جستجوی استاد راهنمای دانشگاهی

• Digital Twins
• Smart Cities
• Blockchain Technology
• +۵ مورد دیگر

Noel Crespi is a Professor and Director of Studies at Telecom SudParis, part of Institut Polytechnique de Paris. He leads research in the NeSS group and is affiliated with SAMOVAR, a prominent research laboratory focusing on networks, systems, and services. His work spans multiple domains in telecommunications, networking, and smart systems. His primary research interests include digital twins for smart cities, blockchain technologies, Internet of Things (IoT) security and applications, 5G/6G networking, and machine learning applications in network management. He has published extensively on these topics, with over 100 publications in top-tier journals and conferences. His recent work shows a strong focus on digital twin applications for urban management, particularly in traffic and air quality monitoring. He has developed modular frameworks for smart city digital twins that integrate real-time data from multiple sources. His research also explores blockchain applications for network security, access control, and service provisioning in next-generation cellular networks. Dr. Crespi has received significant recognition for his work on digital twins, with his 2020 paper "Digital twin in the IoT context" in Proceedings of the IEEE being highly cited. His current research explores the integration of AI with digital twin technologies for sustainable urban development. He has supervised numerous PhD students and collaborates extensively with researchers across Europe and internationally. His work often involves interdisciplinary approaches, bringing together computer science, networking, urban planning, and environmental science. Dr. Crespi has been instrumental in developing frameworks for network digital twins, with applications in traffic management, air quality monitoring, and resource allocation in smart cities. His research demonstrates practical implementations in cities like Madrid, showing measurable improvements in urban management.

Ewelina Rupnikمشاهده پروفایل

پژوهشگر

• Photogrammetry
• Pose Estimation
• Image-based 3D Reconstruction
• +۷ مورد دیگر

Ewelina Rupnik is a prominent researcher in photogrammetry and remote sensing, currently holding a researcher position at the Laboratory on Geographic Information Science (LaSTIG) at the French National Institute of Geographic and Forest Information (IGN) since 2017. She also serves as an associate researcher at the Paris Institute of Earth Physics (IPGP). Rupnik has established herself as a leading expert in historical imagery processing, neural radiance fields, and bundle adjustment techniques. Her educational background includes a PhD in photogrammetry from the Vienna University of Technology (2015), an MSc in Engineering in Photogrammetry from AGH University of Science and Technology, Poland (2005-2010), and an Erasmus exchange at the Technische Universitaet Muenchen, Germany (2009/2010). She recently completed her Habilitation from Université Gustave Eiffel in June 2025. Rupnik's research focuses on advancing photogrammetric techniques for processing historical imagery, developing novel neural radiance field approaches for satellite imagery, and improving bundle adjustment methodologies. Her work bridges traditional photogrammetry with modern deep learning techniques, particularly in the context of sparse satellite views and historical multi-epoch imagery. She has made significant contributions to the open-source MicMac photogrammetry software project. Her recent publications demonstrate a strong trend toward integrating deep learning with traditional photogrammetric methods, with a particular emphasis on satellite and historical imagery analysis. The research spans from fundamental algorithm development (like SparseSat-NeRF and Pointless Global Bundle Adjustment) to practical applications in earth sciences (landslide, earthquake, and glacier volume mapping). 2022 EuroSDR PhD Award for Corona satellite imagery processing Best Paper Award for BRDF-NeRF research Outstanding Reviewer at CVPR 2025 Rupnik actively contributes to the academic community through editorial and leadership roles, including serving as Editor-in-Chief of the French Journal for Photogrammetry and Remote Sensing (2021-2025), Advisory Board Member of the International Journal of Photogrammetry and Remote Sensing (2024-present), and Co-chair of the ISPRS Working Group on Image Orientation and Sensor Fusion (2022-2026). She regularly teaches at Université Paris Cité & ENSG (~30 hours/year since 2015) and has conducted numerous workshops worldwide on photogrammetry with historical images and MicMac software.

