I am Professor and Canada Research Chair in Learning and Simulation for Theoretical Biology at the School of Computer Science of the University of Windsor. I am cross-appointed by the Biology Department and the Great Lakes Institute for Environmental Research at the University of Windsor. I am also consultant for Movyl Entertainement (San Francisco). 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 8 PhD and 14 MSc students and I currently supervise  7 PhD and one MSc students. I have also supervised 2 Postdoctoral fellows and 9 graduate research assistants.

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

• Scott R.; Karim Pour, M.; Gras R., Ecological Effects of Cyclically Fluctuating Resources, the Summer Simulation Multi-Conference, 2016, in press
  

• Scott R., Gras R., MacIsaac H.J., Brown E.A., Cristescu M.E., Zhan, A., Determining a best-case effectiveness of a molecular method for the detection of aquatic invasives, ICAIS 2016, Winnipeg, Manitoba, April 2016, In press.
  

• MacPherson B., Gras R., Individual-Based Ecological Models: Adjunctive Tools or Experimental Systems?, Ecological Modelling, 323, 106-114, March 2016.
  

• Gras R., Golestani A, Hendry A., Cristescu M., Speciation without pre-defined fitness functions, PLOS ONE, DOI: 10.1371/journal.pone.0137838, 2015

• Karim Pour M., Bhattacharjee S., Gras R., Drouillard K., The integration of an individual-based model into toxicokinetics to enhance ecological realism in evaluating population-level impacts of exposure to PCB, 3rd World conference on Complex Systems, November, 2015, DOI: 10.1109/ICoCS.2015.7483286.

• Khater M., Murariu D., Gras R., Predation Risk Tradeoffs in Prey: Effects on Energy and Behaviour, Theoretical Ecology, In press.

• Mashayekhi M. Gras R., Rule Extraction from Random Forest: the RF+HC Methods, AI 2015, Advance in Artificial Intelligence, LNCS 9091, 223-237, Apr 2015.
  

• Our dedicated resources allocation on SHARCNET, 170 processors and 80TB, has been renewed for 2015. It corresponds to a $60000 grant.
  

I study the evolutionary process and the emergence of species in an artificial life 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.