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Jean-Baptiste AlayracView profile

Researcher

• Computer Vision
• Machine Learning
• Multimodal Learning
• +3 more

Jean-Baptiste Alayrac is a Researcher at DeepMind, focusing on structured learning from video and natural language. His academic background includes a PhD from the Sierra and Willow groups at Ecole Normale Supérieure and Telecom ParisTech, where he explored machine learning and computer vision. He has held teaching roles as a Teaching Assistant at Ecole Normale Supérieure and other universities, contributing to courses in statistical machine learning and mathematics. His research interests span multimodal learning, vision-language models, self-supervised learning, and efficient retrieval systems. Notable projects include the Flamingo model for few-shot learning and the Perceiver IO architecture for structured data processing. He has also contributed to foundational works like HowTo100M, leveraging large-scale video-text embeddings. Alayrac's publications emphasize cross-modal interactions, with key contributions in adversarial robustness, layered video representations, and weakly supervised learning. His work often bridges computer vision and natural language processing, with applications in instructional video analysis and cross-lingual translation.

Nadjib BRAHIMIView profile

Associate Professor

• Supply Chain Management
• Production Planning and Scheduling
• Operations Research
• +4 more

Nadjib Brahimi is an Associate Professor at Rennes School of Business, specializing in Supply Chain Management and Operations Research. He holds a PhD in Production and Logistics Systems from Université de Nantes and has extensive academic experience, including roles as Academic Head of the Supply Chain Management & Information Systems department (2019–2020) and Programme Manager of the MSc in Data and Business Analytics. His expertise spans Production Planning, Supply Chain Optimization, and Reconfigurable Manufacturing Systems. Education: PhD in Production and Logistics Systems, Université de Nantes, 2004 M.Sc. in Automatic Control and Applied Computing, Ecole Centrale de Nantes, 2001 Engineering Degree in Electronics and Electrical Engineering, National Institute of Electronics and Electrical Engineering, Boumerdes, Algeria, 1999 Research Focus: His work emphasizes optimization of production systems, reconfigurable manufacturing, and sustainable supply chains. Key areas include task reassignment in assembly lines, biomass supply chain design, and disruption resilience strategies. Key Contributions: Over 40 peer-reviewed articles in journals like International Journal of Production Economics and European Journal of Operational Research . Developed robust optimization models for supplier selection and production routing. Advocated for circular economy principles in hybrid manufacturing systems. Labs/Teams: He collaborates with research groups on reconfigurable manufacturing systems and bioenergy supply chain design, contributing to projects in France, UAE, and Algeria.

David DaneyView profile

Professor

• Robotics
• Cobotics
• Human-Robot Interaction
• +14 more

David Daney is a Senior Researcher (Directeur de recherche) at Inria and HDR-qualified academic, currently serving as Head of Science for the Inria Center at the University of Bordeaux since July 2024. He is the team leader of the Auctus research group, focusing on robotics, cobotics, and human-robot interaction. He is affiliated with Inria and the École Nationale Supérieure de Cognitique (ENSC) at the University of Bordeaux, within the College of Engineering and the Department of Robotics. His research interests include Robotics, Cobotics, Human-Robot Interaction, Human Posture Analysis, Cable-driven Robots, Parameters Identification, Calibration, Interval Analysis, and Haptic Guidance. His work bridges theoretical robotics with industrial applications, particularly in aerospace, automotive, and sustainable agriculture. He has led and participated in numerous industrial collaborations with Airbus, Stellantis, Solvay, AKKA, and Farm3. His recent publications (2023–2025) demonstrate a strong focus on human-robot physical interaction, including real-time capacity estimation (Pycapacity), haptic guidance, model predictive control for dynamic environments, and musculoskeletal modeling for collaborative robotics. These works appear in top-tier journals such as IEEE Transactions on Robotics, Journal of Biomechanical Engineering, and Robotics and Autonomous Systems. HDR (Habilitation à Diriger des Recherches) Principal Investigator of ANR Pacbot Head of Science for Inria Center at University of Bordeaux Erdös number = 3 David Daney supervises multiple PhD students, including Alicia Barsacq, Ahmed-Manaf Dahmani, and Alexis Boulay. He has been principal investigator in several research projects such as LiChIE and ANR Pacbot, focusing on satellite production and human-robot collaboration. He also leads the SHAARE associate team with KAIST’s IRiS lab, advancing shared haptic control. His team develops tools for teleoperation, ergonomic analysis, and robot calibration, with applications in industrial and assistive robotics. He leads the Auctus team at Inria, which develops control and analysis techniques for human-robot physical interaction. The team collaborates with KAIST (SHAARE), ONERA, Pprime Institute, and industrial partners. The MOVER project studies human motor variability for ergonomics, and the Farm3 collaboration explores teleoperated vertical farming robotics.

