I work as an Associate 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 also 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.
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 7 PhD and 14 MSc trainees and I currently supervise 5 PhD and 1 MSc. I have also supervised 2 Postdoctoral fellows and 9 graduate research assistants.
My domains of research are: artificial life, theoretical biology, ecosystem simulation, predator-prey model, bioinformatics, combinatorial optimization, machine learning.
- Mashayekhi M. Gras R., Rule Extraction from Random Forest: the RF+HC Methods, AI 2015, in press.
- Our dedicated resources allocation on SHARCNET, 170
processors and 80TB, has been renewed for 2015. It corresponds to a
- "An Artificial World for Exploring Ecological Questions", a Science Cafe organized by the Canada South Science City, Windsor.
- Scott R., Gras R., MacIsaac H.J., Brown E.A., Cristescu M.E., Exploring
the Balance between Type I and Type II Error in Clustering of Ribosomal DNA
(18S) Sequences Using UPARSE, ASLO 2015, Aquatic Sciences
Meeting, Granada, Spain, Feb 2015.
- Golestani A., Gras R., Can we predict the unpredictable?, Scientific Reports, 4, 6834: DOI:10.1038/srep06834, 2014.
- Mashayekhi M., MacPherson B., Gras R., Species-area relationship and a tentative interpretation of the function coefficients in an ecosystem simulation, Ecological Complexity, 19, 84-95, 2014.
Mashayekhi M., Golestani A., Majdabadi Farahani Y., Gras R., An enhanced artificial ecosystem: Investigating emergence of ecological niches, the Fourteenth International Conference on the Synthesis and Simulation of Living Systems - Artificial Life 14, 693-700.
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.
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.These works are supported by the NSERC grant ORGPIN 341854, the CRC grant 950-2-3617 and the CFI grant 203617 and are made possible by the facilities of the Shared Hierarchical Academic Research Computing Network (SHARCNET) and Compute Canada.
University of Windsor
School of Computer Science
401 Sunset Avenue
Windsor, Ontario, N9b3P4 CANADA
Tel: +1 519 253 3000 ext. 2994
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