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 !

NEW OPEN postdoc position (for French-speaking candidates) ! NEW

CV (in French) available here

Short Bio

Laurent Oudre is currently a Full Professor at Centre Borelli of the Ecole Normale Supérieure Paris-Saclay. He leads a team of about 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 issues (event 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 fifty patents and articles in international peer-reviewed journals and conferences. He is currently the head of the MVA Master Program (Mathematics, Vision and Machine Learning) of ENS Paris Saclay.
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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

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)
  • diiP strategic project for the multimodal assessment of the depth of sedation of severely ill patients in intensive care unit (with Hôpital Militaire Bégin and Data Intelligence Institute of Paris)
  • 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)