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Chao LiuView profile

Researcher

• Surgical Robotics
• Teleoperation
• Haptics
• +2 more

Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.

Hugues AschardView profile

Researcher

• Statistical Genetics
• Genome-Wide Association Studies (GWAS)
• Microbiome Research
• +7 more

Hugues Aschard is a Principal Investigator and Structure Manager at the Pasteur Institute in Paris, where he leads research in statistical genetics, microbiome analysis, and computational genomics. He is the principal investigator of the MicMat project, the EpiGenCOV Consortium, and several bioinformatics software initiatives including JASS, RAISS, and MGMM. Research Interests: Statistical and computational methods in genetics Genome-wide association studies (GWAS) Gene-environment interactions Microbiome and host genetics in inflammatory bowel disease Genetic epidemiology of infectious diseases like COVID-19 Development of open-source tools for multi-trait and summary-statistic analysis Recent Research Trends: His recent publications and projects emphasize integrative genetic modeling, multi-trait analysis across diverse populations, and the development of novel computational methods to handle missing data and improve SNP discovery. His work bridges statistical innovation with biological and clinical applications in complex diseases. Scientific Contributions: Development of JASS, RAISS, and MGMM software tools Leadership in large-scale consortia like EpiGenCOV Advancing methods for cross-ancestry genetic studies Advising and Collaboration: He supervises multiple PhD students and postdoctoral fellows, including Christophe Boetto, Antoine Auvergne, and Lucas Chataigner. He collaborates with major institutions such as APHP and CNRGH. His team includes research engineers and administrative staff, indicating an active and well-supported research group. Laboratories and Teams: He is a key member of the Biomaterials and Microfluidics team at the Pasteur Institute, where he contributes to interdisciplinary research involving Bayesian decision processes and genetic modeling.

• Artificial Intelligence
• Quantitative Marketing
• Data Science
• +7 more

Julien Cloarec is a Full Professor of Quantitative Marketing at iaelyon School of Management, Université Jean Moulin Lyon 3, where he also serves as Vice-President for Digital Strategy and Artificial Intelligence. He is an internationally recognized expert in artificial intelligence, with a focus on responsible AI development that balances innovation with privacy protection. His research interests center on Artificial Intelligence , Privacy in AI , Consumer Behavior , and Digital Marketing . He investigates how users perceive and adopt AI technologies, particularly in domains like autonomous vehicles, brain–machine interfaces, and eHealth, emphasizing the psychological and ethical dimensions of trust and privacy. His recent publications (2022–2025) reflect a strong thematic focus on the personalization-privacy paradox , algorithmic aversion , and transparency in AI systems . These works appear in leading journals across marketing, technology, and transportation research, demonstrating interdisciplinary impact. Scientific Awards: Prix AFM - Association Française du Marketing pour la meilleure communication (2022) Julien Cloarec actively collaborates with regulatory bodies, professional associations, and academic institutions to influence public policy and promote responsible AI. He leads research at the Magellan Laboratory @iaelyon and manages educational initiatives such as the Certificate in Artificial Intelligence for Marketing . He advises on doctoral research and has contributed to publications on the doctoral experience in management. Laboratory Affiliation: Magellan Laboratory @iaelyon - Research in Organizational Management

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.

• Linguistics
• Dialogue Semantics
• Pragmatics
• +4 more

Jonathan Ginzburg is a Researcher in the Department of Linguistics at Université Paris Diderot, affiliated with the CLILLAC-ARP research group. His work bridges linguistics, cognitive science, and computational models, focusing on dialogue semantics, pragmatic functions, and developmental language acquisition. Education details are not explicitly provided in the text, but his research spans formal semantics, syntax, and multimodal interaction. His studies on laughter dynamics and infant communicative behaviors have advanced understanding of pragmatic functions in early development. Key research interests include dialogue structure, question-response systems, and the integration of cognitive science with linguistic theory. Recent work explores neural correlates of language processing through type-theoretical semantics and corpus-based methodologies. He has pioneered the analysis of non-sentential utterances and their role in social interaction. His contributions to dialogue systems design for low-resource languages and the DUEL corpus (analyzing disfluencies, exclamations, and laughter) reflect his interdisciplinary approach. Current projects investigate laughter's pragmatic functions across developmental stages and cross-linguistic variations in exclamatory constructions. Laboratory affiliations include the CLILLAC-ARP laboratory, where he develops computational tools for analyzing dialogue semantics and cognitive interactions. His work on 'response spaces' for questions integrates machine learning and formal pragmatics to model conversational coherence.

