Research
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 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:
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. Research areas include analysis
of gene chip data, estimation of breeding values, QTL analysis,
phylogenetic methods, estimating selection pressure on protein coding
sequences, and genomic analysis.
Members of this group include Hong Gu, Chris Field,
Ed Susko, David Hamilton, Christophe Herbinger,
Bruce Smith, and Joseph Bielawski.
The statistical genetics group is committed to an
integrated approach to genetic and genomic science. Only through the
integration of statistical, biological, mathematical, 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 with
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.
Follow this link for a list of some
recent
publications.
Environmental Statistics:
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.
The
Environmental Statistics group at Dalhousie includes Michael Dowd,
Chris Field, Joanna Flemming, David Hamilton, Bruce Smith and Keith Thompson.
Current
research areas are: (i) Statistical inverse problems for marine
prediction; (ii) Data assimilation and the development of operational
forecast systems; (iii) Satellite image analysis for ocean surface
winds and waves; (iv) Analysis and modelling of bio-optical plankton
observations; and (iv) Modelling extremes in atmosphere and oceans
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.
Follow this link for a list of some
recent
publications.
Individual Faculty Interests:
Click on faculty names to go to their personal web
sites
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J.P.
Bielawski |
Statistical
Genetics| Molecular Evolution |
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K.Bowen |
Applied Problems in Health Professions |
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M. Dowd |
Inverse problems | Environmental Statistics | Ecosystem Modeling |
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C.A. Field |
Robust
Statistics | Statistical Genetics | Data Analysis |
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J. Flemming
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Longitudinal data | State space models |
Environmental risk assessment |
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G. Gabor |
Bayesian
Inference |
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H. Gu |
Multivariate Statistics |Machine Learning | Bioinformatics |
Molecular Evolution
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R.P. Gupta |
Multivariate
Analysis | Distribution Theory | Statistical Inference
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C. Herbinger |
Quantitative
Genetics| Statistical
Genetics |
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D. Hamilton |
Linear and Nonlinear Regression | Statistical Genetics | Design of
Experiments
| Data Analysis |
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B. Smith
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Time Series |
Statistical Genetics | Clinical Trials |
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E. Susko |
Mixture Models | Machine Learning | Bioinformatics | Molecular
Evolution |
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K. Thompson |
Time Series
Analysis | Applications to Oceanography |
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