جستجوی استاد راهنمای دانشگاهی

Yuki M. Asano is a full Professor at the University of Technology Nuremberg , leading the Fundamental AI (FunAI) Lab . Previously, he led the QUVA Lab at the University of Amsterdam and earned his PhD at the Visual Geometry Group (VGG) of the University of Oxford under Andrea Vedaldi and Christian Rupprecht. University of Technology Nuremberg (2024–present) University of Amsterdam (prior to 2024) University of Oxford (PhD, 2020) His research spans Artificial Intelligence , Machine Learning , and Computer Vision , with a focus on Causal Representation Learning , Self-Supervised Learning , and Efficient Model Adaptation . He pioneered techniques like BISCUIT (causal variable identification) and VeRA (parameter-efficient fine-tuning). His work extends to Medical Imaging and Environmental Monitoring through applications in fetal ultrasound analysis and marine debris detection. Recent publications (2023–2025) highlight advancements in Self-Supervised Learning , Vision-Language Models , and 3D Understanding . Notable papers include TWIST & SCOUT (multimodal LLM grounding), SIGMA (masked video modeling), and GeneralAD (anomaly detection). His ICCV 2023 work on Self-Ordering Point Clouds and MoSiC (optimal-transport motion trajectories) underscores his interdisciplinary approach. He received the JUPITER compute grant (2025) and an Outstanding Paper Award at ICLR 2024 . His collaborations span institutions like MIT-IBM Watson AI Lab, Qualcomm AI Research, and University of Amsterdam.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۶ مورد دیگر

Stefano NASINI is an Associate Professor at the University of Lille 3, specializing in Quantitative Methods within the Economics and Mathematics Sciences. He holds a HDR (Habilitation à Diriger des Recherches) from the University of Lille 3 (2021), a Ph.D. in Statistics and Operations Research from the Polytechnic University of Catalonia (2015), and a Master in Statistics (2011). His research focuses on optimization, complex networks, statistical inference, and microeconomic applications. He has held academic positions including a post-doctoral role at IESE Business School (2014–2016) and a visiting researcher role at the University of Lisbon (2014). His work spans scheduling optimization, network analysis, financial contagion modeling, and energy system planning. Key contributions include specialized algorithms for large-scale optimization problems and frameworks for decentralized portfolio management. He is a member of the LEM research group and teaches courses in optimization, econometrics, and social network analysis at the Grande École and MSc levels. Publications highlight interdisciplinary applications, including network-based diffusion models, multi-market financial strategies, and dynamic choice analysis. His research bridges theoretical advancements in operations research with practical challenges in economics, energy, and transportation systems. No scientific awards are explicitly listed in the provided materials. His advising roles and grants are not detailed here, but his extensive publication record reflects active collaboration within academic and applied domains.

• Optimization
• Complex Networks
• Statistical Inference
• +۳ مورد دیگر

Jean-François Le Gall is a full Professor at Université Paris-Saclay and a member of the Orsay Mathematics Laboratory (LMO) since 2006. He has held prominent positions at Pierre and Marie Curie University (1988-2006) and École Normale Supérieure (1997-2007). A Senior Member of the University Institute of France (2007-2017) and an elected member of the Academy of Sciences since 2013, he served as Vice-President of Research for the Mathematics Department at Orsay (2020–present) and led the ERC Advanced Grant GeoBrown (2017–2023). Education: Ecole Normale Supérieure (1978–1982), PhD in stochastic differential equations (1982), State Doctorate on Brownian motion (1987) Research Interests focus on probability theory , particularly Brownian motion , superprocesses , random trees , planar maps , and their connections to PDEs and geometric models. His work bridges stochastic analysis , branching processes , and coalescence phenomena . Selected Publications include foundational studies on the Brownian map , random geometry , and spatial branching processes . His 2025 paper on The area of spheres in the Brownian plane explores fractal properties of random metric spaces, while the 2020 Growth-fragmentation processes work links Brownian trees to fragmentation models. Scientific Distinctions : 1986 Rollo Davidson Prize 1997 Loève Prize in Probability 2005 Sophie Germain and Fermat Prizes 2019 Wolf Prize in Mathematics 2022 BBVA Frontiers of Knowledge Award Academic Leadership includes directing the Probability and Statistics Team (2013–2019) and the Master 2 in Probability and Statistics (2007–2015). He chairs editorial roles in Grundlehren der mathematischen Wissenschaften (since 2020) and Probability Theory and Related Fields (2005–2010).

• Probability
• Brownian motion
• Lévy processes
• +۸ مورد دیگر

Alastair F. Donaldson is a Professor and Director of Research in the Department of Computing at Imperial College London, where he leads the FastPL research group. His work bridges formal methods, software testing, and programming languages, with a focus on enhancing the reliability of high-performance and parallel software systems. He has held key roles including Director of Research (since 2023) and previously served as Lecturer (2011–2014), Senior Lecturer (2014–2017), and Reader (2017–2020) before being promoted to Professor in 2020. His research interests include formal verification, compiler testing, GPU programming, concurrency, and fuzzing. He has made significant contributions to the verification of GPU kernels, metamorphic testing of graphics drivers, and the development of tools like GPUVerify and GraphicsFuzz. His work combines theoretical rigor with practical impact, demonstrated by the acquisition of his startup GraphicsFuzz by Google in 2018 and his subsequent roles as Senior Software Engineer and Visiting Researcher at Google. His recent publications reflect a sustained focus on compiler and system reliability, with trends in fuzzing, formal specification, and automated testing of complex systems such as WebGPU, CXL cache coherence, and large language models for code generation. His work increasingly integrates empirical validation with formal techniques to uncover subtle bugs in real-world systems. Scientific awards and recognitions include: 2017 BCS Roger Needham Award EPSRC Early Career Fellowship Fellow of the British Computer Society Best Paper awards at EuroSys 2024, MET 2021, IISWC 2019, IWOCL 2019, and ICST 2016 Best Industry Paper at ICST 2024 ACM SIGSOFT Distinguished Paper at ISSTA 2023 ACM SIGPLAN Most Influential OOPSLA Paper Award (2012 paper), awarded in 2022 Best Student Paper at PPoPP 2014 He has advised numerous PhD students and leads a vibrant research group. He has secured significant research funding and collaborates extensively with industry and academia. His service includes leadership roles such as General Chair of PLDI 2020, PC Chair of ECOOP 2019, and Steering Committee Chair of PLDI (2022–2025). He also serves on the advisory board of PACM-PL and on program committees for top venues including POPL, OOPSLA, PLDI, ICSE, and ISSTA. He leads the FastPL research group, which focuses on the design and implementation of programming tools and techniques for reliable software. The group conducts cutting-edge research in compiler testing, formal methods, and high-performance systems, fostering collaboration across academia and industry.

