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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 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.
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