• Atmospheric Chemistry
• Air Pollution
• Aerosol Science
• +۵ 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.

Veronique Berge-Cherfaouiمشاهده پروفایل

استاد

• Autonomous Vehicles
• Robotics
• Multi-sensor Fusion
• +۳ مورد دیگر

Veronique Berge-Cherfaoui is a Professor at the University of Technology of Compiègne (UTC), affiliated with the Heudiasyc laboratory (UMR CNRS 7253). She holds leadership roles including Head of the Interacting Robotic Systems (SyRI) team, Scientific Manager of the Intelligent/Autonomous Vehicles platform, and Director of SIVALAB—a joint Renault-UTC-CNRS laboratory. Previously, she led the Real-Time Systems and Embedded Computing (STRIE) department of Computer Engineering until 2021. Her research centers on autonomous vehicle systems , with expertise in multi-sensor fusion , uncertainty management , trajectory planning , and perception integrity . Her Authorization to Direct Research (2009) focused on uncertainty management in sensor fusion, forming the foundation for her work in intelligent transportation systems. Current projects address operational domain integrity monitoring and robust trajectory planning under real-world constraints. Professor Berge-Cherfaoui has supervised 35 doctoral and master’s students, with research spanning: Cooperative human-machine control (MESR/PSA grants) Collaborative perception (Renault/labex MS2T grants) Evidential perception using machine learning (Renault) Dynamic local map fusion (DGA/labex MS2T) Funding sources include Renault, Stellantis, PSA, CNRS, MESR, DGA, and regional grants. She directs SIVALAB and leads the SyRI research team at Heudiasyc, while serving on scientific councils for CNRS’s INS2I institute and CEESAR (European Center for Security Studies and Risk Analysis).

Thomas Jean-Hughمشاهده پروفایل

استاد

• Acoustic Imaging
• Signal Processing
• UAV Acoustic Localization
• +۷ مورد دیگر

Thomas Jean-Hugh is a Professor and Teacher-Researcher at Le Mans Université , affiliated with the Laboratoire d’Acoustique de l’Université du Mans (LAUM) , a leading research institute in acoustics in France. His role involves both teaching and active research in acoustic imaging, signal processing, and structural acoustics. Education PhD in Acoustics and Signal Processing (exact institution and year not specified in text) Research Interests Thomas Jean-Hugh’s research focuses on acoustic source localization and imaging , particularly through the use of microphone arrays , beamforming , and inverse problem solving . His work spans: Acoustic holography for non-stationary and confined sources UAV acoustic detection and tracking using sparse sensor arrays Vibro-acoustic diagnostics for automotive and naval structures Speech processing in meeting environments using distant microphone arrays Non-destructive testing using acoustic emission and imaging techniques Research Trends His recent publications (2022–2025) reveal a strong focus on machine learning-enhanced acoustic imaging , real-time UAV detection , and robust speech processing . He integrates genetic algorithms , Bayesian regularization , and circular harmonics into acoustic array processing, with applications in autonomous systems , marine biology , and automotive acoustics . Scientific Contributions Over 70 peer-reviewed publications in journals and conferences Active supervision of PhD students and postdocs in acoustics and signal processing Participation in international conferences such as Interspeech , ICSV , GRETSI , and ICA Collaborations & Labs He collaborates extensively within LAUM and with external partners including CNRS , ISCA , and international research groups. His lab work involves experimental acoustics , sensor array design , and real-time signal processing .