• Formal Verification
• Software Testing
• Programming Languages
• +۱۲ مورد دیگر

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

Hirokatsu Kataoka serves as Chief Senior Researcher at the National Institute of Advanced Industrial Science and Technology (AIST) in Japan, with multiple academic affiliations including Academic Visitor at the Visual Geometry Group (VGG) at University of Oxford, Visiting Associate Professor at Keio University, and Adjunct Associate Professor at Tokyo Denki University. He is Principal Investigator of both cvpaper.challenge and LIMIT.Lab, and serves as Research Advisor for SB Intuitions. Dr. Kataoka earned his Ph.D. in Engineering from Keio University (April 2011 - March 2014), where he received the Fujiwara Prize in 2014 as valedictorian equivalent. His research primarily focuses on innovative pre-training methodologies that eliminate dependency on natural image datasets, with his Formula-Driven Supervised Learning (FDSL) framework being particularly influential in the field. Kataoka's research interests center around representation learning with limited data resources, including zero-shot, unsupervised, and synthetic learning approaches. His work explores how visual/multimodal models can be effectively trained with minimal real-world data, addressing critical ethical concerns related to large-scale datasets. He has pioneered methods using fractal geometry, mathematical formulas, and procedural generation to create effective pre-training frameworks that rival traditional ImageNet-based approaches. His publication record shows a clear trajectory toward solving the challenges of learning with limited resources, with recent work expanding FDSL to audio processing, microfossil analysis, and visible-to-infrared translation. His papers consistently address the core challenge of building robust visual recognition systems without relying on massive annotated datasets, with increasing focus on practical applications across diverse domains. Scientific Awards & Recognition ACCV 2020 Best Paper Honorable Mention Award for 'Pre-training without Natural Images' AIST Best Paper Award (2019, 2022) BMVC 2023 Best Industry Paper Finalist Featured in MIT Technology Review His 3D ResNets paper ranks among the top 0.5% most-cited CVPR papers over a five-year period Dr. Kataoka actively advises numerous researchers across multiple institutions, with his research team comprising Ph.D. and Master's students from various universities. He has served as Area Chair for CVPR 2024 and 2025, will serve as IEEE TPAMI Associate Editor beginning in 2025, and organizes the LIMIT Workshop series at major computer vision conferences. His LIMIT.Lab, established in June 2025, serves as a collaboration hub focused on building multimodal AI models under constrained resources including compute, data, and labels.

• Computer Vision
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Thomas Walter is a Professor at Mines ParisTech and Director of the Centre for Computational Biology (CBIO) , a research group affiliated with the Institut Curie and INSERM . His work focuses on applying Machine Learning and Computer Vision to biomedical image analysis, particularly in high-content screening and computational pathology . He also serves as Deputy Director of the Computational Oncology (U1331) unit and leads the Statistical Learning and Modeling of Biological Systems team. PhD in Medical Image Analysis (2003, Mines ParisTech) Postdoctoral work at EMBL (European Molecular Biology Laboratory) Director of CBIO since 2018 Holder of a PRAIRIE Chair (Paris Artificial Intelligence Research Institute) since 2019 Dr. Walter's research bridges biomedical imaging , machine learning , and cancer genomics . Key areas include: Statistical reconstruction of biological networks Prediction of tumor progression at genomic/transcriptomic levels Development of deep learning methods for cell cycle analysis Integration of multi-omics data for precision oncology Tools for spatial transcriptomics (e.g., autoFISH, RNA2seg) Recent publications highlight his work in spatial transcriptomics , immunotherapy outcome prediction , and deep learning for digital pathology . His team has developed open-source tools like FISH-quant and pyHiM for single-molecule RNA imaging analysis. Scientific Honors: PRAIRIE Chair (2019) for AI research in life sciences Dr. Walter actively contributes to teaching deep learning for image analysis in multiple graduate programs across France, including courses at Mines ParisTech , Université Paris-Saclay , and Institut Curie . His software tools (FISH-quant, pyHiM) and methodological frameworks (e.g., Cut-Detector, PointFISH) have become standard resources in bioimage informatics.

• Biomedical Image Analysis
• Machine Learning
• Computational Pathology
• +۴ مورد دیگر

