Our interests cover a wide variety of areas in statistics, including both applied and theoretical problems. In addition, there are particularly strong groups in statistical genetics and Bioinformatics and environmental statistics. A special strength of the division is our commitment to interdisciplinary approaches to science. It is not uncommon for faculty to collaborate on a project with scientists from two or three other academic departments at Dalhousie University.
Statistical Genetics and Bioinformatics:
The science of Genetics is dedicated to studying the structure and function of genes and genomes, as well as the way genetic information is transmitted from generation to generation. Genetics has long been important in biology and society; however, with the rapid accumulation of gene and genome sequences, genetics has entered a new age, one which depends heavily on statistical methods. The statistical genetics group at Dalhousie University is comprised of a collection of faculty, staff, and graduate students who work together closely on methods for analyzing and interpreting genetic data.
The statistical genetics group is committed to an integrated approach to genetic and genomic science. Only through the integration of statistical, biological, mathematical, medical and computer science disciplines can the challenges of modern genetics and genomics be met. The statistical genetics group works to enhance the interdisciplinary environment at Dalhousie University through formal collaborations with other academic departments and the Center for Comparative Genomics and Evolutionary Biology. Past affiliations have included organizations such as Genome Atlantic and the CIAR Program in Evolutionary Biology. The Statistics Division provides a home for the weekly meeting of the Statistical Evolutionary Biology Group, a group of biologists, statisticians and computer scientists interested in applying statistical modeling techniques to problems in molecular evolution and comparative genomics.
Research Areas:
Group Members and their interests:
| Joseph Bielawski | Statistical Genetics| Molecular Evolution | Environmental Genomics |
| C.A. Field | Robust Statistics | Statistical Genetics | Data Analysis |
| Hong Gu | Multivariate Statistics | Machine Learning | Bioinformatics | Molecular Evolution |
| David Hamilton | Statistical Genetics | Linear and Nonlinear Regression | Design
of Experiments | Data Analysis |
| Christophe Herbinger | Quantitative Genetics| Statistical Genetics |
| Bruce Smith | Time Series | Statistical Genetics | Clinical Trials |
| E. Susko | Molecular Evolution | Bioinformatics | Mixture Models | Machine Learning |
Environmental Statistics is concerned with the development and application of statistical methods for the environmental sciences, with the purpose of addressing pressing environmental problems facing society. Its goal is the characterization and analysis of spatial and temporal variability in environmental observations, and the development of predictive models. Such models often require the fusion of discipline specific mathematical models with the techniques of statistical data analysis. An important challenge ahead lies in the development of novel techniques for the effective treatment of the wide assortment of new data streams from advanced environmental observing systems.
By its nature, Environmental Statisitics is a multidisciplinary endeavour. The Environment Statistics group at Dalhousie is part of the new Centre for Marine Environmental Prediction (www.cmep.ca). Its goals are to develop and test new technologies for the observation, prediction and visualization of the marine environment. This provides a unique opportunity for practical applications of Environmental Statistics. Our environmental statistics work is also supported by the National Program for Complex Data Structures through a project on Spatial/Temporal Analysis of Marine Ecological Systems.
Research Areas:
Group Members and their interests:
| Michael Dowd | Statistical Data Assimilation | Marine Environmental Data Analysis | Stochastic Ecosystem Modeling | Chris Field | Robust Statistics | Statistical Genetics | Data Analysis |
| Joanna Flemming | Longitudinal Data | Environmental Risk Assessment | State Space Models |
| David Hamilton | Statistical Genetics | Linear and Nonlinear Regression | Design of Experiments | Data Analysis |
| Ron Hilburn | Envirnomental Risk Assessment | Statistical Computing | Hydrologic Modeling |
| Bruce Smith | Time Series | Statistical Genetics | Clinical Trials |
| Keith Thompson | Time Series Analysis | Applications to Oceanography |
Recent Publications
Click HERE for a list of some recent publications.
Individual Faculty Interests
| J.P. Bielawski | Statistical Genetics| Molecular Evolution | Environmental Genomics |
| K.Bowen | Applied Problems in Health Professions |
| M. Dowd | Inverse problems | Environmental Statistics | Ecosystem Modeling |
| C. Field | Robust Statistics | Statistical Genetics | Data Analysis |
| G. Gabor | Bayesian Inference |
| H. Gu | Multivariate Statistics | Machine Learning | Bioinformatics | Molecular Evolution |
| R.P. Gupta | Multivariate Analysis | Distribution Theory | Statistical Inference |
| C. Herbinger | Quantitative Genetics| Statistical Genetics |
| D. Hamilton | Linear and Nonlinear Regression | Statistical Genetics | Design of Experiments | Data Analysis |
| Ron Hilburn | Envirnomental Risk Assessment | Statistical Computing | Hydrologic Modeling |
| B. Smith | Time Series | Statistical Genetics | Clinical Trials |
| E. Susko | Molecular Evolution | Bioinformatics | Mixture Models | Machine Learning |
| K. Thompson | Time Series Analysis | Applications to Oceanography |