• Cyber-Physical Systems
• Digital Twins
• Industrial Automation
• +3 more

Alexandre PARANT is a Researcher at the University of Reims Champagne-Ardenne, affiliated with the School of Engineering and Digital Tools. His work focuses on cyber-physical systems, digital twins, and industrial automation, with a strong emphasis on the IEC 61499 standard for control architecture development. Research Themes: Model-driven engineering for production systems Digital twin implementation IEC 61499 standard application Modular cyber-physical systems Article Trends: Alexandre's publications span model-based development, robotics synchronization, and PLC identification. His work bridges theoretical modeling with practical automation solutions, particularly in educational contexts and industrial manufacturing. Labs & Teams: LINEACT research team Collaboration with CESI Campus Reims

Yves BoubenecView profile

Associate Professor

• Auditory Neuroscience
• Perceptual Attention
• Sound Encoding
• +3 more

Dr. Yves Boubenec is an Associate Professor at École Normale Supérieure (ENS)-PSL University, Paris, France. He serves as Head of the LSP Neuro Platform and Director of Studies at the Department of Cognitive Studies. Academic Rank: Associate Professor Institution: ENS-PSL Departments: Cognitive Studies (ENS), LSP Neuro Platform Email: yves.boubenec@ens.psl.eu Research Focus: Boubenec investigates neural mechanisms of auditory perception and cognition using integrated methodologies spanning single-neuron electrophysiology to large-scale neuroimaging. His work reveals how context, learning, and multisensory interactions shape sound encoding in mammalian neocortex. Primary Research Themes Context-dependent auditory encoding Perceptual attention mechanisms Task-driven neural plasticity Self-supervised learning models Population-level cortical dynamics Human/ferret auditory comparisons Publication Trends: Recent work (2024-2025) examines speech production networks, premotor auditory categorization, and algebraic structures in sound learning. Earlier studies (2018-2022) focus on population gating, hierarchical auditory coding, and self-voice mechanisms. 2025 Self-voice frequency analysis Hierarchical ferret auditory cortex mapping Temporal window constraints 2024 Premotor category hemodynamics Self-supervised sound structures Human speech cortical encoding Methodological Expertise: Combines awake ferret functional UltraSound, Neuropixels recordings, and computational modeling to analyze neural representations across spatial scales. Specializes in translating animal model findings to human auditory processes.

