My research is focused on the use of computational and mathematical methods to study problems of biological interest across multiple scales, from the small to the big. My Ph.D. research in behavioural ecology looked at theoretical models of social foraging behaviour in animals, and I have also worked on models of animal signalling; having joined the research group of Dr. Mark Tanaka, I am now looking forward to using my background in the study of the evolutionary dynamics of viruses and bacteria. I am interested in the application of computational methods to these questions in ecology and evolution, particularly methods of evolutionary computation (genetic algorithms, genetic programming, and other metaheuristics). Some specific examples of past and present research questions include:

  • What are the population dynamics of conventional signalling models in animals? (With Dr. Peter Hurd, University of Alberta; M.Sc. work)
  • How can we make progress in modelling social foraging using computational methods? (With Dr. Luc-Alain Giraldeau, UQAM; Ph.D. work)
  • What evolutionary forces affect the evolution and maintenance of learning abilities in animals? (In collaboration with Dr. Emilie Snell-Rood, University of Minnesota, and Dr. Julie Morand-Ferron, University of Ottawa).
  • How do life-history trade-offs affect reproduction decisions in human beings?
  • What factors affect the evolution of variation in viral mutation rates?

I am also interested in the use and mis-use of statistical methods, including nonparametric methods, machine learning, and spatial statistics. In the long run, I hope to be one of the practitioners of a new brand of behavioural ecology (and related disciplines) that leverages advances in our understanding of computational and mathematical modelling; this is an emerging area that is sometimes called ecological informatics, ecoinformatics, or computational ecology, and I would argue that it can be thought. of as a subset of the larger fields of computational and mathematical biology. This approach will deal with ways to handle the large data sets that are just beginning to emerge in the study of evolutionary questions (as they have in molecular biology, leading to the rise of bioinformatics as a discipline) and at the same time generate new models and theory to explain these data and predict new directions for empirical work.