Dal Crest Michael Dowd

Mike Pic

Director of Statistics
Department of Mathematics & Statistics
Dalhousie University
6316 Coburg Road, PO Box 15000
Halifax, Nova Scotia
Canada B3H 4R2

email: mdowd@mathstat.dal.ca
Telephone: (902) 494-1048
Fax : (902) 494-5130
Office: Chase Building, Room 116


Course Websites - see my Teaching Homepage

Research Interests

My research interests are in Environmental Modelling and Statistics for the Marine Sciences. They include:

  • State Space Models and Data Assimilation :
    I have an ongoing research interest in data assimilation, i.e. combining nonlinear dynamical models with available observations to produce optimal estimates of the time evolving system state and model parameters. This involves linking statistical estimation theory to the problem of fitting dynamical models (governed by time and space dependent partial differential equations) to observations. My recent research involves statistical approaches for the data assimilation problem. These rely on the nonlinear, nonGaussian state space model and use Monte Carlo based Bayesian computational methods. These ideas have been applied to marine ecological prediction, considering both the complex dynamical behaviour of biological models, as well as the high dimensionality found in practical applications.

  • Stochastic Marine Ecosystem Modelling :
    Another research area I have is in the development of mathematical models for lower trophic levels of marine ecosystems. Currently, I am considering stochastic versions of such ecological models for uncertainty qunatification, as well as Bayesian approachs to combining them with new types of observational data. This work has been focused on development and application of differential equation based models of coastal ecosystems to assess environmental effects of shellfish aquaculture, but also food web models and spatial ecology.

  • Data Analysis Methods for Marine Environmental Observations :
    I also have a general interest in the development and application of statistical analysis techniques for marine environmental data sets, especially satellite imagery. These have incuded spatial analysis of spaceborne synthetic aperture radar data for high resolution ocean winds and waves, and ocean color data for for biogeochemical models. Studies have also been undertaken into trend detection using monitoring time series of plankton, as well as looking at analysis of high resolution marine animal movement data and its link to behaviour.

    Recent Students and Post-Docs


    Refereed Journal Publications:

    Other Refereed Contributions: