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Grégoire Allaire is a Professor of Applied Mathematics at École Polytechnique, where he leads research in shape optimization , homogenization , and multi-scale modeling . His work bridges theoretical and applied domains, focusing on partial differential equations (PDEs), composite materials, and computational methods.
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Stephane Cotin is a Research Director at Inria and leader of the MIMESIS team, specializing in real-time physics-based medical simulations. His work focuses on surgical training, planning, and image-guided therapy, with over 200 scientific articles and the development of the open-source SOFA framework. He co-founded InSimo, Twinical, and EVE, and previously held roles at Harvard Medical School and Mitsubishi Electric Research Lab. Cotin’s research bridges imaging, robotics, and medicine to improve healthcare outcomes, emphasizing patient-specific biophysical modeling and real-time computation. His awards include the Academy of Sciences Award (2018) and Dirk Bartz Medical Prize (2015). He has advised numerous PhD students and led projects like MediTwin and PREMYOM, advancing digital twin technologies for precision medicine.
Houman BOROUCHAKI is a Professor at the University of Technology of Troyes (UTT), France, with over 20 years of academic leadership. He has served as Head of the Automatic Mesh Generation and Advanced Methods (GAMMA3) project team since 2008 and previously led the Laboratory of Mechanical Systems and Concurrent Engineering (LASMIS) (2005-2007). His work bridges academic research and industrial applications through collaborations with INRIA , French Petroleum Institute (IFPEN) , Dassault Aviation , and others. Research Interests: A pioneer in adaptive meshing , he focuses on finite element methods , geometric modeling , and numerical simulations . His innovations underpin mesh generation algorithms , 3D triangulation software , and industrial applications in metal forming, composite simulation, and subterranean modeling. Scientific Trends: His recent work emphasizes metric-based meshing , high-order geometric validity , and parallel processing for mesh generation , with applications in petroleum reservoirs, aviation surfaces, and nanomaterials. His Google Scholar profile reflects 25+ years of contributions to meshing and simulation. Teaching: With 22 years of experience, he teaches courses on meshing , numerical analysis , geometric modeling , and computer graphics at UTT, covering undergraduate to PhD levels. Labs & Teams: He leads the interdisciplinary GAMMA3 team and has contributed to LASMIS (mechanical engineering), L2n (CNRS-UMR 7076) (nanomaterials), and LIST3N (computer science).
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Pierre Alliez is a Senior Researcher and Team Leader at Inria Sophia Antipolis – Méditerranée, leading the TITANE project-team. He holds roles such as President of the Inria Evaluation Commission and Scientific Coordinator of the Inria-DFKI partnership. His research focuses on Geometry Processing, including mesh compression, surface reconstruction, and optimal transportation. Alliez has authored numerous scientific publications and book chapters, receiving accolades like the Eurographics Young Researcher Award (2005) and ERC grants (IRON, TITANIUM). His academic activities include supervising over 50 PhD students and postdoctoral researchers, and leading projects like GRAPES (Learning and Processing Shapes) and BIM2TWIN (digital twin construction). He has served on editorial boards for Computer Graphics Forum and ACM Transactions on Graphics , and organized major conferences like Pacific Graphics and Eurographics. His work bridges computational geometry, computer graphics, and applied mathematics, with practical applications in 3D printing, cultural heritage, and urban modeling. Education: No specific educational details provided, but has authored a textbook on Polygon Mesh Processing (AK Peters, 2010). Research Interests: Geometry Processing, Mesh Generation, Surface Reconstruction, Optimal Transport, and 3D Data Analysis. Grants & Projects: ANR Pisco, ERC IRON, BIM2TWIN, GRAPES, and collaborations with industries like Dassault Systèmes and Dorea Technology. Labs/Teams: Leads the TITANE team at Inria, contributing to software like CGAL and advancing open-source tools for geometric processing.
Yelva Roustan is a Lecturer at CEREA (joint laboratory between École Nationale des Ponts et Chaussées and EDF R&D) since 2005, specializing in air quality modeling . Her research focuses on multiscale modeling approaches, source-concentration relationships, and pollutant deposition processes. She teaches at the National School of Bridges and Roads, sharing knowledge from her research in atmospheric transport and environmental science. Her work addresses urban pollution challenges, including the impact of low-emission vehicles on air quality, urban tree effects, and pollutant dynamics in street networks. She has developed the MUNICH street-network model and contributed to projects like EURODELTA for multi-pollutant analysis across Europe. Her methods integrate data assimilation, inverse modeling, and Bayesian techniques to improve emission source reconstruction and model accuracy. Key contributions include studies on radioactive release scenarios (e.g., Fukushima, 106Ru event) and urban infrastructure impacts on pollution. Her research bridges atmospheric science with engineering solutions for sustainable urban environments. She holds a PhD in Environmental Engineering from École Nationale des Ponts et Chaussées (2005) and an HDR in Numerical Modeling of Atmospheric Pollutants (2020).
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.
Christophe Charrier is a Full Professor in Forensics and AI at Université de Caen Normandie, affiliated with GREYC UMR CNRS 6072 and IUT Grand Ouest Normandie's Multimedia and Internet Department (Dept. MMI). He obtained his PhD in Computer Science from Université Jean Monnet (Saint-Etienne) in 1998, followed by an HDR (Habilitation à Diriger des Recherches) in 2011 from Université de Caen Normandie. His academic journey includes roles as a Postdoctoral Researcher at Université Laval (1998-2001), Associate Professor at IUT Saint-Lô (2001), and Visiting Scholar/Professor positions at University of Texas at Austin (2008) and University of Sherbrooke (2009-2011). His research focuses on Digital Image and Video Forensics (e.g., deepfake detection), Image/Video Quality Assessment , Computational Vision , and Biometrics (fingerprint quality, template update, presentation attack detection). He leads the SAFE research group since 2016 and collaborates with the E-payment & Biometrics team at GREYC. His work integrates machine learning for quality metrics, biometric system evaluation, and forensic analysis. Recent publications highlight advancements in deepfake detection , 3D mesh quality assessment , and biometric security . Articles span journals like Intelligent Service Robotics (2024), IEEE Access (2024), and conferences such as CORESA (2024) and Cyberworlds (2023-2024). His studies on fingerprint systems, behavioral biometrics, and environmental impacts on data quality underscore his interdisciplinary approach. Scientific Awards : Best PhD Paper Award (ASONAM 2022) Best Full Paper Award (CW2022) He has mentored 14 PhD students since 2003, including notable alumni like Xinwei Liu (Zhejiang Wanli University) and Antoine Cabana (ALTEN, Toulouse). His projects span biometric certification, latent space manipulation, and 3D mesh evaluation, often in collaboration with institutions in Canada, Norway, and Morocco.
Alain Trouvé is a Professor at the Center for Mathematics and Their Applications (CMLA) within the Ecole Normale Supérieure de Cachan , France. His research focuses on Shape Spaces , Computational Anatomy , Imaging Processing , and Biological Imaging , with applications in medical and computational fields. He directs the Mathematics Department at ENS Cachan and contributes to neuroanatomical studies through diffeomorphometry techniques. Key roles include membership in the Conseil National des Universités (Section 26) and teaching responsibilities such as courses on Geometry and Shape Spaces and Probability Theory . His work spans from theoretical frameworks (e.g., Hamiltonian modeling of shape evolution) to practical applications like 3D cell imaging and white matter fiber analysis. Publications emphasize interdisciplinary methods, including diffeomorphic registration, varifold-based image analysis, and stochastic shape evolutions. Current projects explore multi-scale modeling of biological systems and AI-driven medical diagnostics. Key Research Themes: Diffeomorphic mappings, computational vision, and functional shape analysis. Teaching: Courses on geometric modeling and probability at undergraduate and graduate levels. Tools Developed: xIV-LDDMM Toolkit for multi-modal biomedical data analysis.
Mathieu Fontaine is an Associate Professor in Machine Listening at Télécom Paris , affiliated with the LTCI Lab within the IDS Department (Information, Data, Signal). His research focuses on machine listening for speech and audio signal processing. PhD in Informatics (2019), Lorraine University Master in Applied and Fundamental Mathematics (2015), Poitiers University BSc in Fundamental Mathematics (2013), Rennes University Fontaine's research spans speech enhancement , speaker separation , source localization , and music source separation using heavy-tailed probabilistic models and deep Bayesian networks , with applications in augmented reality . He has expertise in Python , signal processing , and machine learning (80% proficiency). His recent publications (2024) include work on diffusion models for speech synthesis , room acoustics estimation from 3D meshes , robust audio scene analysis , and direction-aware speech processing . Earlier publications (2022-2023) explore flow-based NMF , alpha-stable representations , and adaptive beamforming in multiparty environments. Fontaine collaborates with the S2A team and ADASP group at LTCI Lab. His work integrates probabilistic modeling with deep learning to address challenges in real-world audio processing, including reverberation, noise, and complex acoustic environments.