Pierre Marion is a Researcher at INRIA Paris, working within the Sierra research team since September 2025. His work focuses on the theoretical foundations of deep learning and he is beginning to explore applications of AI in mathematics. Marion has established collaborations across multiple institutions including EPFL, Sorbonne Université, and Google DeepMind. His educational background includes: Engineering degree from École polytechnique (2015-2018) with specialization in Applied Mathematics Master's degree from Sorbonne Université (2019-2020) PhD from Sorbonne Université (2020-2023) under the supervision of Gérard Biau and Jean-Philippe Vert Postdoctoral research at EPFL (2024) supervised by Lénaïc Chizat Marion's research interests primarily focus on the theory of deep learning, where he investigates the optimization and statistical properties of various neural network architectures. His work spans from shallow networks to complex generative models, with a particular emphasis on understanding the mathematical foundations that govern deep learning performance. Recently, he has begun exploring applications of AI in mathematical research, aiming to bridge the gap between theoretical machine learning and mathematical discovery. His research often combines rigorous theoretical analysis with practical implications for training deep neural networks. Analysis of Marion's recent publications reveals several key trends in his research. He has made significant contributions to understanding the role of large learning rates in optimization dynamics, demonstrating how they can accelerate convergence in logistic regression and prevent memorization in score-based generative models. His work on attention mechanisms has provided theoretical guarantees for their effectiveness in specific tasks like single-location regression and clustering. Additionally, Marion has extensively studied the connections between residual networks and neural ordinary differential equations , establishing generalization bounds and exploring scaling properties in the large-depth regime. His earlier work included contributions to natural language processing and quasi-Monte Carlo methods, reflecting a broad mathematical foundation that informs his current deep learning research. Marion has received several notable scientific awards: Runner-up PhD Award of AFIA (French Association for Artificial Intelligence) in 2024 Google PhD Fellowship in 2022 Ecole polytechnique Grand Prize of Research Internships in 2018 As an advisor, Marion currently supervises PhD student Yu-Han Wu (since 2024), with whom he has co-authored multiple publications on large learning rates and denoising score matching. Previously, he co-supervised several Master's students including Seorim Park, Yerkin Yesbay, and Nathan Doumèche. Marion has been actively involved in the machine learning community through conference organization (NeurIPS@Paris meetups), session chairing (ICSDS 2022), and extensive reviewing activities. He has served as a reviewer for top journals including JASA and Annals of Statistics, and conferences including NeurIPS and ICLR, where he was recognized as a top reviewer at NeurIPS 2023. Marion is a member of the Sierra research team at INRIA Paris, which focuses on machine learning theory and applications. He has also collaborated with researchers at CREST (Center for Research in Economics and Statistics), as evidenced by his participation in seminars organized by Anna Korba and Karim Lounici. His work often bridges theoretical computer science, statistics, and applied mathematics, reflecting the interdisciplinary nature of modern machine learning research.

• Deep Learning Theory
• Optimization of Neural Networks
• Generative Models
• +۲ مورد دیگر

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).

• Mesh generation
• Numerical simulation
• Finite element methods
• +۵ 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

François Baccelli is a Research Director at INRIA Paris and Visiting Professor at Télécom Paris, leading the ERC Advanced Grant NEMO project on network mathematics. He holds a Doctor Honoris Causa from Heriot-Watt University and is a Member of the French Academy of Sciences. His career spans key roles including head of the Simons Center on Communication, Information and Network Mathematics (2012–2019), and faculty positions at École Polytechnique (1991–2003) and the University of Texas at Austin (2012–2021) as Simons Math+ECE Chair. Education: PhD in Applied Mathematics from Université Paris-Sud (1983), advised by Erol Gelenbe. Graduate of Télécom Paris (1977). Research Interests: Baccelli’s work bridges applied probability, stochastic geometry, and network dynamics with applications to telecommunications. He pioneered spatial stochastic networks, stochastic geometry modeling of wireless systems, and max-plus algebra for discrete-event systems. His contributions include foundational theories on queuing networks, Palm probabilities, and latency-constrained system design. Awards & Recognition: 2024 Blackwell Prize (INFORMS Applied Probability Society). 2014 ACM Sigmetrics Achievement Award. 2014 IEEE Stephen O. Rice and Leonard G. Abraham Prizes. Collaborations: Co-founder of the LINCS joint laboratory (INRIA/IMT/Nokia Bell Labs/Sorbonne/SystemX), fostering interdisciplinary research in communications and networks. Supervised numerous graduate students who now lead academic and industrial research globally. Labs & Teams: LINCS laboratory and ERC NEMO project, focused on network mathematics and large-scale system dynamics.

• Applied Probability
• Stochastic Geometry
• Wireless Networks
• +۴ مورد دیگر

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.

