| «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) «My opinion is correct and may be wrong, and someone else's opinion is wrong and may be correct» (Ashâfiî, 1767-820) |
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 Scientific Director of "La Maison des Sciences Numériques" (LaMSN), the first interdisciplinary federative structure for digital sciences research and training.
Latest research:
«Incremental Confidence Sampling with Optimal Transport for Domain Adaptation», in International Journal of Neural Systems, 2024.
«On The Use of Persistent Homology to Control The Generalization Capacity of Neural Networks», in ICONIP, 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:
- 24HoS-Holographic Surgery «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
