Nicolas VAYATIS is a Professor at the Ecole normale supérieure Paris-Saclay, affiliated with the Centre Borelli and the Department of Mathematics (DER de Mathématiques). He holds an ELLIS Fellowship (Paris unit) and specializes in theoretical foundations of machine learning, with applications to healthcare and industrial systems. He teaches in programs such as the Master M2 MVA, the normalien DER mathématiques, and the AI track at ENS Paris-Saclay. His research focuses on statistical learning, domain adaptation, change-point detection, and graph signal processing. He has advised over 20 PhD/Master’s students, including notable names like Antoine de Mathelin and Charles Truong. His work bridges academic theory and practical applications, collaborating on projects like fall detection using smart floor sensors and gait analysis with inertial measurement units. He contributes to open-source tools like the ruptures library for change-point detection and the ADAPT domain adaptation toolbox. Key collaborations include projects with INSEAD, Université Paris-Saclay, and industry partners like Michelin. His research has been published in venues such as NeurIPS, ICML, and AISTATS, with a focus on robust algorithms and real-world impact.
