Thierry Badard is an Associate Professor at the Department of Geomatics Sciences , Université Laval, where he also serves as Director of the Center for Research in Geospatial Data and Intelligence (CRDIG) . With over 28 years of experience in geospatial science, he leads research initiatives at the intersection of GeoAI , LiDAR processing , and smart city technologies . Director, CRDIG (2016-2022) Steering Committee Member, Big Data Research Centre (CRDM) Researcher, Institute for Intelligence and Data (IID) Research Expertise spans geospatial big data, GeoNLP, and IoT applications for digital twins. His work addresses flood risk modeling , 3D urban analytics , and environmental monitoring through AI-driven solutions. Recent publications focus on contrastive learning for LiDAR segmentation and geospatial ontologies for early warning systems. Grant Leadership includes collaborative projects on smart insurance analytics (2018-2025), Arctic bioaerosol research (2019-2025), and Quebec-Morocco digital twin partnerships (2022-2023). He has advised 15+ graduate students in geomatics and related fields.
Jacques Gautier is an Assistant Professor in Geovisualization at LASTIG, part of the French National Geographic Institute (IGN France) since September 2020. He is a member of the GEOVIS research team focusing on advanced geovisualization techniques for spatio-temporal data analysis. Prior to his current position, he served as a Postdoctoral Researcher at LASTIG working on the Urclim European project, developing geovisualization methods for climate data in urban environments. His educational background includes a PhD in Geography from Université Grenoble Alpes (2015-2018), where his dissertation focused on "GrAPHiST: An exploratory analysis approach for identifying the dynamics of spatio-temporal phenomena," and an Engineering degree in Geographical Information Science from ENSG (2009-2012). Dr. Gautier's research focuses on innovative approaches to visualize complex spatio-temporal data across multiple domains. His expertise spans meteorological data visualization, epidemiological data visualization, 2D/3D geovisualization techniques, and exploratory data analysis of spatio-temporal phenomena. He has developed specialized methods for identifying cyclic patterns in time-series data, visualizing uncertainty in ensemble forecasting systems, and creating interactive visualization environments for domain experts in urban planning, public health, and emergency response. Analysis of Dr. Gautier's publication record reveals a consistent focus on developing visualization techniques that bridge theoretical advances with practical applications. His work spans urban climate analysis, pandemic response (particularly during COVID-19), and mountain rescue operations. A distinctive aspect of his research is the integration of harmonic analysis with visual exploration to identify cyclic patterns in spatio-temporal data, as demonstrated in his GrAPHiST framework. Dr. Gautier has been actively involved in several significant research projects including ORACLES (focusing on ensemble forecasts of marine submersion), Urclim (aiming to develop integrated Urban Climate Services), and Choucas (an interdisciplinary project to assist mountain rescue operations). These projects highlight his ability to translate visualization research into practical decision-support tools for critical situations. As a member of the GEOVIS research team, Dr. Gautier contributes to advancing geovisualization methodologies through both theoretical development and practical implementation. His work on mixed temporal diagrams, helical time representations, and uncertainty visualization has provided new approaches for exploring complex spatio-temporal datasets across multiple disciplines.
Hani Hamdan is a Professor of Electrical Engineering and Computer Science at CentraleSupélec, part of Université Paris-Saclay. He holds a PhD from Université de Technologie de Compiègne and has held roles including Research Engineer at CETIM, Researcher at CNRS, and Assistant Professor at Sorbonne-Paris-Nord. His research focuses on machine learning, signal processing, robotics, and telecommunications, with contributions to biomedical engineering and sustainable technologies. He leads the COMEDY and MODESTY research groups within the L2S laboratory. Education: PhD in Systems and Information Technologies, Université de Technologie de Compiègne (2005) MSc in Industrial Control, Université Libanaise/UTC (2001) Engineering Diploma in Electricity & Electronics, Université Libanaise (2000) Research Interests: Machine learning for healthcare and environmental applications Signal processing in robotics and communication systems Intelligent communication networks (FSO, IoT security) Data-driven analysis of biomedical systems and civil infrastructure Key Contributions: Innovative clustering algorithms for big data (e.g., bin-EM-CEM) FSO communication system optimizations Robotics rehabilitation systems using ML-enhanced biomechanics Awards: Best Paper Award for Big Data clustering (2018) Outstanding conference organization award (DeSE 2017) CIFRE doctoral scholarship (2002-2005) Lab Affiliations: Laboratoire des signaux et systèmes (L2S), collaborating with CNRS and CentraleSupélec on interdisciplinary projects in signal processing and automated systems.