• Machine Learning
• Signal and Image Processing
• Acoustic Signal Processing
• +۷ مورد دیگر

Ahmed Nait Chabane is a Professor and teacher-researcher at CESI Engineering School in Saint-Nazaire, France, where he serves as the educational manager of the first year of the integrated preparatory cycle. His academic home is within the School of Engineering, specifically focusing on Electronics and Signal Processing. Education: Doctor of Signal and Image Processing from UBO Brest / ENSTA Bretagne (2013), Thesis: "Grazing-invariant segmentation of side-scan sonar images using a competitive neural approach" Specialized Master's degree in Image Processing and Geographic Information Systems from USTHB University, Algiers, and Télécom Bretagne (2009) Electronics Engineer from University of Bejaia (2007) Professor Nait Chabane's research spans multiple domains including acoustic signal and image processing, machine learning algorithms, decision systems, and Industry 4.0/5.0 applications. His early work (2010-2015) focused on acoustic signal processing and classification algorithms, while his current research (2017-present) emphasizes decision algorithms, heterogeneous data analysis, knowledge extraction, and human-robot collaboration systems. His work bridges theoretical research with industrial applications, particularly in aerospace and manufacturing sectors. His publication record shows a clear evolution from sonar image processing toward broader applications of machine learning in industrial settings, with recent work heavily focused on human-robot collaboration, Industry 4.0 applications, and optimization algorithms for manufacturing processes. The research demonstrates increasing interdisciplinary collaboration with industry partners through CIFRE theses. Professor Nait Chabane actively supervises multiple PhD students through CIFRE partnerships with industry, including Guillaume Morin-Duponchelle (completed), Pierre Hemono, and Houssein Olleik (ongoing). His teaching portfolio includes Electronics, Electricity, Signal and Image Processing, Artificial Learning, Statistics, and Operational Research. His Engineering and Digital Tools Research Team focuses on practical applications of advanced algorithms in industrial contexts, with particular emphasis on solving real-world constraints in confined environments, resource optimization, and human-robot interaction challenges.

Florence Tupinمشاهده پروفایل

استاد

• Synthetic Aperture Radar (SAR)
• Image Processing
• Deep Learning
• +۳ مورد دیگر

Florence Tupin is a Professor at Télécom Paris (Institut Polytechnique de Paris) and head of the Image, Data, Signal department. She leads the IMAGES research team at LTCI Laboratory, focusing on advanced SAR image processing, deep learning applications in remote sensing, and 3D reconstruction for earth observation. Her work bridges theoretical models and large-scale geospatial data exploitation. Her research integrates statistical modeling , non-local filtering , and deep neural networks to address challenges in SAR despeckling, tomographic imaging, and multi-temporal analysis. She directs projects like ANR ASTRAL (2022-2025) and ALYS (2015-2021), developing open-source tools such as MuLoG and MERLIN for the remote sensing community. Publication analysis reveals dominant themes: deep learning-driven SAR restoration (45%), 3D reconstruction (25%), multi-temporal change detection (20%), and polarimetric analysis (10%). Her group consistently pioneers self-supervised methods for resource-efficient remote sensing. Awards & Recognition: IEEE Geoscience and Remote Sensing Society Distinguished Achievement (2025) Advising & Grants: Directs 9 PhD students and 4 postdocs; secured €2M+ funding (ANR, CNES, CEA). Recent PhDs focus on deep learning for SAR tomography, multi-modal fusion, and Arctic monitoring. Leads Futur & Rupture initiatives on forest biomass estimation. Labs & Teams: Heads the IMAGES team (20 researchers) at LTCI Laboratory. Collaborates with CNES, CEA, and international partners (Wuhan University, Arctic University of Norway) on satellite data exploitation.

• Robotics Localization
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

DING Yuehua is a Researcher-Lecturer at the CESI Strasbourg Campus, part of the Engineering and Numerical Tools research team. They hold a HDR (Habilitation à Diriger des Recherches) from the University of Nantes (2020) and a PhD in 2011 focused on MIMO communication system performance optimization. Their academic roles include teaching Electronics, Signal Processing, and Big Data to engineer students in their first and third years. Research interests span Robotics Localization , Data Processing/AI , and Signal Processing , with a focus on integrating machine learning into robotics and wireless communication systems. Recent work emphasizes indoor robot localization using WiFi-visual fusion and SAR-based ship detection. Key technological skills include Python, C, and MATLAB. Publications reflect a strong emphasis on multi-scale object detection, hybrid modeling in marine science, and advanced signal processing techniques for MIMO systems. Review activities include IEEE TSP and TVT. No notable scientific awards are listed, but ongoing contributions include cross-disciplinary projects in medical robotics and materials science.