• Human-Machine Interaction
• Neuroergonomics
• Physiological Computing
• +3 more

Raphaëlle N. Roy is a Professor at ISAE-SUPAERO, part of the University of Toulouse, France. Her academic career spans multiple institutions, including the Gipsa-Lab in Grenoble and the University of British Columbia. She specializes in Neuroergonomics and Physiological Computing, focusing on Human-Machine Interaction in aerospace contexts. PhD in Cognitive Neuroscience & Signal Processing, Université Grenoble Alpes/CEA-Leti (2015) Habilitation à Diriger des Recherches (HDR), Université de Toulouse (2022) Certified Lecturer in Neuroscience (2016) Her research interests include Neuroergonomics , Brain-Computer Interfaces , and Neuroadaptive Technology , with applications in aerospace systems. She leads the Neuroergonomics and Human Factors research group and coordinates the Certificate in Human Factors & Neuroergonomics for Aeronautics and Transportation . Roy has organized major conferences like the International Neuroergonomics Conference (Paris 2016, Munich 2021, Bordeaux 2024) and co-founded the French BCI association CORTICO , serving as Vice-President since 2023. She is an Associate Editor for Frontiers in Neuroergonomics and has reviewed for over 30 journals and conferences, including NeurIPS and IEEE Transactions. Ministerial fellowship for PhD thesis She supervises PhD and Master’s students, including Mathias Rihet , Elena López-Contreras González , and Pauline Dreyer , and collaborates with institutions like INRIA, TU Delft Robotics Institute, and LAAS-CNRS. Her work is supported by ANR, ESA, and CNES grants, including the EPIIC project (ANR).

• Human-robot physical interaction
• Elderly people
• Smart-walker
• +3 more

Ludovic Saint-Bauzel is a lecturer at Sorbonne University and head of the IRIS team at the Institute of Intelligent Systems and Robotics (ISIR). His work focuses on improving physical human-robot interaction for individuals with autonomy loss through disability or aging. Specializes in computational models of disabilities Develops user intent detection through sensor fusion Active in IFRH and Fedrha federations IEEE and True Life Lab member Research interests His research centers on Human-robot physical interaction with applications in Elderly care , Smart-walker development, and Walking exoskeleton systems. Key methodologies include: Sensor fusion (depth cameras, force sensors, IMUs) Adaptive robot control systems Pathological movement modeling Motor intent prediction algorithms Article trends show consistent focus on haptic communication (6/15), assistive robotics (9/15), and sensorimotor interaction (11/15) across 2013-2022 publications. Laboratory involvement : Leads the IRIS team at ISIR, part of Fedrha (Federation for Research on Disability and Autonomy) with 50+ research teams.

Christophe HurterView profile

Researcher

• Artificial Intelligence
• Data Visualization
• Augmented Reality
• +9 more

Christophe Hurter is a Researcher at the National School of Civil Aviation , specializing in Artificial Intelligence , Data Visualization , and Human-Computer Interaction . His work bridges Aviation Technology , Neuroscience , and Extended Reality (XR) , focusing on enhancing human performance through AI-driven systems. Affiliation: National School of Civil Aviation (Faculty Member, AI axis) Research Interests include: Explainable AI for aviation systems Neurophysiological monitoring using thermal imaging Eye tracking and cognitive processes in memory retrieval Graph and trajectory visualization for air traffic control Augmented/Virtual Reality interfaces for remote collaboration Event-based vision systems for spiking neural networks Recent article trends highlight his expertise in machine learning for medical diagnostics , XR applications in aviation , and visual analytics for human factors research . His work often addresses real-world challenges in air traffic control, piloting training, and biomedical data interpretation.

