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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 learning.
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 (2015-2022),
and Deputy/Managing Editor of
Moroccan
Journal of Pure and Applied Analysis at Sciendo-Gruyter
company (since
2016). 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. Younès
Bennani
is founder and Chief Scientific Officer (CSO) of "La
Maison des Sciences Numériques" - Paris Nord
(LaMSN), the first interdisciplinary
federative structure for digital
sciences research and training.
Latest research:
«On The Use of Persistent Homology to Control The Generalization Capacity of Neural Networks», in ICONIP, 2023.
«Modular
Self-Supervised Learning for Hand
Surigical Diagnosis»,
in IEEE-IJCNN,
2023.
«Self-Training and Modular Approaches for Surigical Image Recognition», in IEEE-IJCNN, 2023.
«Hierarchical Optimal Transport for Unsupervised Domain Adaptation», in Machine Learning Journal, Springer Nature, 2022.
«Unsupervised
Collaborative Learning Using
Privileged Information»,
CoRR abs/2103.13145, 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:
- 24H-HoloSurgery «International Conference on Holographic Surgery using Artificial Intelligence».
- 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