• Energy Performance of Buildings
• Smart Building Systems
• Data Analysis & Sensor Fusion
• +۳ مورد دیگر

MICHENEAU Kevin is a Researcher-Lecturer at CESI LINEACT, affiliated with the Engineering and Numerical Tools Research team. His work spans both applied engineering and fundamental physics research. He holds a PhD in Particle Physics from the University of Nantes (2018) and a Master's in Subatomic Physics from the same institution (2014). His research focuses on energy performance optimization in smart buildings, intelligent building control systems, and data fusion techniques for occupancy modeling. Concurrently, he continues contributions to dark matter detection through advanced analysis of XENON experiment data. Education: PhD in Particle Physics, University of Nantes (2018) Master's in Subatomic Physics, University of Nantes (2014) Research Interests: Developing models for occupancy-aware energy systems Optimizing multi-objective control strategies for building HVAC systems Data-driven approaches for sensor fusion and anomaly detection Continued dark matter research via XENON collaboration experiments Recent Research Trends: Recent work demonstrates a strategic shift toward applied building systems research while maintaining expertise in particle physics. 2024's multi-objective MPC study represents his growing focus on practical energy efficiency solutions, while XENON1T/XENON100 analyses (2017-2019) highlight sustained contributions to dark matter detection methodologies. Grants & Advising: Supervising PhD candidate Adrien BOURGOIN (2023-2026) Laboratory Affiliations: Core member of CESI LINEACT's Engineering and Numerical Tools Research team, collaborating on smart building instrumentation and computational modeling projects.

• Computer Vision
• Image Processing
• Autonomous Systems
• +۶ مورد دیگر

Antoine Manzanera is a Professor HDR at ENSTA Paris, affiliated with the Unité d'Informatique et d'Ingénierie des Systèmes (U2IS) and the Systèmes autonomes et Robotique (SAR) department. His research focuses on Computer Vision, Image Processing, and Autonomous Systems, with notable contributions to medical imaging applications and robotics. He holds a Habilitation Degree from Université Pierre et Marie Curie (2012) and has taught at ENSTA, Université Paris Sorbonne, and international institutions. BSc Mathematics, Université Claude Bernard Lyon 1 (1991) MSc Theoretical Computer Science, Université Claude Bernard Lyon 1 (1993) PhD Signal and Image Processing, Télécom ParisTech (2000) Research interests span Computer Vision fundamentals, including Video Analysis, Scene Reconstruction, and Autonomous Visual Learning. His work bridges theory and practice, addressing challenges in medical diagnostics (e.g., colonoscopy analysis, Parkinson's biomarkers) and robotics (e.g., drone navigation, autonomous driving datasets). Recent articles highlight advancements in multimodal data fusion, deep learning optimization, and medical imaging tools. Collaborations include projects on synthetic databases for colon depth estimation and Riemannian geometry for oculomotor analysis. His contributions to open-source frameworks like Video++ and educational initiatives (e.g., DroMOOC on drones) further his impact.

