Permanent researchers
![]() Team leader CNRS, Senior Researcher, HDR >> web page |
![]() CNRS, Researcher, HDR >> web page |
![]() Inria, Researcher, HDR >> web page |
![]() CNRS, Senior Researcher, HDR Head of the IRISA Lab >> web page |
![]() Univ. Rennes 1, Associate Professor >> web page |
![]() Univ. Rennes 1, Associate Professor >> web page |
![]() INSA Rennes, Professor, HDR Deputy director of the IRISA Lab >> web page |
Administrative assistant
![]() Team assistant Mail: aurelie.patier@inria.fr |
Starting Research Position
![]() in the context of the SAIDA project “Backdoors and AI Security” >> web page |
Post-doctoral Fellow
![]() in the context of the AID-CNRS Project “Fake news identification in social networks” |
Engineers
![]() “Computational journalism” >> web page |
![]() In the context of the ARCHIVAL project >> web page |
PhD students
![]() In the context of the SAIDA project “Sécurité des réseaux de neurones” |
![]() In collaboration with Imatag “Multimodal fake news detection” >> web page |
![]() In collaboration with Interdigital “Semantic multimodal question answering” >> web page |
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![]() In the context of the SAIDA project “Input space exploration for the security of neural-network models” |
![]() In collaboration with Ouest-France “Construction incrémentale dynamique de graphes de connaissance par fouille de contenus” |
![]() In the context of the ANR Archival “Espace multimodal pour la génération et la justification de liens sémantiques entre documents” >> web page |
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![]() “Learning hierarchies for motif discovery in multimedia data” |
![]() In collaboration with Zama and in the context of the SAIDA project “Apprentissage profond et chiffrement homomorphe” |
![]() In collaboration with Thales and in the context of the SAIDA project “Reliability of Deep Neural Networks with Rare Even Simulation algorithms. Theory and practice” |
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![]() In collaboration with Solocal “Plongement de données hétérogènes pour la recherche de professionnels.” |
![]() In the context of the ANR MEERQAT “Metric learning for instance- and category-level visual representation” >> web page |
Former Members
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