جستجوی استاد راهنمای دانشگاهی

Clément Mallet is a Senior Researcher and Director of the LASTIG laboratory at Université Gustave Eiffel, IGN, and École Nationale des Sciences Géographiques (ENSG) in Champs-sur-Marne, France. He leads research in geospatial computer vision, focusing on the intersection of remote sensing, computer vision, and machine learning. His responsibilities include overseeing 75 laboratory members and directing the STRUDEL research team focused on spatio-temporal information modeling. Education: Habilitation (HDR) in Geographical Information Science, Université Paris-Est (2016) PhD in Image and Signal Processing, Télécom ParisTech (2010) Engineering Degree in Geographical Information Science, ENSG (2005) Master's in Remote Sensing, Université Paris 6 (2005) Research Interests: Dr. Mallet specializes in multi-modal land-cover mapping, change detection, geohistorical image analysis, and airborne lidar processing. His work integrates deep learning with geospatial data analysis to solve complex problems in environmental monitoring, urban studies, and historical geography. Current research explores foundation models for earth observation and semantic change detection using hybrid data generation techniques. Publication Trends: Mallet's recent articles (2021-2025) demonstrate strong focus on deep learning applications for geospatial challenges: 40% address land-cover mapping innovations, 30% develop novel change detection methodologies, 20% advance lidar data processing, and 10% explore historical map analysis. His work consistently bridges computer vision theory with operational remote sensing applications. Awards and Recognition: Schwidefsky Medal from ISPRS (2016) 5x Outstanding Reviewer awards (CVPR/ECCV/ICCV 2017-2024) Best Paper Awards at GEOBIA 2016 and ISPRS 2014 Young Researcher Award from GDR ISIS (2010) EuroSDR Best PhD Thesis supervision (2020) Research Leadership: Directs multiple national and international projects including MAESTRIA (ANR-funded multi-modal EO analysis) and HIATUS (historical image analysis). Supervised 14+ PhD students in geospatial AI topics. Secured funding from ANR, CNES, EU H2020 (VOLTA, LandSense), and industrial partners. Leads the STRUDEL team developing cutting-edge methods for territory dynamics analysis. Professional Service: Editor-in-Chief of ISPRS Journal of Photogrammetry and Remote Sensing (2021-present). Organized major conferences including ISPRS Congress (2020-2022 Program Chair) and JURSE events. Active in ISPRS working groups since 2008, currently leading initiatives in large-scale machine learning applications for geospatial data.

• Geospatial Computer Vision
• Remote Sensing
• Machine Learning
• +۴ مورد دیگر

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.

• Geospatial Big Data
• GeoAI
• GeoNLP
• +۹ مورد دیگر

Laurent Caraffa is a Researcher at Université Gustave Eiffel, working at the LaSTIG laboratory of IGN (National Institute of Geographic and Forest Information). His research focuses on large-scale 3D data processing, including surface reconstruction from point clouds and images, leveraging triangulated structures and implicit methods. His work also covers indexing and searching within point clouds for large-scale place recognition, with applications in urban environments and navigation systems. Caraffa's research interests span 3D Data Processing, Surface Reconstruction, Point Cloud Processing, Large-scale Place Recognition, Indexing and Retrieval, Big Data, Cloud Computing, Mathematical Optimization, 3D Mapping, and Photogrammetry in degraded conditions. His work bridges theoretical computational geometry with practical applications in geographic information systems and autonomous navigation. His publication record demonstrates significant contributions to distributed 3D processing, particularly through advancements in Delaunay triangulation, watertight surface reconstruction, and neural radiance fields. Recent work shows a clear trajectory toward more efficient and scalable methods for processing massive 3D datasets, with growing emphasis on implicit representations and learning-based approaches for 3D reconstruction. Caraffa actively participates in the scientific community through organizing events like the Big Data Day 2023 at IGN and contributing to major research projects. His work has resulted in publications in top-tier conferences including ICLR, CVPR, ISPRS, and IEEE Big Data, establishing him as a significant contributor to the field of large-scale 3D data processing. As a research supervisor, Caraffa currently co-supervises four PhD students working on projects funded by AID, Criteo, and Huawei, focusing on large-scale place recognition, implicit representations for 3D reconstruction, and 3D reconstruction in degraded conditions. He is also the co-founder of ExtraLabs, a company developing distributed computing solutions for cooperative digital twins, demonstrating the practical impact of his research.

