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Research Interests

Supervised and unsupervised learning, Statistical Pattern Recognition, Flexible regression/discrimination/clustering techniques.


Sparsity Inducing Penalties, Representation learning, Co-Clustering, Cross-Validation, Semi-supervised learning, Noise Injection, Evidential Pattern Recognition, Bioinformatics, Fault Diagnosis & Environmental Monitoring.

PhD students


PhD candidate Co-supervisor Subject Dates
Do, T. H. Nguyen V. L. Probabilistic Graphical Models for Complex Learning Tasks 2023-2026
Urbani N. S. Rousseau Detection of visual defects 2021-2024
Carvalho P. A. Durupt (Roberval) Machine learning in industrial diagnosis and process control 2021-2024


PhD Co-supervisor Subject Dates
G. Frisch J.-B. Leger Co-clustering for collaborative filtering 2018-2021
F. Cara N. Boudaoud (Roberval) Machine learning in industrial diagnosis and process control 2018-2022
A. Loukkal Localization and semantic segmentation with a cartographic prior 2017-2021
X. Li F. Davoine Regularization for deep transfer learning 2016-2019
S. A. Puthiya Parambath N. Usunier New methods for learning with multiple objectives 2013-2016
A. Garcia-Duran A. Bordes, N. Usunier Learning representations in multi-relational graphs: algorithms and applications 2013-2016
J.-M. Bécu C. Ambroise (LaMME) Uncertainty in Sparse Regression 2012-2015
X. Liu A. Bordes Weakly Supervised Bio-NLP 2011-2014
L. F. Sanchez G. Govaert Sparse Classification and Clustering 2010-2013
A. Lomet G. Govaert Model Selection for the Latent Block Model 2009-2012
M. Szafranski P. Morizet Hierarchical Penalization for Incorporating Knowledge in Statistical Model 2005-2008
R. Hérault F. Davoine Vision and Statistical Learning for Characterizing Human Behavior 2004-2007
M. Avalos C. Ambroise Parsimonious Additive Models 2001-2004
S. G. Maquiné de Souza T. Denœux Wastewater Sludge Processing by Image Analysis 2000-2005
J. François Data and knowledge fusion: an evolutive approach to diagnosis 1997-2000

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