Djalil CHAFAÏ is a University Professor of Mathematics at Université Paris-Dauphine - PSL, with dual affiliation at CEREMADE (Centre de Recherche en Mathématiques de la Décision) and DMA (Département de Mathématiques et Applications) at École normale supérieure (Paris) - PSL. He currently serves as Directeur des études du DMA (2021-2026) and Directeur scientifique du RNBM (2021-2025). His extensive research spans multiple areas of probability theory, mathematical physics, and applied mathematics. CHAFAÏ's research interests center around geometric and probabilistic functional analysis, random matrices, random graphs, free probability, and high-dimensional phenomena. His work connects mathematical theory with applications in biology, physics, and data science. He has made significant contributions to understanding cutoff phenomena in high-dimensional diffusions, Riesz energy problems, and Coulomb gases. His research often combines theoretical analysis with visual illustrations created using computational tools like Octave, Python, and Julia. Analysis of his recent publications reveals a strong focus on cutoff phenomena in various stochastic processes, equilibrium measures in potential theory, and the mathematical properties of random matrix ensembles. His work demonstrates a consistent pattern of bridging abstract mathematical concepts with concrete physical phenomena, particularly in statistical physics. The interdisciplinary nature of his research is evident in the diverse applications ranging from mathematical biology to data science. CHAFAÏ has successfully advised numerous doctoral students whose work continues to influence the field. His current doctoral students include Samuel Chan-ashing, Rémi Bonnin, and Kewei Pan, while his former students have gone on to positions at prestigious institutions worldwide. He is actively involved in the mathematical community through organizing conferences, seminars, and workshops, including the Matrices Et Graphes Aléatoires (MEGA) project and the Conviviality project funded by ANR.
Christophe Brun is an Associate Professor at Laboratoire des Écoulements Géophysiques et Industriels (LEGI) , Université Grenoble Alpes, specializing in geophysical fluid dynamics and turbulence modeling. His research focuses on katabatic winds, atmospheric boundary layers, and Görtler instability through field experiments and numerical simulations. Key Research Areas: Katabatic Flow Dynamics Large Eddy Simulation (LES) Stably Stratified Turbulence Mountain Meteorology Recent Publications (2024): Analysis of turbulent boundary layers in alpine katabatic flows Wave turbulence evidence and Bolgiano spectra 3D velocity measurement techniques Technical Affiliation: Member of the MEIGE team at LEGI, working with advanced rotating platforms like Coriolis for geophysical flow experiments.
Berill Blair is an Associate Professor of Resource Governance and Sustainability at SKEMA Business School, focusing on innovation and change management related to natural resource governance, climate change adaptation, and community resilience in Arctic regions. Her research bridges climate information, technological systems, stakeholder strategies, and policy through co-production of solutions with academia, institutions, businesses, and local communities. PhD in Natural Resources and Sustainability (University of Alaska Fairbanks, 2017) Master of Arts in Global Environmental Policy (University of Alaska Fairbanks, 2010) Bachelor of Science in Computer Science (Western Oregon University, 2002) Her research interests span: Climate change adaptation and Arctic governance Co-production of knowledge in climate services Risk perception and social-ecological systems Responsible innovation in marine sectors Stakeholder engagement in environmental policy Simulation-based tools for climate decision-making Scientific awards : Resilience and Adaptation Program Fellowship (2012) Funding : Research Council of Norway (2020) National Science Foundation (2014) Berill actively organizes academic events including GRONEN 2024, ISAGA 2023 workshops, and European Geosciences Union sessions. She serves as a reviewer for Weather, Climate and Society and Ecology and Society .
Richard Alligier is a lecturer and researcher at Ecole Nationale de l'Aviation Civile (ENAC) specializing in artificial intelligence applications for air traffic management. His work bridges machine learning, optimization algorithms, and trajectory prediction to address critical challenges in conflict detection and resolution for both manned and unmanned aerial systems. Research Focus : Trajectory prediction, conflict resolution, UAV collision avoidance, mass/thrust estimation, and uncertainty modeling Key Collaborations : Nicolas Durand, David Gianazza, Xavier Olive, Kim Gaume, Sarah Degaugue His publications (2011–2024) analyze trajectory uncertainty quantification, 3D maneuver visualization, and human-aligned deconfliction strategies using ADS-B data. Notable contributions include: Dual-horizon collision avoidance algorithms integrating human factors High-confidence interval prediction frameworks Wind parameter extraction from flight paths Machine learning models for climb/descent phase optimization Awarded the 2019 best paper in trajectory prediction , his work emphasizes operational alignment between automated systems and air traffic controller decision-making. He employs GPU acceleration, metaheuristics, and deep learning architectures while maintaining a focus on practical implementation through partnerships with ONERA and ISAE-SUPAERO.