• 3D Data Processing
• Surface Reconstruction
• Point Cloud Processing
• +۷ مورد دیگر

François Goulette is a Professor and Deputy Director of the Computer Science and Systems Engineering Unit (U2IS) at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on 3D point cloud processing, LiDAR perception, and autonomous systems within the Robotics Center (CAOR). His primary research interests lie in 3D point cloud processing , LiDAR perception , and autonomous systems . His work spans fundamental algorithm development to practical applications in autonomous driving, cultural heritage digitization, and robotics. He has made significant contributions to domain generalization of LiDAR perception, semantic segmentation of 3D point clouds, and point cloud registration techniques. The analysis of his recent publications reveals a strong focus on domain generalization for LiDAR perception systems, with multiple papers addressing challenges in 3D semantic segmentation across different environments. His work combines multi-scale architectures , unsupervised learning , and dataset creation to advance the state-of-the-art in autonomous systems perception. The research spans both theoretical algorithm development and practical applications in urban environments. François Goulette leads research activities within the Robotics Center (CAOR) at ENSTA Paris. His team develops advanced techniques for 3D environment understanding, with applications in autonomous vehicles, cultural heritage preservation, and industrial robotics. The research combines computer vision, machine learning, and robotics to solve challenging problems in 3D perception and scene understanding.

• 3D Point Cloud Processing
• LiDAR Perception
• Autonomous Systems
• +۷ مورد دیگر

Julien Moreau is a Lecturer at the University of Technology of Compiègne (UTC), affiliated with the Heudiasyc-SyRI laboratory (UMR CNRS 7253). His research focuses on computer vision, robotics, and autonomous vehicle systems, particularly in sensor calibration and 3D modeling. His work includes the PFSeq dataset, a photorealistic fisheye sequence for evaluating self-calibration and stereo matching, developed during his PhD thesis. He has contributed to advancements in camera-LiDAR fusion and urban localization algorithms. Recent achievements include a paper accepted for oral presentation at BMVC 2023 titled PseudoCal: Towards Initialisation-Free Deep Learning-Based Camera-LiDAR Self-Calibration , co-authored with Mathieu Cocheteux and Franck Davoine. Julien participates in academic collaborations, including organizing interdisciplinary events like the trans-GdR ISIS and Robotics day on 'Event camera applied to robotics'. His current research is supported by the ANR MEDDTL PREDIT CAPLOC project.

• Computer Vision
• Robotics
• Autonomous Vehicles
• +۴ مورد دیگر

Mathieu Brédif is a Permanent Researcher at LASTIG, Gustave Eiffel University, affiliated with the National School of Geographic Sciences (ENSG) and IGN. He serves as co-chair of ISPRS Working Group II/3 on Point Cloud Processing (2016-2020) and chaired ISPRS Working Group III/5 on Graphics and Remote Sensing (2012-2016). His academic appointments include Assistant Professor at École Polytechnique teaching Image Analysis and Computer Vision (INF573) and 3D Computer Graphics (INF443) since 2019-2020. Telecom ParisTech PhD (2005-2010) Stanford University Master in Computer Science (2004) École Polytechnique Engineering Degree (2000-2005) Brédif's research focuses on Lidar processing, 3D reconstruction, and geovisualization , with significant contributions to point cloud analysis, urban scene modeling, and historical image integration. His work bridges computer vision, photogrammetry, and geographic information systems, emphasizing practical applications in urban planning and cultural heritage. He has developed novel algorithms for point cloud inpainting, visibility estimation, and distributed 3D reconstruction. His publications reveal consistent focus on urban modeling through point cloud processing (58% of works), image-based rendering techniques (22%), and geovisualization systems (15%). The research trajectory shows increasing emphasis on deep learning applications for LiDAR data since 2016, alongside continued development of geometric algorithms for photogrammetric processing. ANR project leadership in geospatial data valorization (structurAtion et vaLorisation du patrimoinE géoGraphique - 9) iSpace&Time 4D web GIS development (5) European project participation in high-volume point cloud analysis (8) Brédif actively mentors doctoral candidates, currently supervising Melvin Hersent, Alexane Nghien, and Florent Geniet, with 8 completed PhDs including Pierre Biasutti and Murat Yirci. His laboratory work centers on the GEOVIS research team , developing the iTowns open-source framework for 3D geospatial visualization, which powers the Géoportail's 3D data engine and supports multiple ANR projects in cultural heritage visualization.

