Brief Biography
Dr. Robin Gras is Associate Professor and Canadian Research Chair in Probabilistic Heuristics and Bioinformatics at the School of Computer Science of the University of Windsor. He is also cross-appointed by the Biological Department at the University of Windsor. He was senior scientist, from 2000 to 2004, in the Swiss Institute of Bioinformatics, Geneva Switzerland after being post-doctorant from 1998 to 2000 in the same institute and lecturer, in 1998, at the University of Rennes, France. He received his B.Sc. and his M.Sc. in computer science at the University of Rennes. He completed his Ph.D. in computer science applied to bioinformatics at INRIA of Rennes in 1997, and obtained his Habilitation a Diriger la Recherche in 2004 in the University of Rennes. From 2000 to 2002 he was also consultant for GeneProt Inc. concerning the automation of protein identification and characterization process.
Research Activities
My research focuses on analyzing and modeling complex biological systems. Most of the biological processes involve a dynamic system of interacting components. In general, the network of interactions between these components is partially or completely unknown. As the number of components involves is very large and the complexity of the network is very high, no exact analysis methods can provide a result in a reasonable time. I work on heuristics approaches based on the building of probabilistic models of the data and simulation of dynamic interacting systems to provide good approximations of the underlying studied processes’ model. The uniqueness of my researches comes from two directions. First, I improve and combine several efficient methods to discover dependencies in a dataset and use these information for feature selection and to build high accuracy predictors. This is particularly important to be able to understand the new data coming from system biology (gene expression data and proteomics) and from clinical measurement. Second, I have conceived a very detail simulation framework based on low level interaction between the agents of the system. To my knowledge it is the only existing simulation able to represent cooperative and competitive agents with complex evolving behavior, emergence and death of species based on genomic set representation and learning capacity of the agents.
You can reach me at rgras@uwindsor.ca