• Computer Vision
• Facial Expression Recognition
• Deep Learning
• +4 more

Kévin Bailly is a Lecturer at Sorbonne University, affiliated with the Institute of Intelligent Systems and Robotics (ISIR) and part of the Machine Learning and Artificial Intelligence (MLIA) team. His research focuses on computer vision, deep learning, and their applications in facial expression recognition, neural network optimization, and medical imaging. Dr. Bailly's research interests span multiple areas including: Computer Vision and Image Analysis Deep Learning and Neural Network Optimization Facial Expression and Action Unit Recognition Model Compression and Quantization Techniques Medical Applications of Artificial Intelligence His recent publications demonstrate a strong focus on neural network optimization, with particular emphasis on quantization, pruning, and compression techniques that maintain model performance while reducing computational requirements. His work spans both theoretical advancements in deep learning and practical applications in healthcare, human-computer interaction, and affective computing. He has developed novel approaches like PowerQuant for non-uniform quantization, RULe for real-time face alignment in degraded conditions, and RED++ for data-free pruning of deep neural networks. Dr. Bailly has published extensively in top-tier venues including ICLR, NeurIPS, IEEE TPAMI, and IEEE TAC, with a consistent output of high-impact research from 2022-2024. His work bridges theoretical computer vision with practical applications, particularly in medical diagnostics and human-computer interaction systems. He actively collaborates with researchers across multiple institutions, including Arnaud Dapogny, Edouard Yvinec, and Matthieu Cord, and has contributed to interdisciplinary projects that apply AI techniques to medical domains such as fracture classification and obstetrics.

Joanna C. S. SantosView profile

Assistant Professor

• Software Engineering
• Software Security
• Program Analysis
• +4 more

Joanna C. S. Santos is an Assistant Professor at the University of Notre Dame's Department of Computer Science and Engineering. She leads the Security and Software Engineering research lab (S²E) and focuses on Software Engineering, Security, and Program Analysis. Her work bridges empirical studies with practical tool development. PhD in Computing and Information Sciences (Rochester Institute of Technology) M.Sc. in Software Engineering (Rochester Institute of Technology) B.Sc. in Computer Engineering (Federal University of Sergipe) Her research spans Software Security (vulnerability detection, ReDoS), Code Generation (LLM evaluation, benchmarking), and Program Analysis (taint tracking, call graphs). Recent articles show a strong focus on LLM-generated code quality and quantum computing applications. Scientific Awards : 2023 - Distinguished Reviewer (ESEC/FSE) 2020 - Research Pitch Winner (JOBS @MICRO) 2017 - Best Paper (ICSA) 2014 - CAPES Scholarship 2013 - ERBASE 3rd Place She actively contributes to conference committees (OOPSLA, ICSE, SCAM) and collaborates across institutions. Her lab S²E drives research in secure software development and empirical cybersecurity validation.

Slim EssidView profile

Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +7 more

Slim Essid is a Full Professor at Télécom Paris, leading the Audio Data Analysis and Signal Processing (ADASP) group. He holds a Doctorat (Ph.D.) and Habilitation from Université Pierre et Marie Curie (UPMC). With 15+ years of research experience, he has advised 15 PhD graduates and currently co-advises 10 others. His work focuses on machine learning, signal processing, and multimodal systems, publishing over 150 peer-reviewed papers. He serves as a reviewer for top journals/conferences (e.g., IEEE Transactions) and research funding agencies. Education: State Engineering Degree, École Nationale d’Ingénieurs de Tunis (2001) M.Sc. (D.E.A.) in Digital Communication Systems, École Nationale Supérieure des Télécommunications, Paris (2002) Ph.D., Université Pierre et Marie Curie (2005) Habilitation (HDR), UPMC (2015) Research Interests: Multimodal learning, self-supervised representations, audio-visual segmentation, music structure analysis, domain generalization, and speech enhancement. Recent publications highlight innovations like TACO (training-free sound-prompted segmentation) and CLOUDS (domain-generalized semantic segmentation framework using foundation models). His work bridges audio processing with vision and language models, emphasizing unsupervised/zero-shot approaches. Key achievements include state-of-the-art methods in sound event detection, speaker diarization, and music segmentation. He collaborates with 14 post-docs and leads projects funded by French/EU agencies.

