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

«My opinion is correct and may be wrong, and someone else's opinion is wrong and may be correct
»
(Ashâfiî
, 1767-820)

« 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.

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 «Recent Advancements in Multi-View Data Analytics»

Hardcover Series ISSN 2197-6503
VIII, 362, Vol. 106
  Springer International Publishing, 2022




            
Master of Data Science & Machine Learning
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