Marie-José Montpetit is a well-known Canadian researcher and consultant in distributed systems, in-network computing, digital twinning and applied Artificial Intelligence. From 2009-2019 she was a research scientist at MIT and before that held numerous R&D positions in the networking industry. She now focuses her research on distributed intelligent networking and applications. She is the co-chair of the Computing in the Network (COIN) research group at the IRTF, an adjunct professor at École de Technologie Supérieure in Montreal, a visiting scientist and lecturer at Telecom Paris Sud on programmable networks at aa lecturer at McGill University on the mapping of networking intelligence to supply and distribution. Until September 2024 she was an academic consultant at Iowa State university on intelligent wireless networks and is still an adjunct in the Electrical and Computer Engineering Deprartment. She is the recipient of the 2008 Motorola Innovation Prize and the 2010 MIT TR10.

Doctor in machine learning, specialized in deep learning and reinforcement learning, François-Xavier benefits from a multidisciplinary background in finance & economics (B.B.A.), strategy  & innovation management (M.Sc.), business intelligence (M.Sc. with direct entry to Ph.D.), and machine learning (Ph.D.), which has earned him many contributions of various kinds, as a researcher, consultant, trainer and entrepreneur.

His pedagogical proficiency and vulgarization abilities can be attributed to an extensive experience in AI and advanced analytics knowledge transfer. Besides teaching data science at university (M.B.A.), as principal scientist, he directed the AI training programs of the largest federation of credit unions in North America (Desjardins), while overseeing research and development work and partnerships. As a consultant and entrepreneur, he has also designed and delivered numerous AI training and ideation workshops to an international clientele, in a variety of industries.

With SOTA, François-Xavier is pursuing his activities in support of AI innovation, through which he leverages his hybrid affiliations with MILA  &  HEC Montréal.

Jonathan's key contributions are anchored at the intersection of industry and academia. On the one hand, he dedicated himself to Desjardins' AI research and development division, where he lead projects on a variety of expert subjects including recommender systems, graph neural networks and self-supervised learning. On the other hand he has been imparting programming knowledge at the University of Montréal, from which he holds both an M.Sc. in Data Science (HEC Montreal) and a Bachelor in Economics. He is also recognized for winning the reinforcement learning robotics competition at MILA (summer program).

Adept at translating complex technical concepts into accessible ideas, he bridges the gap between theory and practical application, which has led him to develop Desjardins' advanced analytics deep learning series. Jonathan is proficient in designing and implementing comprehensive machine learning programs across various educational platforms.

From introducing students to programming and data exploitation, all the way to disseminating emerging approaches to  AI researchers, the exceptional feedbacks and evaluations Jonathan received testify of his ability to nurture learners and organizations at all levels of mastery and technological maturity.