• PhD Defense: Esteban Marquer (Orpailleur)

    Esteban Marquer (Orpailleur) will defend his thesis, entitled "Reasoning over Data: Analogy-based and Transfer Learning to improve Machine Learning", on Monday, June 24 at 2 p.m., in room B013. Abstract Recent years have seen a renewed interest in the potential of analogy detection and analogical inference, with successful applications in Machine Learning (ML) to the […]

  • Soutenance de Jacques Zhong (Larsen)

    Jacques Zhong (Larsen) soutiendra sa thèse intitulée "Prise en compte de la variabilité morphologique d'opérateurs sur des chaînes de montage en réalité virtuelle" le 25 juin à 13h30 en salle C005. Résumé Dans l'industrie, les troubles musculo-squelettiques constituent un problème de santé majeur affectant le bien-être et la productivité des travailleurs. Le travail d'un ergonome […]

  • PhD Defense: Abdelkarim Elassam (Tangram)

    Abdelkarim Elassam (Tangram) will defend his thesis, entitled "Learning-based vanishing point detection and its application to large-baseline image registration", on Thursday, July 4 at 1:30 p.m., in room A008. Abstract This thesis examines the detection of vanishing points and the horizon line and their application to visual localization tasks in urban environments. The thesis proposes […]

  • PhD Defense: Runbo Su (Simbiot)

    C005

    Runbo Su (Simbiot) will defend his thesis, entitled "Trust Management in Service-Oriented Internet of Things (SO-IoT)", on Friday, July 5 at 9 a.m., in room C005. Abstract Unlike Trust in Social Science, in which interactions between humans are measured, thanks to the integration of numerous smart devices, Trust in IoT security focuses more on interactions […]

  • PhD Defense: Alaaeddine Chaoub (Synalp)

    Amphi 7, Bâtiment Victor Grignard

    Alaaeddine Chaoub (Synalp) will defend his thesis, entitled "Deep learning representations for prognostics and health management", on Wednesday, July 10 at 2 p.m., in Amphi 7, Bâtiment Victor Grignard. Abstract This thesis contributes to the application of Deep Learning (DL) in Remaining useful life (RUL) prediction of industrial equipment, addressing significant challenges in this field. […]