• Geometry Processing
• Computer Graphics
• Computational Geometry
• +۵ مورد دیگر

Margherita Pagani is a Full Professor and Director of the SKEMA Centre for Artificial Intelligence at SKEMA Business School in Paris. She holds a Ph.D. in Management and an HDR (Habilitation à Diriger des Recherches). Her roles include Research Affiliate at UC Berkeley’s APEC Study Center and Advisor to the European Economic Social Committee (EESC) on virtual worlds and the Metaverse. Previously, she founded and led the MSc in Digital Marketing & Data Science and the AIM Research Center at emlyon business school. She has held visiting roles at MIT, UCLA, Georgetown, NUS, and Redlands University. Education: Ph.D. (2015, Lyon), HDR (2016, Évry), MSc (1995, Bocconi), MIT Certifications (2005-2006). Research: Focuses on AI in consumer engagement, digital ecosystems, robotics in services, and metaverse applications. Over 130 publications in journals like MIS Quarterly, MIT Sloan Management Review, and Harvard Business Review. Awards: 2023 International Marketing Trends Award, 2022/2023 Top 2% Cited Scientist (Elsevier), 2009 Mobile Marketing Academic of the Year. Her work bridges AI and marketing creativity, emphasizing human-centric systems. She leads initiatives like the SKEMA AI School and organizes conferences on ethics, AI in education, and service recovery. Active in editorial roles for journals like Micro & Macro Marketing and Industrial Marketing Management . Key affiliations include the Association Française de Marketing and involvement in policy initiatives. Supervised over a dozen PhD students, focusing on AI applications in marketing and entrepreneurship.

• Artificial Intelligence for Business
• Consumer Behavior and Digital Technologies
• Digital Platforms
• +۳ مورد دیگر

Wouter Bos is a Research Director at CNRS working at the Laboratory of Fluid Mechanics and Acoustics (LMFA) at École Centrale de Lyon, France. He leads research within the Turbulence & Instabilities team, focusing on fundamental aspects of fluid dynamics with applications spanning from plasma physics to epidemiology. His academic journey reflects a deep engagement with theoretical and computational fluid mechanics, particularly in turbulence phenomena. Dr. Bos's research interests center on fluid dynamics, with particular emphasis on turbulence in various contexts including two-dimensional flows, magnetohydrodynamics, plasma physics, and statistical mechanics of fluids. His work explores fundamental questions about energy transfer, coherent structures, and statistical properties of turbulent flows. He has made significant contributions to understanding turbulence without vortex stretching, two-dimensional turbulence, and the application of fluid dynamics principles to epidemiological modeling. Analysis of his recent publications reveals a strong focus on theoretical and computational approaches to turbulence. His work spans from fundamental questions about equilibrium states in two-dimensional turbulence to practical applications in plasma confinement and epidemic modeling. The publications demonstrate consistent innovation in turbulence theory, with particular attention to statistical mechanics approaches, spectral analysis, and the development of reduced-order models for complex fluid phenomena. His research shows growing interdisciplinary connections, especially between fluid dynamics and epidemiology as evidenced by his work on modeling the spread of infectious diseases. Dr. Bos actively supervises doctoral students and postdoctoral researchers, with recent students including Tong Wu, Ryo Araki, Smiron Varghese, Wesley Agoua, and Bruce (Xi Yuan) Yin. He participates in collaborative research projects such as the ANR CM2E project (2021-2025) on Characteristic Mapping Method for the Euler Equations, working with researchers from Aix-Marseille University and McGill University. His laboratory work involves both theoretical analysis and computational simulations, with applications ranging from fundamental fluid mechanics to practical problems in energy research (particularly related to ITER and fusion plasma physics) and public health. The interdisciplinary nature of his research demonstrates the broad applicability of fluid dynamics principles across seemingly disparate scientific domains.

• Fluid Dynamics
• Turbulence
• Magnetohydrodynamics
• +۳ 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