جست‌وجوی استاد و پژوهشگر

Pierre Sagautمشاهده پروفایل

استاد

• Turbulent Flows
• Aerodynamics
• Aeroacoustics
• +۵ مورد دیگر

Pierre Sagaut is a Professor at Aix-Marseille Université , leading research in the Instabilities, Turbulence and Couplings team. He serves as Editor-in-Chief of the Journal "Computers and Fluids" and holds editorial roles at Journal of Computational Physics , Journal of Turbulence , and Journal of Scientific Computing . His academic leadership extends to roles in the Scientific Council of the AFM (President) and ERCOFTAC (Vice-President). Research Interests focus on Lattice Boltzmann Method (LBM) for compressible/turbulent flows Aerodynamics, aeroacoustics, and aerothermics Uncertainty quantification and data assimilation Immersed boundary techniques for complex geometries Scientific Awards include the 2024 CNRS Bronze Medal Senior Member, Institut Universitaire de France Article Trends highlight LBM applications in nuclear reactor safety, urban pollutant dispersion, helicopter intake dynamics, and transonic flows. Recent works address mass leakage correction, hybrid RANS/LES turbulence modeling, and quantum-inspired lattice gas algorithms. Grants & Collaborations involve partnerships with CNRS, ERCOFTAC, and AFM, focusing on computational fluid dynamics and turbulence modeling.

Dominique Bechmannمشاهده پروفایل

استاد

• Computer Graphics
• Geometric Modeling
• 3-D Interaction in Virtual Reality
• +۳ مورد دیگر

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.

Anna Korbaمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Inference
• Sampling Methods
• +۴ مورد دیگر

Anna Korba is an Assistant Professor at CREST-ENSAE Paris within the Statistics Department. She holds an ENSAE Engineering degree in Data Science and a Master's in Mathematics, Vision & Learning (MVA) from ENSAE Paris. Her career includes a Ph.D. in Machine Learning at Télécom ParisTech, followed by a postdoctoral position at UCL's Gatsby Unit. Her research focuses on sampling techniques, Bayesian inference, optimal transport, and generative modeling, with recent work on constrained sampling and fairness integration. She contributes to collaborative efforts at the intersection of machine learning, dynamical systems, and PDEs. Notably, she co-presented tutorials on Wasserstein gradient flows at ICML 2022. Her work addresses unsolved challenges in sampling efficiency and fairness constraints. She is actively involved in CREST research initiatives and academic mentorship.

• Explainable Artificial Intelligence
• Machine Learning
• Data Clustering
• +۴ مورد دیگر

Nicolas Labroche serves as a Researcher in the Department of Computer Science at the Polytechnic School of the University of Tours. He is affiliated with the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT) within the UFR of Sciences and Technology. His academic work spans multiple research domains with significant contributions to machine learning methodology and applications. His primary research focuses on Explainable Artificial Intelligence , particularly for medical applications where model interpretability is critical. He has pioneered methods in clustering of local attributive explanations and discernibility in medical ML models . Additional expertise includes exploratory data analysis through mathematical programming for comparison queries, time series forecasting for environmental monitoring, and preference-based explanations in recommender systems. His work bridges theoretical computer science with practical applications in healthcare, environmental science, and business intelligence. Analysis of his publication trends reveals consistent innovation in making data exploration more efficient and interpretable. Recent work emphasizes user-centric explainability (2024-2025), mathematical optimization for analytics (2022-2023), and synthetic data generation (2022). His research demonstrates strong interdisciplinary connections between computer science, medicine, and environmental science. His laboratory affiliation with LIFAT provides the infrastructure for his computational research. While specific grant details aren't public, his publication record indicates sustained research productivity across multiple European-funded projects focused on data science applications.

Ludovic Sacchelliمشاهده پروفایل

پژوهشگر

• Control Theory
• Sub-Riemannian Geometry
• Optimal Control
• +۷ مورد دیگر

Ludovic Sacchelli is an Inria researcher (CR) affiliated with the McTAO team at the Centre Inria d'Université Côte d'Azur and the Laboratoire J.A. Dieudonné of Université Côte d'Azur. His research spans control theory, sub-Riemannian geometry, and mathematical neuroscience, focusing on optimal control, observers, and estimation problems. His work on sub-Riemannian manifolds and control systems includes stabilization techniques for non-uniformly observable systems and applications to UAV control, neural fields, and bioprocess modeling. He has contributed to heat kernel analysis, line fields interpolation, and geometric models for sound processing. His recent publications address topics like distributed state estimation in neural models, polynomial state-affine control systems, and geometric algorithms for orientation field interpolation. He has also explored observability singularities in bilinear systems and stabilization of weakly contractive systems. Teaching roles include instructing Measure Theory , Stochastic Processes , and applied mathematics at institutions such as Université Côte d'Azur, Polytech Nice, Lehigh University, and École Polytechnique. His mentorship includes supervising a Masters research project on numerical implementation of line fields interpolation in 2019.