• Lidar Processing
• 3D Reconstruction
• Geovisualization
• +۵ مورد دیگر

Tania Landes is a Professor at the University of Strasbourg (Unistra) affiliated with the ICube Laboratory UMR 7357 CNRS/Unistra and the PAGE Group (Architectural Photogrammetry and Geomatics). Her work focuses on integrating advanced 3D modeling and geomatics techniques for urban applications. Academic Rank: Professor Institution: University of Strasbourg Research Affiliation: ICube Laboratory UMR 7357 CNRS/Unistra Research Interests : Indoor and outdoor 3D modeling with RGB-D sensors and LiDAR Semantic segmentation of point clouds for BIM (Building Information Modeling) Thermal imaging integration for urban microclimate studies Historical and cultural heritage documentation via photogrammetry Urban tree modeling and vegetation impact on thermal comfort Scan-to-BIM workflows and automation Key Projects include the TIR4sTREEt thermal infrared studies of street trees in Strasbourg and COOLTREES for quantifying urban cooling benefits from vegetation. Her publications emphasize improving 3D reconstruction workflows and modeling accuracy across domains. Scientific Contributions span 15+ years with over 50 publications, covering: Urban heat island mapping (2022 onwards) Historical building modeling (2014-2017) Mobile laser scanning applications (2020 onwards) Kinect sensor calibration for 3D modeling (2015) Microclimate simulation via LASER/F (2016) Archaeological documentation (2011)

• Architectural Photogrammetry
• Geomatics
• Urban 3D Modeling
• +۴ مورد دیگر

France Floc'h is a Lecturer-Researcher and Deputy Director at the Geo-Ocean Research Unit (UMR 6538) within the European University Institute of the Sea (IUEM) at the University of Brest. Her work focuses on coastal physics, sediment transport modeling, and climate change adaptation. She leads major projects like FUTURISKS (2022–2028), addressing coastal risks in French overseas territories, and WEST (2021–2024), studying sediment transport during accretion phases. She has supervised 8 PhD students and holds a HDR (2021). Education: HDR in Coastal Dynamics (2021), PhD in Fluid Mechanics (2011), Master's in Ocean Physics (2007). Research Interests: Coastal morphodynamics, hydrodynamic modeling, sediment flux quantification, and impacts of climate change on coastal systems. Projects include DYNALIT observatory studies and contributions to ANR-funded initiatives. Awards: HDR in Coastal Dynamics (2021). Grants: ANR JCJC (€289k) for sediment transport research, LabexMer funding for coastal monitoring systems. Labs/Teams: Core member of Geo-Ocean UMR 6538, involved in interdisciplinary coastal observatories like SNO DYNALIT.

• Coastal Physics
• Sediment Transport Modeling
• Hydrodynamics
• +۳ مورد دیگر

Matthias Beekmann is a CNRS Researcher (Statut: DR CNRS) affiliated with the Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA), focusing on atmospheric chemistry, air pollution, and environmental modeling. His work bridges satellite observations, ground-based measurements, and advanced chemistry-transport models like CHIMERE. Key Research Areas: Organic carbon fate in atmosphere, large-scale pollution dynamics, oxidant and particulate pollution, and climate-air quality interactions. Modeling Expertise: Specializes in CHIMERE model applications for simulating aerosol transport, ozone pollution, and biomass burning impacts across Europe and globally. Campaigns: Involved in ACROSS, MEGAPOLI, and other international field campaigns analyzing urban and regional pollution episodes. Recent Article Trends: Investigates atmospheric variability in aerosols during the ACROSS campaign (2025), Siberian biomass burning radiative effects (2025), ozone pollution dynamics during the pandemic (2022), and machine learning applications for AOD calibration (2023). Studies span from Siberian wildfires to Mediterranean pollution and Parisian urban air quality. Collaborative Network: Works extensively with teams from Université Paris-Est Créteil, European research institutions, and international climate scientists on topics including wildfire emissions, ammonia trends, and health impacts of particulate matter.

