I am Professor at the School of Computer Science of the University of Windsor and CSO and partner at Movyl Technologies and MVYL Associates (San Francisco). I am cross-appointed by the Biology Department and the Great Lakes Institute for Environmental Research 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.

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

With MVYL associates, we propose AI consulting for helping small, medium and large companies acquiring the knowledge and tools needed to integrate and execute AI in their strategy roadmap. 

With Movyl Technologies, we offer an AI automation platform to discover and automatically share unique content tailored to your social media accounts.

• MacPherson B., Scott R., Gras R., Sex and recombination purge the genome of deleterious alleles: An Individual Based Modeling Approach, Ecological Complexity, In Press.

• Scott R.; Gras R., A simulation study shows impacts of genetic diversity on establishment success of digital invaders in heterogeneous environments, Ecological Modelling, 431, https://doi.org/10.1016/j.ecolmodel.2020.109173, September 2020.
  

• Bhattacharjee S., MacPherson B., Wang R. F., Gras R., Animal communication of fear and safety related to foraging behavior and fitness: an individual-based modeling approach, Ecological Informatics, 54, November 2019, https://doi.org/10.1016/j.ecoinf.2019.101011
  

• Bhattacharjee S.,Gras R., Estimation of Distribution using Population Queue based Variational Autoencoders,2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, 1406-1414., doi: 10.1109/CEC.2019.8790077, 2019

• Scott R.; MacPherson B., Gras R., A comparison of stable and fluctuating resources with respect to evolutionary adaptation and life-history traits using individual-based modeling and machine learning, Journal of Theoretical Biology, 459(14), 52-66, 2018.
• Scott R., MacPherson B., Gras R., EcoSim, an enhanced artificial ecosystem: addressing deeper behavioral, ecological, and evolutionary questions, Cognitive Architectures, Maria Isabel Aldinhas Ferreira, João Silva Sequeira and Rodrigo Ventura Editors, Springer, 223-278, 2018.

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.

I am also involved in design and development of Natural Language Processing, Knowledge Extraction, Visual Information Processing, Mood and Sentiment Processing methods for social media automation.