• Occupational Health Psychology
• Organizational Psychology
• Psychosocial Risk Prevention
• +5 more

Christine Jeoffrion is a Professor of Occupational Psychology at Université Grenoble Alpes, where she directs the Doctoral School of Human Sciences and leads the "Risk and Change Accompaniment" axis at the Inter-university Psychology Laboratory. With extensive international engagement through organizations like EAOHP and IAAP, her work bridges occupational health, organizational psychology, and psychosocial risk prevention. Key affiliations: Université Grenoble Alpes, Université de Tours, INRS, CNAM Leadership roles: Doctoral School Director (2024+), CNU member (2024-2028), HCERES expert (2023+) Her research focuses on workplace psychological and physical health, examining organizational factors affecting well-being through 15+ funded projects like ANR PacBot (€670k) and CDP BOOT (€850k). Current PhD students explore virtual reality interventions, prison worker coping mechanisms, and organizational transformations for meaningful work. Recent publications analyze cobot impacts on work quality, telework dynamics, and bullying consequences. She has co-edited special issues for International Journal of Environmental Research and Public Health and Psychologie du travail et des Organisations . Her 2023 Légion d'honneur recognition underscores her field leadership. As a thesis advisor, she has directed 34+ doctoral candidates across France and Gabon, including works on intercultural competence, healthcare worker stress, and managerial innovation. She contributes to European research networks through symposium organization and journal editorial boards.

• Autonomous Industrial Vehicles
• Multi-Agent Systems
• Fuzzy Logic
• +7 more

Jean-Marie Bonnin is a Researcher at IMT Atlantique , affiliated with the Network Systems, Cyber Security and Digital Law department. His work spans autonomous industrial vehicles, vehicular networks, and cooperative systems, with a focus on energy management, task allocation, and safety protocols. IMT Atlantique, Rennes Campus Research in Industry 4.0 and Smart Mobility Research Interests : Autonomous Industrial Vehicle Fleets Fuzzy Logic for Multi-Agent Systems V2X Communication Protocols Scientific Contributions include: Modeling energy consumption in extreme-edge IoT nodes Decentralized task allocation for autonomous vehicles Collision avoidance in industrial environments

Benoît LemaireView profile

Lecturer

• Computational Cognitive Modeling
• Working Memory
• Eye Movement Research
• +6 more

Benoît Lemaire is a permanent Lecturer at the University of Grenoble Alpes, affiliated with the Laboratoire de Psychologie et NeuroCognition (LPNC) and the CoMMet team (Consciousness, Memory and MetaCognition). His academic career spans computational cognitive modeling, with a focus on working memory, eye movement research, and educational technology applications. PhD in Computer Science/AI, Université Paris-Sud (1989-1992) Postdoctoral Research: University of Pittsburgh (1993), Swedish Institute of Computer Science (1994) Academic Roles: Maître de conférences (1996-), transitioning through Laboratoire des Sciences de l'Éducation (1994-1996), Laboratoire Leibniz (2004-2006), TIMC (2006-2010), and LPNC (2010-). Lemaire’s research integrates computational modeling with empirical studies across multiple domains: Working Memory : Time-based decay, interference effects, semantic compression, and attentional refreshing mechanisms. Eye Movements : Information search in texts, reading strategies, and visual-semantic integration. Educational Applications : Text assessment, metaphor comprehension, and adaptive learning systems. Inductive Learning : MDL-based models for concept learning and lexical knowledge acquisition. His recent publications (2021–2025) emphasize computational models of mental arithmetic, semantic knowledge impacts on memory, and similarity-based compression techniques. All work aligns with cognitive science and AI methodologies. Current affiliations include: Laboratoire de Psychologie et NeuroCognition (LPNC) – 2010- CoMMet team (Consciousness, Memory and MetaCognition) University of Grenoble Alpes – Permanent Lecturer

Frédéric PASCALView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Adaptive Radar Detection
• +5 more

Frédéric Pascal is a Full Professor at CentraleSupélec, part of the University of Paris-Saclay, and a member of the Laboratoire des Signaux et Systèmes (L2S). His research focuses on statistical signal processing, machine learning, and robust estimation techniques, with applications in radar detection, covariance matrix estimation, and information geometry. He has held roles including Coordinator of AI activities at CentraleSupélec and Head of the "Signals and Statistics" group at L2S. His academic journey includes a PhD from University Paris X – Nanterre (2006) and an HDR (2012) from University Paris-Sud. His work emphasizes adaptive signal processing in non-Gaussian environments, with contributions to robust covariance estimation, M-estimators, and applications in radar systems and biomedical signal processing. He has authored over 100 journal/conference papers and serves as an Associate Editor for IEEE Transactions on Signal Processing and Elsevier Signal Processing. Current research interests include AI transparency, data-driven methods for industry 4.0, and health-related signal analysis.