Jérôme DANTAN is an Associate Professor in Computer Science affiliated with the Agrobiosciences Department, part of the Business and Engineering Sciences faculty. He is a member of the INTERACT research unit , focusing on systems engineering and decision models in uncertain contexts. His work integrates computational methods with applications in smart farming and digital agriculture. He holds a PhD in Computer Science from the Conservatoire National des Arts et Métiers (CNAM, Paris) and an engineering degree from ENSEA (Cergy-Pontoise). His research emphasizes decision-making frameworks for imperfect data, including applications in agricultural systems and environmental monitoring. Key areas include the Choquet integral for multi-factor prediction, fuzzy decision support systems (e.g., Decifarm), and the impact of digital technologies on farming business models. He co-directs the MSc Agricultural and Food Data Management program and collaborates with institutions like CNAM Paris and ISAMM Tunis. Recent projects include data visualization tools for industrial maintenance systems at Sogitec Industries (Dassault Group) and participatory innovation bootcamps focused on farmer-centered technology adoption. His work bridges computational methods, agricultural practices, and stakeholder engagement to address challenges in sustainable socio-environmental systems. Professional activities span teaching, research, and consulting. Notable partnerships include collaborations with the Institut Supérieur des Arts Multimédia de la Manouba (Tunisia) and industry engagements in distributed systems, Big Data, and machine learning applications.
Dr. Axel Hutt is a senior researcher (Directeur de Recherche) at INRIA Grand Est in Strasbourg, France, leading the MIMESIS team. His work focuses on neural systems modeling, nonlinear dynamics, and data assimilation in complex systems. He holds a PhD from the University of Stuttgart (2001) and an HDR (Habilitation) from the University of Nice (2013). Hutt’s research bridges neuroscience, mathematics, and engineering, with contributions to anesthesia modeling, neurostimulation, and EEG analysis. He has held roles at the Max Planck Institute, Humboldt University Berlin, and the University of Ottawa, and received the Schloessmann Fellowship (2000) and ERC Starting Grant (2011). His current projects include PhD supervision of T. Nette and participation in high-profile conferences like Re:publica and ICMNS. Recent work explores myelination effects, closed-loop neurostimulation, and AI ethics. Education: Physics Diploma (Stuttgart, 1997), PhD (Stuttgart, 2001), HDR (Nice, 2013) Key Roles: INRIA Team Leader (MIMESIS, 2019–present), NeuroSys Team Head (2013–2015) Research Themes: Neural field theory, stochastic processes, clinical neurostimulation applications Publications: Over 180 peer-reviewed articles across journals like Communications Physics and PLoS Computational Biology , focusing on noise-driven dynamics, EEG modeling, and computational psychiatry. Awards: ERC MATHANA Grant (2011), Schloessmann Fellowship (2000)
Shengkai Zhang is an active researcher with 26 publications and 444 citations spanning engineering, computer science, and environmental disciplines. His work demonstrates strong interdisciplinary collaboration through co-authorship with researchers like Kezhong Liu and Mozi Chen across multiple high-impact venues including IEEE conferences, arXiv, and specialized journals. His research interests center on Machine Learning applications in maritime systems , with significant contributions to Large Language Model integration for ship navigation, wireless sensing for bridge officer monitoring, and sensor fusion techniques. Additional expertise spans robotics perception (visual-inertial systems, mmWave radar enhancement), environmental modeling (urban energy systems, climate studies), and biomedical applications of traditional medicine. Recent work shows increasing focus on AI foundation models and their security implications. Zhang's publication trajectory reveals consistent output with accelerating impact since 2023, featuring 15+ papers in 2024 alone. His research clusters around three core themes: Maritime AI Systems (LLM navigation, track association, watchkeeping monitoring) Advanced Sensing Technologies (mmWave radar, Wi-Fi sensing, GNSS fusion) Environmental & Biomedical Applications (urban energy modeling, gut microbiome studies) These areas demonstrate both technical depth in signal processing/computer vision and practical focus on real-world engineering challenges.
