WelcometomyWebsite




«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 Paris-Saclay University. He is currently Full Professor of Computer Science at Sorbonne Paris Nord University. 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 (from 2005 to 2011) of a team whose main theme is Machine Learning and Applications at the computer science lab. of Paris-Nord (LIPN - UMR 7030 CNRS). He has published 3 books and approximately 300 papers in refereed conferences proceedings or journals or as contributions in books. Younès Bennani is the Vice-President for Digital Transformation at the Sorbonne Paris Nord University - Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation.


Latest research:

«A Survey on Domain Adaptation Theory», CoRR abs/2004.11829, 2020.

«Data Anonymization through Collaborative Multi-view  Microaggregation», Journal of Intelligent Systems (JISYS), 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.

CNRS - Actualités scientifiques

TechTalksTV


Scientific events:





        








       



«Advances in Domain Adaptation Theory»

Hardcover ISBN: 9781785482366
eBook ISBN: 9780081023471
ISTE - Elsevier


            
Master of Data Science & Machine Learning
Master EID2