• Photogrammetry
• 3D Modeling
• Heritage Documentation
• +۵ مورد دیگر

Pierre Grussenmeyer is a University Professor and Lecturer in the Civil Engineering and Topography Department at INSA Strasbourg, where he also heads the Photogrammetry Laboratory within the ICUBE research unit (TRIO Team). His academic role encompasses teaching aerial/architectural photogrammetry, topometry, positional astronomy, and virtual modeling, while leading research in 3D documentation techniques for heritage and urban environments. His research specializes in photogrammetry , 3D modeling , and heritage documentation , with core expertise in camera calibration, close-range photogrammetry, industrial metrology, and photogrammetric software development (TIPHON/ARPENTEUR projects). Current work integrates deep learning for semantic segmentation of heritage structures, BIM for architectural modeling, and multi-sensor fusion (LiDAR, photogrammetry, GPS) for comprehensive spatial analysis of cultural sites and urban infrastructure. Analysis of his 2023-2025 publications reveals accelerating adoption of AI-driven methods, particularly for semantic segmentation of heritage point clouds and NeRF-based 3D reconstruction. His work bridges traditional photogrammetric precision with cutting-edge neural networks, focusing on practical applications like Rhine castle modeling, forensic scene documentation, and automated road marking extraction from mobile scans. Key trends include synthetic data generation for AI training and rigorous benchmarking of state-of-the-art reconstruction algorithms. Grussenmeyer supervises technological research projects (PRT) and end-of-studies projects (PFE) at INSA Strasbourg, while maintaining active industry collaborations through CIFRE theses with the Alsace region. His work extends to professional training through short continuing education programs, equipment expertise services, and R&D studies for working professionals in surveying and heritage sectors. As head of the Photogrammetry Laboratory, he directs research in architectural photogrammetry, industrial metrology, and virtual model development. Current lab initiatives include underwater crime scene documentation, tropical vernacular architecture preservation in Indonesia, and multi-sensor 3D pipelines for Javanese temples, emphasizing practical solutions for heritage conservation through photogrammetric innovation.

• Distributed Systems
• Networks
• Data Stream Analysis
• +۹ مورد دیگر

Yann Busnel is a Full Professor at IMT Atlantique in France, where he serves as Head of the Network Systems, Cyber Security and Digital Law (SRCD) department at the Rennes Campus since January 2017. He also leads the D2-NTS department (Networks, Telecommunications and Services) within UMR IRISA. His academic journey includes positions as Assistant Professor at Université de Nantes (2009-2014), Associate Professor and Head of Computer Science Department at ENSAI (2014-2016), and postdoctoral work at Università "La Sapienza" in Rome. Busnel's research focuses on models for large-scale distributed systems and networks, with particular expertise in data stream analysis over massive datasets. His work spans cybersecurity, dependability, medical data analysis (including pharmacovigilance and genomic sequence analysis), and the self-organized coordination of drone fleets for disaster management. He has published over 100 peer-reviewed papers and coordinated numerous national and international research projects. His recent scholarly output demonstrates a clear progression toward applied cybersecurity solutions, particularly in federated learning for intrusion detection, blockchain analysis, and wireless network security. The research shows strong interdisciplinary connections between distributed systems theory and practical security applications, with increasing emphasis on medical data analysis and drone coordination systems. His work bridges theoretical computer science with real-world security and networking challenges. Busnel actively mentors PhD students across multiple institutions, with current advisees working on topics including AI in cybersecurity using game theory, threat intelligence tools for IoT, and trust in distributed network services. He has coordinated substantial research funding through projects like CMA TCE (France 2030), EDIH Bretagne (Horizon Europe), and various ANR-funded initiatives. He leads several research chairs including OM&AI2MD (Operations Models and Artificial Intelligence to Manage Disasters), PRACom (Pôle de Recherche Avancée en Communications), and Cyber CNI (Cyber Security of Critical Infrastructures), demonstrating his leadership in both academic research and industry collaboration. His work with these chairs focuses on practical applications of his theoretical research in disaster management, communications technology, and critical infrastructure protection.