Pascal Gaillard is an Associate Professor at the University of Toulouse - Jean Jaurès, where he conducts research at the CLLE Lab (Cognition, Languages, Language, Ergonomics, UMR5263) in the "Language and Cognitive Processes" Team, while teaching in the Music Department at the National Higher Institute for Teaching and Education in Toulouse - Midi-Pyrénées (INSPE). His academic career spans over two decades, beginning as an Assistant Professor in Musicology at Université de Toulouse - Le Mirail from 1998 to 2000, before becoming an Associate Professor at the University of Toulouse since 2002. His educational background includes: PhD in Musicology and Auditory Perception (1996-2000) from Université de Toulouse - Le Mirail / Université de Paris - Jussieu Master (DEA) in Musicology - Ethnomusicology (1993-1994) from Université de Toulouse - Le Mirail Maîtrise in Musicology - Ethnomusicology (1989-1990) from Université de Toulouse - Le Mirail Licence in Musicology (1985-1989) from Université de Toulouse - Le Mirail Dr. Gaillard's research focuses on auditory perception and cognitive processes, particularly the categorization of sounds. His work spans multiple domains including musical timbre perception, speech perception in individuals with age-related hearing loss, environmental sound recognition, and auditory processing in deaf individuals with cochlear implants. He has pioneered studies on how humans organize their sound world through categorization, drawing on prototypical categorization theory developed by Rosch (1976). A key insight from his research is that auditory categorization is dynamic rather than fixed, varying according to listener needs, tasks, and action goals. His recent publications demonstrate a strong interdisciplinary approach, bridging music cognition, clinical audiology, and cognitive neuroscience. There's a clear trend toward applied research with clinical implications, particularly for deaf children with cochlear implants, as evidenced by multiple studies on humanoid robots for speech-language training. His work also shows increasing integration of computational approaches, including transfer learning for music preference prediction and ontology-based data management. Dr. Gaillard has secured numerous research grants from prestigious funding bodies including the French National Research Agency (ANR), Occitanie Regional Council, and ANSES. Current projects include "The hospital and its 'Beeps': evaluation of the hearing health of caregivers" (2025-2028), "SILENCE - Comparative acoustics: earth, planets and extreme environments" (2024-2026), and "Rehabilitation of age-related hearing loss: evolution of cognitive load in a 3D virtual environment - AgeHear" (2020-2024). He directs the "Cognition, Behavior and Use" facilities (CCU) which include specialized auditory booths for research. Since 2003, he has developed TCL-LabX, experimental software for conducting categorization studies. His research collaborations span international boundaries, working with labs in France, Canada, and the Netherlands, as well as industry partners in aeronautics and healthcare sectors.
Thomas Leduc is an Associate Professor at Nantes Université, affiliated with the École Nationale Supérieure d'Architecture de Nantes and the UMR AAU CNRS 1563 research unit. He has been a key figure in urban climate and geospatial research, serving as Director of the CRENAU research team (2015-2019) and Deputy Director of UMR AAU (2014-2019). Currently, he remains an active researcher and member of the Council of UMR CNRS 1563 through 2025, while also contributing to the Scientific Council of École de Design Nantes Atlantique and serving on the Doctoral School Council SIS. Professor Leduc's research focuses on the intersection of urban climate science and geospatial analysis, with particular emphasis on urban thermal comfort, pedestrian mobility, and visibility analysis in urban environments. His work combines advanced GIS methodologies with practical urban planning applications, developing innovative approaches to measuring and modeling urban microclimates. He has made significant contributions to understanding how urban form affects thermal comfort and has developed specialized techniques for analyzing urban visibility using isovists and wavelet transforms. His recent publications (2022-2025) demonstrate a strong research trajectory in urban climate modeling, with a focus on practical applications for climate adaptation. The publications reveal a consistent pattern of interdisciplinary collaboration, particularly with researchers specializing in environmental science, urban design, and computer vision. His work increasingly integrates mobile measurement techniques with geospatial frameworks to provide more nuanced understandings of urban microclimates at pedestrian level. Active participant in multiple research projects including Coolscapes, ResilientGAIA, and Lunne Coordinator of the Urbaclim research action of the GDR MAGIS Organizer of significant scientific events including SCAN'18 and Vu-pas-vu-2017 Co-supervisor of numerous doctoral students working at the intersection of urban climate and geospatial analysis Professor Leduc's research group maintains strong connections with both academic and professional communities, regularly collaborating with urban planners, architects, and environmental scientists to translate research findings into practical urban design strategies. His work on urban cooling strategies and thermal comfort has direct relevance to cities facing increasing heat stress due to climate change.
