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; empirical likelihood; finite mixture model; sample survey; asymptotic theory |
| 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 |
| 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 | , Design of experiments, experiments with computer models, data mining, drug discovery, quality improvement |
| Wu, Lang | Biostatistical methods; Longitudinal data analysis, mixed effects models, missing data, hypothesis testing, biostatistics |