Dominique Bechmann is a **Professor of Computer Science** at the University of Strasbourg, affiliated with the **Department of Computer Science** within the **Sciences Collegium**. He is also a researcher at the **ICube Lab** (UMR 7357 CNRS-University of Strasbourg). His academic career spans over three decades, including roles as Head of the IGG Computer Graphics and Geometry research group (1997–2022) and Head of the National Research Group GdR IG-RV (2014–2021). He holds a Habilitation (1995) and PhD (1989) from the University of Strasbourg, with postdoctoral research at IBM's Thomas Watson Research Center (1989–1990). His research focuses on **Computer Graphics**, **Geometric Modeling**, and **Virtual Reality**, with key contributions in free-form deformation, 3D modeling of anatomical structures, and interaction techniques in immersive environments. He has led projects like the ICT-Asian initiative on Virtual Reality (2004–2007) and organized conferences such as AFRV 2012 and AFIG-EG France 2005. His teaching spans undergraduate and graduate levels in algorithms, computer graphics, and computational geometry. Bechmann has held numerous leadership roles, including Head of the Computer Science Department (1997–2000), Vice-Head of LSIIT Lab (2009–2012), and member of the National Commission of Universities (CNU) section 27 (2007–2011). His work bridges theoretical computer graphics with practical applications in medicine, architecture, and collaborative systems.
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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.
Virginie Ehrlacher Galland is a Professor at CERMICS (Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique) within École des Ponts ParisTech. Her expertise lies in applied mathematics, numerical analysis, and computational physics, with a focus on multiscale problems, quantum chemistry, and uncertainty quantification. She holds a PhD from CERMICS (2012) and a Habilitation (2020) from Université Paris-Dauphine. Her research interests include cross-diffusion systems, reduced basis methods, and optimal transport applications. Key contributions involve numerical methods for electronic structure calculations, homogenization techniques, and adaptive algorithms. She leads the ERC Starting Grant HighLEAP (2023-2028) and contributes to major projects like the ERC Synergy project EMC². Awards: Irène Joliot-Curie Prize (2023), Chevalier de l’Ordre National du Mérite (2025). Grants/Projects: ERC Starting Grant HighLEAP (PI), ERC Synergy EMC² (Member), ANR JCJC COMODO (PI). Her work bridges theoretical analysis and computational methods, addressing challenges in materials science, fluid dynamics, and machine learning applications.
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)
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Lionel Pichon is a leading researcher at the Laboratory of Electrical and Electronic Engineering of the University of Paris. His primary affiliations include the Department of Electrical and Electronic Engineering of Paris, where he specializes in advanced electromagnetic research. His work focuses on Electromagnetics , Electromagnetic Compatibility (EMC) , and Wireless Power Transfer , with particular emphasis on composite materials, biomedical applications, and automotive systems. Expertise in numerical methods (FDTD, FIT, DGTD) for electromagnetic simulations Pioneer in shielding effectiveness analysis for composite materials Developer of AI-driven approaches for dielectric property characterization Over 70 publications since 2017 highlight his contributions to fields like inductive power transfer systems, medical device EMC, and GPR imaging techniques. Collaborates extensively with institutions globally, including research on antenna design for implantable medical devices and electromagnetic compatibility in healthcare facilities. Current research trends emphasize machine learning integration with traditional electromagnetic analysis to optimize system performance and safety standards.
Véronique VEQUE is a Professor at CentraleSupélec, working within the L2S research laboratory. Her research focuses on networking technologies including wireless networks, vehicular networks, and satellite communications. She specializes in developing efficient routing protocols, mobility management systems, and energy optimization techniques for next-generation networks. Dr. Veque's current research examines flexible resource allocation in disaggregated RAN architectures, intelligent routing algorithms for dense WiFi networks, and predictive modeling for IoT congestion control. Her work combines theoretical modeling with practical implementations for telecommunications systems. Her publication record spans network architecture design, mobility modeling, routing optimization, and energy-efficient communication. Recent work has focused on Open-RAN functionality placement, Cloud-RAN resource allocation, and stochastic mobility modeling for urban environments.
