effects models, missing data, hypothesis testing, biostatistics |
