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TERATEC 2019 Forum
Workshops - Wednesday June 12

Workshop 5 - 14:00 to 17:30
Machine learning & Maintenance prédictive
Chaired by Erick JONQUIERE, AFNet et Jean-Laurent PHILIPPE, Intel

A short story of algorithms and perspectives of Artificial Intelligence

By Nicolas VAYATIS, Director, CMLA

Il n'y a rien de nouveau dans l'IA. En effet, le boom technologique porté par l'IA repose sur trente ans de développements méthodologiques et algorithmiques pour l'estimation de règles prédictives à partir de données grâce à des algorithmes apprenants. Par ailleurs, un processus de maturation technologiques de telles méthodes initié il y a une quinzaine d'années est en cours et un nombre très limité d'applications en ont vu l'impact. Les promesses d'innovations de rupture sont pourtant nombreuses et ce, à peu près dans tous les secteurs de l'activité humaine : santé, éducation, transports, gestion des politiques publiques, pour en citer quelques uns, et bien évidemment dans l'industrie. Les études prospectives sont riches, les PoC innombrables, mais aucune méthodologie d'implémentation concrète de l'IA pour des usages métiers ne semble avoir fait ses preuves à ce jour. Le défi intellectuel est de comprendre pourquoi cette IA ultra-performante n'est pas déjà à l'oeuvre dans l'industrie, le défi technologique étant de trouver le bon chemin car il est certain que ce chemin existe.

Biography : Nicolas Vayatis is Full Professor at the Department of Mathematics of ENS Paris-Saclay and is the Director of the Center for Mathematics and Their Applications (CMLA – CNRS and ENS Paris-Saclay). He also leads a research group on Machine Learning and Massive Data Analysis (MLMDA) of about 20 people which is highly involved in interdisciplinary projects in the areas of network science, healthcare, digital marketing, and scientific computing. His main research interests are machine learning theory and algorithms, predictive modeling, sequential optimization and inference problems arising from real graph data. Nicolas Vayatis has been the advisor of 15 PhD students (defended) and 11 postdoctoral researchers between 2009 and 2017 and has coauthored more than 90 publications in peer-reviewed international journals and conferences. He also serves as an Action Editor for the Journal of Machine Learning Research since 2007. He is regularly invited to participate to scientific committees for the main conferences of the field of machine learning (NIPS, COLT, ALT,…). Over the years, Nicolas has been intensively teaching applied mathematics, statistics and probability, data mining and statistical learning for engineers, maths students, economists and psychologists, at various institutions (Université Pierre-et- Marie-Curie, ENS Cachan, Ecole Centrale Paris, ENSAE, INSEAD, Université Paris Nanterre, GeorgiaTech, Universitat Pompeu Fabra), and he currently is the main coordinator of the graduate master program MVA on Mathematics, Vision, and Learning which offers high level research training for more than 150 students per year. Nicolas Vayatis also provides expertise as a scientific advisor for the French Nuclear Agency (CEA).

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For any other information regarding the workshops, please contact :

Jean-Pascal JEGU
Tel : +33 (0)9 70 65 02 10
jean-pascal.jegu@teratec.fr
Campus TERATEC
2, rue de la Piquetterie
91680 BRUYERES-LE-CHATEL
France