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«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 24 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. 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:
«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.
«A new sparse representation learning of complex data: application to dynamic clustering of web navigation», Pattern Recognition (The journal of Pattern Recognition Society), 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.
Scientific events:
- ISCV: «International Conference on Intelligent Systems and Computer Vision» IEEE conference.
- ETP: «Deep Learning & Data Science» Ecole Thématique de Printemps.
- CIF-SD: Conférence Internationale Francophone sur la Science des Données
- H2DM: International Workshop on High Dimensional Data Mining
