Laurent Oudre - Home Page

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I'm currently a Full Professor at ENS Paris-Saclay in the Centre Borelli (UMR 9010). My research activities focus on machine learning, time series analysis, pattern recognition and signal processing, with applications in biomedical research and industry.

Jobs offers, internships, PhD positions available : send me an e-mail !

CV (in French) available here

Short Bio

Laurent Oudre is a full professor at the Centre Borelli of the Ecole Normale Supérieure Paris-Saclay (France). He leads a team of more than ten young researchers and has been working for about fifteen years on signal processing, pattern recognition and machine learning for time series. His work covers a wide range of topics: event detection (including change-point, pattern and anomaly detection), feature extraction, unsupervised or semi-supervised approaches, representation learning and graph signal processing. His scientific projects are mainly focused on AI applications in health and industry, often with a strong interdisciplinary component. He is also involved in initiatives around reproducible research and acculturation to AI (especially for the medical community). He is the author of more than 70 patents and articles in international peer-reviewed journals and conferences. He is also the director of the MVA (Mathematics, Vision and Learning) master's degree at the ENS Paris-Saclay.

ENS Paris Saclay
Centre Borelli
4, avenue des Sciences
91190 Gif-sur-Yvette
Bureau 2U20b
Batiment Nord, 2ème étage
+33 1 81 87 53 96
laurent.oudre [at] ens-paris-saclay [dot] fr

Teaching programs

	Director of the MVA Master's degree (Mathématiques, Vision et Apprentissage)
	Director of the ARIA diploma (Année de Recherche en Intelligence Artificielle)

Current projects

SaclAI-School training program which aims to develop an ambitious and innovative AI school around multidisciplinary and core AI (with ANR AMI Compétences et Métiers d'Avenir and Université Paris-Saclay)
PhLames industrial chair for the study of the physics of high voltage overhead lines (with ENS Paris Saclay and RTE)
Grant from the Thomas Jefferson Fund for the robust and interpretable multi-modal signal processing approaches for the screening, understanding and monitoring of neurodevelopmental and neurophysiological disorders (with Duke University)

A selection of recent papers

A. Bois, B. Tervil, and L. Oudre. Persistence-based clustering with outlier-removing filtration. Frontiers in Applied Mathematics and Statistics, 10:1260828, 2024.

S. W. Combettes, C. Truong, and L. Oudre. An Interpretable Distance Measure for Multivariate Non-Stationary Physiological Signals. In Proceedings of the International Conference on Data Mining Workshops (ICDMW), pages 533-539, Shanghai, China, 2023.

T. Germain, C. Truong, L. Oudre, and E. Krejci. Unsupervised classification of plethysmography signals with advanced visual representations. Frontiers in Physiology, 14:1154328, 2023.

S. Perochon, and L. Oudre. Unsupervised Action segmentation of Untrimmed Egocentric Videos. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Rhodes, Greece, 2023.

A. Mazarguil, L. Oudre, and N. Vayatis. Non-smooth interpolation of graph signals. Signal Processing, 196:108480, 2022

P. Humbert, L. Oudre, N. Vayatis, and J.Audiffren. Tensor Convolutional Dictionary Learning with CP Low-Rank activations. IEEE Transactions in Signal Processing, 70:785-796, 2022.