Sao Mai NguyenView profile

Lecturer

• Robotics
• Artificial Intelligence
• Cognitive Science
• +7 more

Sao Mai Nguyen is an Enseignante-Chercheuse (Lecturer-Researcher) at ENSTA Paris, affiliated with the Unité d'Informatique et d'Ingénierie des Systèmes (U2IS). Her research bridges robotics, artificial intelligence, and cognitive science, focusing on cognitive developmental robotics, intrinsic motivation in learning, and human-robot interaction. She explores how robots can adapt to social and physical environments, particularly in physical rehabilitation and smart home applications. Research Interests: Nguyen’s work integrates machine learning, robotic embodiment, child psychology, and neuroscience. Key areas include human-robot interaction, assistive robotics for chronic low back pain rehabilitation, activity recognition in smart homes using IoT sensors, and intrinsic motivation-driven learning frameworks. Recent Contributions: Her 2024 HDR thesis on reinforcement and imitation learning for sequential tasks underscores her expertise in strategic learning systems. Recent publications address bio-inspired robotics models, hierarchical reinforcement learning, and benchmark environments like 'Open the Chests' for activity recognition. Collaborations include projects like the R-COOL randomized trial for robot-coached physical exercises. Labs & Teams: She contributes to the U2IS lab, advancing interdisciplinary research in AI and robotics. Her work spans experimental platforms for sensorimotor learning and healthcare robotics applications.

Maxime Ferreira Da CostaView profile

Associate Professor

• Signal Processing
• Data Science
• Inverse Problems
• +6 more

Maxime Ferreira Da Costa is an Associate Professor at CentraleSupélec, Université Paris-Saclay, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on theoretical and algorithmic foundations of data science, particularly in inverse problems, structured signal processing, and physical layer security. Key research areas include super-resolution, system calibration, and privacy-enhancing communication schemes. He holds a Ph.D. from Imperial College London (2018), and previously worked at USC and Carnegie Mellon University. Notable achievements include an ANR Young Researcher Grant (2023) for projects on data science and physical layer security. He actively contributes to conferences and workshops, with recent presentations at Institut Henri Poincaré and Université Paris-Saclay. Education: Ph.D. in Electrical Engineering, Imperial College London (2018) M.Sc. Electrical Engineering, Imperial College London (2012) Engineer Diploma, CentraleSupélec (2012) Research interests span continuous inverse problems, off-the-grid methods, wireless security, and sensing systems. Current projects explore goal-oriented resource allocation, fake path injection for privacy, and preconditioned optimization techniques. His work bridges signal processing theory with practical applications in imaging, telecommunications, and sensing.

Antoine MiechView profile

Researcher

• Computer Vision
• Machine Learning
• Natural Language Processing
• +7 more

Antoine Miech is a Researcher at DeepMind's Vision Group , with prior affiliations at Inria and Ecole Normale Supérieure where he completed his computer vision Ph.D. under Ivan Laptev and Josef Sivic . He has collaborated with researchers from Facebook AI and Google during his academic career. Research Interests span video understanding, weakly-supervised machine learning, and multimodal analysis. His work focuses on: Text-video embedding Self-supervised video representation Action localization Anticipatory video modeling Scalable multimodal learning Scientific Contributions include: HowTo100M - A massive dataset of narrated instructional videos MIL-NCE - A novel loss function for video-text alignment MEE - A model for handling heterogeneous data Context Gating - Learnable pooling architecture Awards & Recognition : Google Ph.D. Fellowship (2018) Technical Leadership : Created the LOUPE TensorFlow toolbox for feature pooling and maintained annotated video dataset catalogs. Organized the Data Science Game competition (2016-2017).