Hugues MOUNIERView profile

Professor

• Control Theory
• Differential Flatness
• Controllability Analysis
• +5 more

Professor Hugues MOUNIER is affiliated with Paris Saclay University and the Laboratoire des Signaux et Systèmes (L2S) at CentraleSupélec. His primary role is as a Professor leading the COMEDY team, focusing on control systems and their applications. He has been a permanent member of L2S since 2010 and joined Paris Saclay University in 2008. His research spans theoretical and applied domains: differential flatness (for nonlinear and infinite-dimensional systems), Liouvillian systems, controllability/observability properties, and applications in contemplative neuroscience, physiological systems, and engineering systems like drilling, automotive, and network control. He leads the ANR project MindMadeClear, integrating mathematical models for meditation practices with physiological and phenomenological analysis. Key contributions include modeling oilwell drilling vibrations, model-free control strategies for vehicles and data centers, and neuroscientific studies using EEG classification for meditation states. His work bridges abstract theory (e.g., flatness analysis for HPA axes) with real-world applications (e.g., congestion control, energy management). He collaborates internationally, presenting at venues like ECC, IFAC, and IEEE conferences. His lab teams include SYCOMORE and MODESTY, focusing on dynamical systems modeling and estimation. No awards are explicitly listed, but his extensive peer-reviewed publications reflect sustained academic impact.

• Signal Processing
• Remote Sensing
• Geospatial Data Analysis
• +5 more

Alexandre Hippert-Ferrer is an Associate Professor at Université Gustave Eiffel, where he is a permanent member of the LASTIG lab working in the Strudel team. He primarily teaches at the National School of Geographic Sciences (École Nationale des Sciences Géographiques). Dr. Hippert-Ferrer received his PhD from University Savoie Mont Blanc, Annecy, France in 2020, supervised by Philippe Bolon and Yajing Yan. His doctoral research focused on reconstruction of missing data in displacement time series by remote sensing. Following his PhD, he was a post-doctoral researcher at L2S (Université Paris-Saclay) working with Florent Bouchard and Frédéric Pascal. He maintains active collaborations with researchers including Arnaud Breloy and Nabil El Korso from LEME at University of Paris Nanterre. Hippert-Ferrer's research focuses on robust statistical methods for signal processing, particularly for geospatial data with missing values. His work bridges the fields of remote sensing, signal processing, and statistics, with a strong emphasis on practical applications in earth observation and geodesy. He has developed innovative approaches using Empirical Orthogonal Function (EOF) analysis, Expectation-Maximization (EM) algorithms, and Riemannian geometry for handling incomplete data in displacement measurement time series. His methods have been applied to Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) data for monitoring ground displacement. His publication record shows a clear progression from basic gap-filling techniques to sophisticated statistical models. Starting with EOF-based approaches, his work has evolved to incorporate advanced methods including elliptical distributions, low-rank covariance estimation, and graphical factor models with Riemannian optimization. The consistent theme across his publications is developing mathematically sound yet practical algorithms for real-world geospatial data challenges, particularly when data is incomplete or corrupted. Dr. Hippert-Ferrer has made significant contributions through publications in top journals including IEEE Transactions on Signal Processing and IEEE Transactions on Geoscience and Remote Sensing. His open-source implementation of the EM-EOF algorithm on GitHub demonstrates his commitment to reproducible research. As an educator at the National School of Geographic Sciences, he trains the next generation of geospatial professionals, bridging theoretical statistical methods with practical applications in earth observation.

Jean-Baptiste EichenlaubView profile

Associate Professor

• Cognitive Neuroscience
• Sleep Research
• Neurophysiology
• +3 more

Jean-Baptiste Eichenlaub is an Associate Professor at Université Savoie Mont Blanc (USMB), affiliated with the Laboratoire de Psychologie et NeuroCognition (LPNC). Previously, he held postdoctoral positions at Massachusetts General Hospital/Harvard Medical School and Swansea University. His work spans sleep research , cognitive neuroscience , and neural correlates of dreaming , with a focus on memory consolidation , dream recall frequency , and REM sleep mechanisms . Ph.D. in Cognitive Neuroscience (Lyon Neuroscience Research Center, 2011) Master of Physiology & Neuroscience (Lyon University, 2008) Bachelor of Biology (Lyon University, 2006) His research integrates neuroimaging , electrophysiology , and computational tools , exemplified by contributions like the DREAM EEG database and Spinky toolbox for sleep analysis. Recent publications examine sleep habits in preteens , dream-lag effects , and gamma oscillations in NREM sleep . Notable roles include: Junior member, Institut Universitaire de France (2024 - present) Co-responsible - Memory Team (2021 - present) Coordinator - 1st Year Bachelor's in Psychology (2021 - 2024)