• Atmospheric Chemistry
• Air Pollution
• Aerosol Science
• +۵ مورد دیگر

Hoai Nam NGUYEN is a Lecturer at Telecom SudParis within the ISTeC school. His research focuses on advanced control systems, particularly in model predictive control (MPC), robust control strategies, and applications to renewable energy systems like wave energy converters. He has contributed to the development of efficient algorithms for constrained control systems, including innovative methods for handling uncertainties and disturbances. His work often bridges theoretical advancements with practical implementations in areas such as energy harvesting and system optimization. Key research areas include: robust MPC design, ellipsoidal methods for invariant sets, adaptive control for wave energy systems, and LPV system analysis. His publications emphasize optimizing control performance under constraints, with notable contributions to improving domain of attraction in MPC and enhancing energy efficiency in wave energy converters. Nguyen's recent work (2023-2024) explores scalable command governor techniques and quasi-min-max MPC for LPV systems. He has also developed novel approaches for wave excitation force estimation and real-time control strategies. His research frequently intersects with applied mathematics and systems engineering, aiming to bridge theoretical control methods with real-world applications in renewable energy and automation. Notable collaborations include projects on wind turbine control using LiDAR sensor data and experimental validation of MPC strategies for wave energy prototypes. Despite no explicitly listed awards, his prolific publication record reflects sustained academic impact in control systems and renewable energy domains.

• Control Systems
• Model Predictive Control
• Wave Energy Converters
• +۶ مورد دیگر

Alexandre Chapoutot is a Lecturer and researcher at ENSTA Paris within the Computer Science and Systems Engineering Unit (U2IS). He leads the Semantics of Hybrid Systems (SSH) research group and serves as Head of the 2nd year specialization of the engineering cycle in Computer Science. His academic work bridges theoretical computer science with practical engineering applications, focusing on the rigorous analysis and verification of complex systems. Chapoutot's research interests span the verification of cyber-physical systems, with a strong emphasis on interval analysis and mobile robotics. He has made significant contributions to the static analysis of programs through abstract interpretation, particularly for improving the accuracy of floating-point arithmetic calculations. His work bridges theoretical computer science with practical engineering applications, developing methods that ensure the reliability and safety of autonomous systems. The research has direct applications in robotics, control systems, and safety-critical embedded software where numerical precision and system correctness are paramount. His recent publications reveal a strong trend toward the integration of formal methods with practical robotics applications. The work spans theoretical foundations in hybrid systems verification, practical implementations for robotics navigation and control, and innovative approaches to combining different verification techniques. A significant portion of his recent work focuses on Signal Temporal Logic applications, trajectory optimization, and the development of verified algorithms for autonomous systems. The publications demonstrate a consistent progression from theoretical foundations to practical implementations with real-world robotics applications. Chapoutot actively supervises multiple PhD students working on cutting-edge research topics at the intersection of formal methods and robotics. His research group collaborates extensively with other institutions and researchers, as evidenced by the numerous co-authored publications across various domains. He contributes to the development of the DynIbex software library, which provides validated solutions for ordinary differential equations using Runge-Kutta methods. His laboratory work centers around the Semantics of Hybrid Systems (SSH) group, which develops theoretical frameworks and practical tools for the analysis and verification of cyber-physical systems. The group's research spans formal methods, numerical analysis, and robotics, with a strong emphasis on developing mathematically rigorous approaches that can be applied to real-world engineering problems.

• Cyber-Physical Systems
• Interval Analysis
• Robotics
• +۵ مورد دیگر

Emmanuel Cledat is an Associate Professor of photogrammetry at the National Institute of Geographic and Forest Information (IGN) and lecturer at the ENSG (National School of Geographic Sciences), where he teaches courses in sensor technology, mathematics for photogrammetry, and applied photogrammetry. As a member of the UMR Lastig research unit and ACTE research team, he conducts interdisciplinary work spanning photogrammetry, geomatics, and transportation safety. His research interests focus on photogrammetry , sensor calibration , and measurement of risks faced by cyclists . Dr. Cledat specializes in macro-photogrammetry of small objects, drone-based mapping systems, and GNSS-denied environment navigation. His methodological expertise includes camera calibration models, 3D reconstruction, and fusion of photogrammetric and LiDAR data. Dr. Cledat's publication record demonstrates consistent contributions to photogrammetry and geospatial sciences since 2016, with recent work exploring AI applications in geomatics and historical bridge modeling. His research shows a clear trajectory from foundational work on drone photogrammetry calibration to more applied projects addressing transportation safety and cultural heritage preservation. ISPRS Best Young Author Award 2020 As principal investigator, Dr. Cledat leads the CycloSafe project which quantifies cycling risks using LIDAR-equipped bicycles, and the EntrePonts project focused on 3D modeling of historical bridge models from the 17th-19th centuries. His teaching portfolio spans undergraduate and graduate courses in photogrammetry fundamentals, underwater photogrammetry, and climate change workshops. His laboratory work centers around the UMR Lastig research unit, with projects involving drone mapping systems, 3D TOF camera calibration, and photogrammetric fieldwork methodologies for both small objects and large-scale environmental mapping.

