«Ignorance leads to fear, fear leads to hatred
and hatred leads to violence. That's the equation
(Ibn Roshd,
Averroès, 1126-1198)

« Raise your words, not voice. It is rain that grows flowers, not thunder
 (Jalal Ad-Din Rumi, 1207-1273)
Younès Bennani received his PhD in Machine Learning from Université Paris-Saclay. He is currently Full Professor of Computer Science at Université Sorbonne Paris Nord. Younès Bennani research interests are in Machine Learning and Data Science. His research focuses on unsupervised learning, deep learning, and collaborative clustering. His recent work deals with the representations learning, federated learning, transfer learning and domain adaptation. He is the founder and scientific director (2005-2011) of a team whose main theme is Machine Learning and Applications at the Laboratoire d'Informatique de Paris Nord (LIPN - UMR 7030 CNRS). He has published 3 books and approximately 350 papers in refereed conferences proceedings or journals or as contributions in books. He has supervised 25 doctoral theses already defended, and is currently supervising 5 PhD students. He is director of Post-graduate programs in Machine Learning & Data Science at Institut Galilée (since 2001). He was elected President of the Computer Science Department at Institut Galilée (2010-2013). He was appointed Deputy Director of the LIPN-CNRS from 2008 to 2012. Younès Bennani is IEEE Senior member and Associate Editor at Springer - Knowledge and Information Systems Journal, and Deputy/Managing Editor of Moroccan Journal of Pure and Applied Analysis at Sciendo-Gruyter company. Younès Bennani was also elected Vice-President at Université Sorbonne Paris Nord, in charge of digital transformation (2016-2020) - Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation.

Latest research:

«Inductive Semi-supervised Learning Through Optimal Transport», CCIS Springer, 2021.

«Unsupervised Collaborative Learning Using Privileged Information», CoRR abs/2103.13145, 2021.

«Balanced K-means Using Quantum Annealing», IEEE-SSCI, 2021.

«A survey on domain adaptation theory: learning bounds and theoretical guarantees», CoRR abs/2004.11829, 2020.

«Advances in Domain Adaptation Theory», ISBN: 9781785482366 - ISTE - Elsevier, 2019.

«Collaborative Clustering: Why, When, What and How», International Journal on Information Fusion (Information Fusion), Elsevier, January 2018.

«Co-clustering through Optimal Transport», International Conference on Machine Learning (ICML'2017), Australia.

CNRS - Actualités scientifiques


Scientific events:



Master of Data Science & Machine Learning
Master EID2

«Advances in Domain Adaptation Theory»

ISBN: 9781785482366 - Elsevier

Sciences & sens de l’intelligence artificielle
Edition Dalloz 2020

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