Thierry ArtièresView profile

Professor

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +6 more

Thierry Artières is a University Professor at Aix-Marseille University, primarily affiliated with École Centrale Marseille (ECM), where he holds multiple leadership positions including Head of the Computer Science teaching unit, Head of the IAAA course of the Computer Science Master's degree, and Head of the IAM course of the 3rd year Computer Science option. He is a key member of the QARMA (Machine Learning) research team within the LIS (Laboratoire d'Informatique et Systèmes) and collaborates with several research institutes including the READ laboratory, Institut de Neurosciences de la Timone (INT), and the ILCB (Institute of Language, Communication and the Brain). His research interests span Machine Learning, Deep Learning, and Artificial Intelligence with applications to neuroscience, medical imaging, and computational biology. His work focuses on understanding brain representations of voice and sound, optimizing MRI acquisition through deep learning, and developing novel machine learning techniques for multi-label classification and generative modeling. He has supervised numerous PhD students including Loris Berthelot, Hamed Benazha, Malek Senoussi, Swetali Nimje, and Charly Lamothe. His recent publications reveal a strong focus on applying deep learning to neuroscience problems, particularly in understanding how the brain processes sound and voice. His work combines theoretical machine learning advances with practical applications in medical imaging and cognitive neuroscience, often through collaborations between computer science and neuroscience laboratories. His research demonstrates a consistent trend toward interdisciplinary work that bridges AI with biological and medical domains. Member of QARMA Machine Learning team at LIS Collaborator with Institut de Neurosciences de la Timone Involved with ILCB Institute (Institute of Language, Communication and the Brain) Supervisor of multiple PhD students and Master's interns Regularly posts about internship opportunities in Machine Learning and AI Professor Artières actively mentors students through PhD positions, Master's internships, and engineering student projects. He has secured funding for multiple research projects, including ANR-funded collaborations with neuroscience institutes. His lab regularly offers 5-6 month internships on cutting-edge topics in machine learning, and he has facilitated numerous research opportunities for students interested in AI and data science careers. He also contributes to understanding the French job market for AI and data science professionals.

Caroline ChanelView profile

Professor

• Autonomous Systems
• Decision-Making Algorithms
• Robotics
• +3 more

Caroline Chanel is a Professor at ISAE-SUPAERO (DCAS), holding the CASAC Chair since 2022. She has extensive experience in research and teaching, with roles including Associate Professor (2019-2024), Research Engineer (2013-2019), and HDR accreditation (2023). Her research focuses on embedded decision-making for autonomous systems, including proactive architectures, multi-robot cooperation, and human-machine interaction. She leads and participates in high-profile projects like SAMURAI (AID), CHIP-GT (ANR-DFG), and HAICO (CASAC Chair). She has supervised numerous PhD students and post-doctoral researchers, collaborating with institutions like ONERA, INRAe, and international partners. Education: PhD in Control and Embedded Systems (University of Toulouse 2013), Master's in Automatic Systems (Université Paul Sabatier 2008-2009), and Engineer Diploma from PUCRS (2002-2008). Awards include the Amelia Earhart Fellowship (2011) and ISAE-SUPAERO Foundation PhD Fellowship (2013). Research interests span decision-making algorithms, multi-agent systems, and neuroergonomics. Key projects address UAV navigation, blockchain integration for autonomous systems, and physiological computing for human-robot interaction. Teaching responsibilities include modules on autonomous systems, control systems, and neuroergonomics at ISAE-SUPAERO's engineering and master's programs. She co-leads the DECISIO doctoral team (ISAE-ONERA) and is involved in executive roles for conferences like EDSYS'.

Jing-Rebecca LiView profile

Professor

• Diffusion Magnetic Resonance Imaging
• Numerical Methods for Partial Differential Equations
• Numerical Linear Algebra
• +4 more