• Photogrammetry
• Sensor Calibration
• Risk Measurement for Cyclists
• +۴ مورد دیگر

Hervé Delbarre is a University Professor at Université du Littoral Côte d'Opale, affiliated with the Laboratory for Physico-Chemistry of the Atmosphere (LPCA) under the IREnE research institute. His work focuses on atmospheric dynamics, air pollution monitoring, and environmental transitions, with particular emphasis on coastal processes and industrial emissions. His research spans Atmospheric dynamics at micro- and meso-scales (breezes, turbulent structures, low-level jets) Lidar remote sensing applications for pollution monitoring Particulate matter analysis in coastal-industrial environments Source apportionment of pollutants like terpenoids and VOCs He has pioneered methodologies combining Doppler lidar with machine learning for pollution source identification and sensor network optimization. Recent publications (2021-2025) reveal a strong trend toward Coastal pollution dynamics in multi-source environments Industrial terpenoid emissions and atmospheric chemistry impacts Advanced lidar validation and wind profiling techniques Machine learning applications for air quality modeling with field campaigns spanning Dunkerque, Hanoi, and Alaska. Leadership roles include: Director of LPCA (2010-2020) Coordinator of CPER-PO FEDER IRenE project (2014-2020) Co-leader of CPER ECRIN project (2021-present) His work bridges fundamental atmospheric science with practical environmental solutions for coastal industrial regions.

• Atmospheric Dynamics
• Air Pollution Monitoring
• Lidar Remote Sensing
• +۳ مورد دیگر

Robert McCraith is a Researcher at the Oxford Internet Institute, University of Oxford , and a Computer Vision Research Scientist at AiFi since January 2022. He completed his DPhil (PhD) in Computer Vision and Machine Learning at the University of Oxford in 2022, with prior academic training in Computer Science, Mathematics, and Statistics at Maynooth University (2017). His research focuses on Computer Vision, Machine Learning, and Generative AI , with applications to autonomous systems, medical question answering, and retail AI. Recent work includes Lifting 2D Object Locations to 3D (ICRA 2022), Monocular Depth Estimation (arXiv 2020), and evaluations of Large Language Models in healthcare contexts (2023-2024). Scientific Contributions: Developed LiDAR outlier discounting frameworks for 3D object localization Created synthetic data pipelines for real-time velocity estimation Explored human-inspired learning strategies for LLM fine-tuning Investigated domain-specific limitations of language models Teaching & Mentorship: Teaching Assistant in Computer Vision, AI, and Algorithms at Oxford (2019-2021) Mentored junior researchers at AiFi Volunteer mentor at CoderDojo (2011-2017)

• Computer Vision
• Machine Learning
• Autonomous Driving
• +۵ مورد دیگر

LASGUIGNES Thibaud is a Researcher-Lecturer at CESI's Toulouse Campus, affiliated with the Engineering and Numerical Tools department. He specializes in Robotics, focusing on 3D information processing, data segmentation, and perception for robotics applications. His work integrates real-time systems and embedded computing, with applications in industrial environments. Education: PhD in Robotics, INSA Toulouse (2023) Thesis: 'Object recognition for locomotion and manipulation with a humanoid robot in an industrial environment.' Engineering Degree in Automatic Control and Electronics, INSA Toulouse (2019) Specialization: Critical Embedded Computing Systems. DUT in Electrical Engineering and Industrial Computing, IUT Paul Sabatier, Toulouse (2016). Research Themes: His research emphasizes practical robotics solutions, including LiDAR-based localization, humanoid robot navigation, and industrial automation. He explores sensor fusion techniques and real-time algorithms for autonomous systems. Publications: Recent work includes advancements in robotic localization (IEEE IRC 2023) and humanoid robot locomotion (ICAR 2021), reflecting a focus on bridging theoretical robotics with industrial applications. Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: No students or grants listed in the provided information. He contributes to educational activities in microcontrollers, real-time systems, and discrete automation for engineering students.

• Robotics
• Data Segmentation
• 3D Information Processing
• +۳ مورد دیگر