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DTSTART;TZID=Europe/Paris:20260114T140000
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DTSTAMP:20260502T192825
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UID:9664-1768399200-1768404600@www.etis-lab.fr
SUMMARY:Data&AI Seminar - Khalil Bachiri
DESCRIPTION:Title: Multimodal and Heterogeneous Graph Learning for Robust\, Explainable and Frugal Intelligent Systems \nAbstract:\nThe increasing availability of heterogeneous and multimodal data poses significant challenges for the design of modern intelligent systems\, particularly in terms of representation\, fusion\, robustness\, explainability\, and computational frugality. These challenges become even more critical when data are structured through complex and evolving relational graphs\, as is often the case in real-world applications. In this presentation\, I will introduce my research contributions on multimodal and heterogeneous graph learning\, developed during my PhD\, with the objective of designing models capable of understanding\, reasoning\, and learning from interacting modalities. I will present graph-based and topology-aware learning architectures that explicitly model modality heterogeneity\, inter-modal dependencies\, and structural relations\, while relying on adaptive fusion\, alignment mechanisms\, and attention-based reasoning to improve robustness\, stability\, and interpretability. These approaches have been validated on real-world recommendation and decision-support scenarios and have led to several international publications. Finally\, I will outline my research perspectives\, aiming to further develop robust\, energy-efficient\, and explainable multimodal intelligent systems\, including responsible AI\, multimodal platforms\, and low-footprint learning for complex environments. \nShort Bio:\nKhalil Bachiri is a Doctor in Artificial Intelligence from CY Cergy Paris Université (ETIS\, CNRS UMR 8051)\, where he is currently an ATER. His research focuses on multimodal learning and heterogeneous graph learning\, topology-aware models\, and explainable AI\, with an emphasis on robustness\, frugality\, and multimodal reasoning for recommendation and decision-support systems. He has published in international journals and conferences. He also worked as an AI Research Engineer at CNRS and has been a visiting researcher at LIPN (Université Sorbonne Paris Nord). \n 
URL:https://www.etis-lab.fr/event/dataai-seminar-khalil-bachiri/
LOCATION:Online
CATEGORIES:Seminar
ORGANIZER;CN="Vassiis Christophides":MAILTO:vassilis.christophides@ensea.fr
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DTSTART;TZID=Europe/Paris:20260129T133000
DTEND;TZID=Europe/Paris:20260129T150000
DTSTAMP:20260502T192825
CREATED:20260123T075216Z
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UID:9749-1769693400-1769698800@www.etis-lab.fr
SUMMARY:Séminaire DATA&AI : Issam Falih
DESCRIPTION:Apprentissage multimodal frugale : alignement par transport optimal\, explicabilité par concepts et déploiement Edge\nRésumé :\nLes architectures d’apprentissage profond constituent aujourd’hui l’état de l’art pour l’analyse et la fusion de données multimodales. Leur déploiement effectif dans des environnements ouverts soulève encore de nombreuses questions quant à leur fiabilité et leur transparence. En particulier\, si ces modèles excellent en conditions contrôlées\, plusieurs travaux ont mis en évidence leur sensibilité aux dérives distributionnelles (concept drift) et leur opacité décisionnelle\, limitant leur usage dans des contextes critiques. \nDans cette présentation\, je traite dans un premier temps\, des problématiques d’alignement et de fusion multimodale ou je présente le Transport Optimal Hiérarchique comme un levier géométrique pour l’alignement de structures dans un cadre non supervisé. Dans un deuxième temps\, j’aborde l’explicabilité des réseaux de neurones à travers les modèles à goulot de concepts (CBM). Je présente une architecture hybride (KL-CBM) où un classifieur dense est aligné sur un module probabiliste. Enfin\, je conclue avec des applications réelles notamment de l’inférence distribuée sur systèmes embarqués (Edge AI). \nLien de connexion :\nSeminaire Issam Falih | Meeting-Join | Microsoft Teams \n 
URL:https://www.etis-lab.fr/event/seminaire-dataai-issam-falih/
LOCATION:Online
CATEGORIES:Seminar
ORGANIZER;CN="Vassiis Christophides":MAILTO:vassilis.christophides@ensea.fr
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