I work as an Associate Professor and Canadian Research Chair in Probabilistic Heuristics and Bioinformatics at the School of Computer Science of the University of Windsor. I am also cross-appointed by the Biological Department at the University of Windsor. I was senior scientist, from 2000 to 2004, in the Swiss Institute of Bioinformatics, Geneva Switzerland after being post-doctorate from 1998 to 2000 in the same institute and lecturer, in 1998, at the University of Rennes, France. I received my B.Sc. and my M.Sc. in computer science at the University of Rennes. I completed my Ph.D. in computer science applied to bioinformatics at INRIA of Rennes in 1997, and obtained my Habilitation a Diriger la Recherche in 2004 in the University of Rennes. From 2000 to 2002 I was also consultant for GeneProt Inc. concerning the automation of protein identification and characterization process.

I have been funded by NSERC, SSHRC, GeneProt Inc. (Switzerland), CNRS (France), INRIA (France). I also received CFI and ORF infrastructure grants. To date, I have graduated 3 PhD and 12 MSc trainees and I currently supervise  6 PhD and 1 MSc students. I have also supervised 2 Postdoctoral fellows and 7graduate research assistants.

My domains of research are: artificial life, theoretical biology, ecosystem simulation, predator-prey model, bioinformatics, combinatorial optimization, machine learning.

• Khater M., Gras R., Adaptation and Genomic Evolution in EcoSim, 12th International Conference on Adaptive Behaviour, in press.

• Scott R., Gras R., Comparing Distance-Based Phylogenetic Tree Construction Methods Using An Individual-Based Ecosystem Simulation, EcoSim, The Thirteenth International Conference on the Synthesis and Simulation of Living Systems - Artificial Life 13, in press.
  

• Golestani A., Gras R., Cristescu M., Speciation with gene flow in a heterogeneous virtual world: can physical obstacles accelerate speciation?, Proceedings of the Royal Society B: Biological Sciences, in press.
  

• Mashayekhi M., Gras R., Investigating the Effect of Spatial Distribution and Spatiotemporal Information on Speciation using Individual-Based Ecosystem Simulation, Journal of Computing, 2(1), 98 – 103.

• We have been allocated 105 dedicated processors and 65 TB of dedicated storage for one year (2012) from Compute Canada.

• Hosseini M., Gras R., Md Sina, Prediction of Imminent Species' Extinction in EcoSim, International Conference on Agents and Artificial Intelligence 2012, Portugal, 318-323.
  

I study the evolutionary process and the emergence of species in a simulated ecosystem. I have conceived an individual-based evolving predator-prey ecosystem simulation called EcoSim. The agents evaluate their environment (e.g., distance to predator/prey, distance to potential breeding partner, distance to food, energy level), their internal states (e.g., fear, hunger, curiosity) and choose among several possible actions such as evasion, eating or breeding. The behavioral model of each individual is unique and is the outcome of the evolution process. One major and unique contribution of this simulation is that it combines a behavioral, an evolutionary and a speciation mechanism. This is the only simulation modeling the fact that individual behaviors affect evolution and speciation. This approach allows interesting studies on theoretical ecology and artificial life in collaboration with biologists. For example, this approach is used to study the species abundance distribution, patterns and rates of speciation, the evolution of sexual and asexual populations, the interaction and diffusion of an invasive species or a disease in an existing ecosystem, etc.

Several videos of the simulation are available here. A very long run of the simulation is analyzed here weekly.

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. 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.