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Nathalie Mejean

Nathalie Mejean
 

Recherche:

Ma recherche est au croisement des systèmes du vivant en agriculture et alimentation, des sciences cognitives et des méthodes d'ellicitation et de formalisation des savoirs par intelligence artificielle. Proposer, explorer avec les experts une représentation in silico des mécanismes qui prennent place dans les systèmes alimentaires complexes et induisent les dynamiques aux différentes échelles. Intégration des connaissances expertes et couplage à des méthodes d'apprentissage automatique.

My research is located at the interface of life science for agri-food systems, cognitive science and artificial intelligence. Propose, explore with the experts in silico representation of mechanisms that take place in complex agri-food systems, crossing over scales, is the challenge of my research. Computing tools are developed to enhance the interaction between the expert reasoning, his know-how and machine learning automatic process.

Keywords: Agri-Food systems, fuzzy logic, human expertise formalization, computing tools frameworks for experts and machine learning cooperation

Parcours:

  • IR Cemagref de 1998 à 2005
  • IR INRA de 2005 à 2010
  • DR INRAE 2010 - et membre associée ISCPIF (Institut des Systèmes Complexes de Paris Ile de France)

Formation:

  • Ingénieure INSA Toulouse, 1993.
  • Docteure de l’ENSIA Massy (AgroParisTech) en Génie des Procédés Alimentaires (qualifiée section 61 et 62), 1997.
  • Habilitée à Diriger des recherches (thèse d’état) en Sciences Pour l’Ingénieur de l’université de Clermont Ferrand, 2004.

Quelques publications clés (*nom d'usage Perrot):

  1. Nadia Boukhelifa, Anastasia Bezerianos, Ioan Cristian Trelea, Nathalie Perrot, and Evelyne Lutton.“An Exploratory Study on Visual Exploration of Model Simulations by Multiple Types of Experts”. In: CHI 2019 The ACM CHI Conference on Human Factors in Computing Systems. Glasgow, United Kingdom, May 2019. doi: 10.1145/3290605. url: https://hal.inria.fr/hal-02005699
  2. Evelyne Lutton and Nathalie Perrot. “Human in the loop for modelling food and biological systems: a perspective based on Artificial Intelligence”. In: FOODSIM’2018. Ghent, Belgium, Apr. 2018. url: https://hal.inrae.fr/hal-02786708.
  3. Roche, A., Perrot, N., Chabin, T., Villiere, A., Symoneaux, R., Thomas-Danguin, T. (2017), In silico modelling to predict the odor profile of food from its molecular composition using experts' knowledge, fuzzy logic and optimization: Application on wines, 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), Montreal, QC, 2017, pp. 1-3. doi: 10.1109/ISOEN.2017.7968875.
  4. Perrot, N., De Vries, H., Lutton, E., Van Mil, H.G.J., Donner, M., Tonda, A., Martin, S., alvarez, A., Bourgine, P., van der Linden, E. Axelos, M. (2016). Some remarks on computational approaches towards sustainable complex agri-food systems. Trends in Food Science and Technology, 48, 88-101.
  5. Van Mil, H.G.J., Foegeding, A.E. , Windhab, E.J. , Perrot, N., Van der Linden, E. (2014). A complex system approach to address world challenges in food and agriculture. Trends in Food Science and Technology, 40 (1), 20-32.
  6. Lutton, E., Tonda, A., Gaucel, S. Riaublanc, A., Perrot, N. (2014) Food model exploration through evolutionary optimisation coupled with visualisation: application to the prediction of a milk gel structure. Innovative Food Science and Emerging Technologies, 25, 67-77.
  7. Perrot, N., Baudrit, C., Brousset, J.M., Abbal, P., Guillemin, H., Perret, B., Goulet, E., Guerin, L., Barbeau, G., Picque, D. (2015). A Decision Support System Coupling Fuzzy Logic and Probabilistic Graphical Approaches for the Agri-Food Industry: Prediction of Grape Berry Maturity. PlosOne, 10(7). 10.1371/journal.pone.0134373.
  8. Sicard, M., Perrot, N., Reuillon, R., Mesmoudi, S., Alvarez, I., Martin, S. (2012) A viability approach to control food processes: Application to a Camembert cheese ripening process. Food Control, 23, 312-319.
  9. Perrot, N., Baudrit, C., Trelea, I.C., Trystram, G., Bourgine, P. (2011). Modelling and analysis of complex food systems: state of the art and new trends. Trends in Food Science and Technology, 22(6), 304-314.
  10. Baudrit, C., Sicard, M., Wuillemin, P.H., Perrot N. (2010). Towards a global modelling of the Camembert-type cheese ripening process by coupling heterogeneous knowledge with dynamic Bayesian networks, Journal of Food Engineering, 98 (3), 283-293.
  11. Barrière, O., Lutton, E., Baudrit, C., Sicard, M., Pinaud B., Perrot, N. (2008). Modeling Human Expertise on a Cheese Ripening Industrial Process Using GP. Lecture Notes in Computer Science, Parallel Problem Solving from Nature – PPSNX , Springer Berlin/Heidelberg (Eds). Vol 5199, pp. 859-868. 
  12. Allais, I., Perrot, N., Curt, C., Trystram, G., (2007) Modelling the operator know-how to control sensory quality in traditional processes. Journal of Food engineering. 83 (2): 156-166.
  13. Perrot, N. (2006). Fuzzy concepts applied to food product quality control. Editorial. Fuzzy Sets and Systems, 157, 1143-1144. Special edition.

 

Chapitres d’ouvrages…

Perrot, N., Baudrit, C., Sicard, M., Bourgine, P. (2014). L’aliment: un système complexe à modéliser et maîtriser. In Science culinaire sous la direction de Christophe Lavelle. Belin éditions.

Perrot, N., Baudrit, C. (2012) Intelligent Quality control systems in food processing based on fuzzy logic in Robotics and automation in the food industry: Current and future technologies. Edited by D Caldwell, Italian Institute of Technology, Italy, December 2012 ISBN 1 84569 801 0, Woodhead Publishing Series in Food Science, Technology and Nutrition No. 236.

Publications