Improving Predictions Through Mathematics
Scientific research in probability theory - establishing models to determine the likely number of times a certain event may occur - is highly valued in Canada because it plays such an important role in so many diverse areas. It is particularly important in such fields as physics, statistics, computer science, information technology, genomics and ecology, which rely on models of randomness based on mathematical formulas.
Dr. Edwin Perkins is a leading researcher in probability theory. His outstanding contributions have been applied to several research fields. In fact, over the past twenty years, Dr. Perkins' work has settled wide-ranging and previously unresolved issues, opened up new avenues of research, and attracted worldwide interest among leading probability specialists.
As Chair in Probability, Dr. Edwin Perkins has built an expert team of top university researchers to continue his work in this challenging field. This group is reputed to be the strongest in probability in Canada, and one of the most influential in the world. All four members are Fellows of the Royal Society of Canada. Additionally, Dr. Perkins is the 2001 winner of the Jeffrey-Williams prize, the most prestigious award of the Canadian Mathematical Society.
Dr. Perkins' team is studying interacting particle systems arising from stochastic (random) models of populations including competing species, as well as predator-prey and symbiotic systems that experience random reproduction, migration and selection. This fundamental research aims to establish theories for stochastic mathematical models, which in turn should enable more precise predictions of mathematical probabilities. To achieve these results, the team is collaborating with visiting scientists from the U.S., France, Israel, Holland and Canada, postdoctoral fellows and graduate students.
Pondering fundamental scientific questions is important to all modern societies. By gaining a deeper mathematical understanding of certain simplified models of interacting species, Dr. Perkins' research may eventually improve our knowledge of more complex models, and provide valuable predictability tools for application in other mathematical fields.