• Multi-source Sensor Fusion
• Semantic Mapping
• Collaborative Robotics
• +۵ مورد دیگر

Hiba Al-Assaad serves as a Teacher-researcher at CESI Higher Education Group in Toulouse, France, specializing in Engineering and Digital Tools within the Computer Networks and Telecommunications domain. Her work bridges academic instruction in preparatory and engineering cycles with advanced research in robotics and navigation systems. Her research focuses on multi-source sensor fusion, semantic mapping for mobile robotics, and cyber-physical systems. Key areas include developing robust navigation algorithms using digital elevation models, creating decentralized decision systems for industrial applications, and advancing federated learning frameworks. Her work consistently addresses real-world industrial challenges through collaborative robotics and smart transportation solutions. Analysis of her recent publications reveals a strong trajectory toward industrial cyber-physical systems, with increasing emphasis on federated learning, meta-algorithms, and human-robot collaboration in manufacturing environments. Her research integrates geospatial engineering with machine learning to solve navigation and perception challenges. She actively supervises doctoral candidates including A. Achour (defense 2024) researching real-time semantic mapping with robot swarms, and B. Guendouzi (defense 2025) developing decision support systems for industrial cyber-physical environments. Her technical contributions span from fundamental navigation algorithms to cutting-edge machine learning applications, maintaining consistent output across journals like International Journal of Image and Data Fusion , IEEE CSR , and Journal of Network and Computer Applications .

Sylvie MARCOSمشاهده پروفایل

پژوهشگر

• Signal Processing
• Radar Systems
• Machine Learning
• +۶ مورد دیگر

Sylvie MARCOS is a Senior Researcher at CentraleSupélec, France, affiliated with the Laboratoire des Signaux et Systèmes (L2S). She holds a PhD in Telecommunications (1987) and an HDR in Signal Processing (1995) from the University of Paris-Sud. Her research focuses on signal processing, radar systems, machine learning applications in environmental monitoring, and chaotic sequence generation for telecommunications. She has supervised numerous doctoral students and co-authored over 50 publications in high-impact journals and conferences. Education: Engineer, Ecole Centrale de Paris (1984) PhD in Telecommunications, University of Paris-Sud (1987) Habilitation à Diriger des Recherches (HDR), University of Paris-Sud (1995) Research Interests: Time series analysis and anomaly detection Radar signal processing and LPI radar detection Chaotic sequences for MIMO radar and CDMA systems High-resolution source localization and sparsity-based methods Adaptive filtering and neural network applications Her recent work emphasizes environmental monitoring using machine learning and deep learning techniques, as well as robust radar systems design. She collaborates with institutions like CEA-DAM Valduc and Onera on defense-related signal processing challenges. Grants/Projects: Active involvement in European and national research projects on radar systems, environmental signal processing, and sparse signal recovery. Labs/Teams: Member of L2S research groups MODESTY, COMEDY, and SYCOMORE, focusing on systems modeling and robust control.

Pedro Castilloمشاهده پروفایل

پژوهشگر

• Nonlinear Control
• Robust Control
• Reactive Control
• +۷ مورد دیگر

Pedro Castillo is a DR CNRS Researcher at the Heudiasyc laboratory within the University of Technology of Compiègne, France. He leads the SyRI team (Systèmes Robotiques en Interaction) and oversees drone-related activities at the laboratory. His research focuses on nonlinear control, robust navigation, multi-agent systems, and resilient control strategies for unmanned aerial vehicles (UAVs). He has contributed significantly to the theoretical and experimental validation of control schemes for under-actuated systems, emphasizing real-time implementation on robotic platforms. His work bridges theoretical advancements and practical applications, particularly in aerial robotics and cooperative control architectures. Dr. Castillo has supervised 19 PhD students (15 defended, 3 ongoing, and 1 cotutelle), managed 29 research projects, and organized over 50 seminars at Heudiasyc. He is an Associate Editor for the IEEE Robotics and Automation Letters (2020–2024) and has participated in numerous academic committees, including ANR and HCERES evaluations. His research has resulted in over 170 scientific publications, including two influential books on aerial vehicle modeling and control. Key contributions include the design of resilient control algorithms, multi-agent coordination frameworks, and hardware prototypes such as the PVTOL, octarotor, and ROMEO quadrotor. His work has been showcased at international conferences and trade fairs, solidifying Heudiasyc's reputation as a leader in UAV control research.