Jing-Rebecca Li is a Professor and Research Scientist at ENSTA Paris, affiliated with the Applied Mathematics Unit (UMA) and INRIA Saclay as part of the IDEFIX research team. Her work bridges advanced mathematical techniques with medical imaging applications, particularly in diffusion MRI. She maintains a dual affiliation between ENSTA Paris, a leading engineering school in France, and INRIA, the French national research institute for digital science and technology. HDR (Habilitation à Diriger des Recherches) in Mathematics, Université Paris-Sud, 2013 Ph.D. in Mathematics, Massachusetts Institute of Technology, 2000 B.Sc. in Mathematics, University of Michigan, 1995 Dr. Li's research focuses on developing sophisticated numerical methods to solve partial differential equations with applications in diffusion magnetic resonance imaging. Her work spans brain and cardiac imaging, numerical linear algebra, machine learning algorithms for inverse problems in PDEs, and natural language processing tools. She has pioneered approaches to simulate diffusion MRI signals in complex biological tissues, enabling more accurate interpretation of imaging data for neuroscience and cardiology applications. Her research has significant implications for understanding brain microstructure and cardiac tissue organization through non-invasive imaging techniques. Her recent publications demonstrate a clear trend toward increasingly sophisticated modeling of biological tissues, with growing emphasis on cardiac applications alongside her foundational work in brain imaging. She has developed robust computational frameworks that incorporate permeable interfaces, geometrical deformations, and realistic neuronal geometries to better simulate diffusion MRI signals. Her work increasingly integrates machine learning with traditional numerical methods, creating hybrid approaches that leverage the strengths of both paradigms for microstructure estimation. Householder Prize for the best dissertation in Numerical Algebra (2002) Dr. Li has supervised numerous doctoral students across multiple institutions, with a focus on computational methods for diffusion MRI. Her current research is supported by significant grants including the Engineering for Health (E4H) interdisciplinary center project investigating biomarkers for Multiple Sclerosis through diffusion MRI (2023-2025). Previously, she led the ANR-funded SIMUDMRI project (2010-2014) and participated in the US-French Collaboration project on Computational Imaging of the Aging Cerebral Microvasculature (2013-2016). Her work demonstrates strong interdisciplinary collaboration between mathematics, computer science, and medical imaging communities. As leader of the IDEFIX research team at INRIA Saclay, Dr. Li directs a group focused on inversion methods for differential equations applied to imaging and physics problems. Her team has developed the SpinDoctor software package, a widely used MATLAB toolbox for diffusion MRI simulation that has become a standard tool in the field. The team maintains strong collaborations with Neurospin (CEA) and international research groups working on advancing diffusion MRI methodology and applications.

Georgi StojanovView profile

Associate Professor

• Artificial Intelligence
• Cognitive Robotics
• Developmental Robotics
• +4 more

Georgi Stojanov is an Associate Professor at The American University of Paris since Fall 2005, previously serving as associate professor at the Faculty of Electrical Engineering, Sts Cyril and Methodius University in Skopje (2001-2005). He co-founded the Cognitive Robotics Group (2000) and the Institute for Interactivist Studies, actively organizing the bi-annual Interactivist Summer Institute (ISI). His academic credentials include: PhD in Computer Science and Artificial Intelligence (1997), University SS Cyril and Methodius, Skopje MSc in Computer Science (1992), Thesis: 'Recognition and Modeling of Evoked Brain Potentials', University SS Cyril and Methodius, Skopje BS in Electrical Engineering (1988), Faculty of Electrical Engineering, University 'Sts. Cyril and Methodius', Skopje (Computer Science major) Stojanov's research spans Artificial Intelligence with emphases on Cognitive Robotics , Developmental Robotics , and Computational Creativity . His work investigates interactivist theory, metaphor/analogy mechanisms in AI, and embodied cognition frameworks. Key contributions include modeling attention architectures, curiosity-driven robotic systems, and Piagetian-inspired developmental models, consistently bridging cognitive science with practical robotics applications. His focus on perceptual similarity in creative processes represents a significant thread in recent publications. Publication trends (2005-2013) reveal a progression from foundational interactivism theory (2006) toward computational creativity (2010-2013), with sustained exploration of embodied learning through inductive logic programming. His work demonstrates strong interdisciplinary connections between machine learning, cognitive development, and human-computer interaction. He maintains active leadership through program committee roles for AI/robotics conferences and has delivered invited lectures at institutions including Université Montpellier 3, Université Paris-8, and the New Bulgarian University. His 2013 AAAI symposium on Creativity and Cognitive Development highlights ongoing scholarly engagement. Stojanov's collaborative network includes the Cognitive Robotics Group (Skopje), Institute for Interactivist Studies, and international partners at University of Trieste, Université de Versailles-Saint-Quentin-en-Yvelines, and UC San Diego, reflecting his commitment to cross-institutional cognitive systems research.