Boris Bordeauxمشاهده پروفایل

پژوهشگر

• Geometric Modeling
• Fractal Structures
• Lightweight Structures
• +۲ مورد دیگر

Boris Bordeaux is a PhD researcher at the University of Burgundy , affiliated with the Geometric Modeling team within the LIB Laboratory . His research focuses on the design of lightweight structures and computational geometric modeling. Research Work: Boris has contributed to the development of fractal structures with locally controlled lacunarity, presented at the International Conference in Central Europe on Computer Graphics (WSCG 2024). This work intersects geometric modeling and structural optimization. Teachings : He delivers 54 hours of practical work in Year 1 Computer Science courses. Team Affiliation : Geometric Modeling team, LIB Laboratory, University of Burgundy

Mathieu Brédifمشاهده پروفایل

پژوهشگر

• Lidar Processing
• 3D Reconstruction
• Geovisualization
• +۵ 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.

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.

Thibault Tricardمشاهده پروفایل

دانشیار

• Computer Graphics
• Procedural Modeling
• 3D Printing
• +۵ مورد دیگر

Thibault Tricard is an Associate Professor at Ensimag Grenoble and a permanent researcher in the Maverick team at Inria Grenoble. His work bridges Computer Graphics and Additive Manufacturing , focusing on procedural methods for virtual world content generation. His research interests include: Procedural modeling for surface and microstructure details Mesh shaders and GPU-accelerated volume rendering 3D printing optimization with staggered infill structures Recent publications like Interval Shading (HPG 2024) and Procedural Phasor Noise (ACM Siggraph 2020) highlight trends in real-time rendering and fabrication-aware procedural synthesis. Awards include the GDR IG-RV Thesis Award 2023 . He advises PhD students and collaborates with researchers such as Sylvain Lefebvre and Hans-Peter Seidel. Teaching includes GP-GPU High-Performance Computing and 3D Graphics at Inp Grenoble and UGA Mosig. Personal projects like the LavaCake Vulkan library emphasize developer accessibility.

• Quantum Chemistry
• Uncertainty Quantification
• Numerical Analysis
• +۵ مورد دیگر

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.

Sophie Huibertsمشاهده پروفایل

پژوهشگر

• Linear Programming
• Mixed Integer Programming
• Algorithm Analysis
• +۴ مورد دیگر

Sophie Huiberts is a CNRS researcher at LIMOS, Clermont Auvergne University in Clermont-Ferrand since fall 2023. Previously, she was a Simons Junior Fellow at Columbia University in New York City, hosted by Tim Roughgarden. She completed her PhD research at Centrum Wiskunde & Informatica in Amsterdam under Daniel Dadush and received her doctorate in 2022 from Utrecht University. Dr. Huiberts specializes in theoretical aspects of mathematical optimization, particularly focusing on the gap between practical performance and theoretical predictions of linear programming algorithms. Her research examines software implementations like Gurobi, CPLEX, SCIP, and HiGHS to understand why these algorithms perform better in practice than worst-case analysis would suggest. She has made significant contributions to smoothed analysis of the simplex method, establishing both upper and lower bounds on its complexity under perturbations of worst-case inputs. Analysis of her publication record shows consistent focus on bridging theoretical computer science with practical optimization methods. Her work spans linear programming theory, integer programming, combinatorial optimization, and computational geometry, with particular emphasis on understanding the geometric properties of optimization problems and the behavior of algorithms on real-world instances. Simons Junior Fellowship Dr. Huiberts maintains active engagement with the research community through social media platforms including Mastodon and Bluesky, and produces high-quality recordings of her research talks available on YouTube. She has made a conscious decision to stop air travel since 2023 due to climate concerns, demonstrating commitment to sustainable research practices while maintaining scientific connections through digital means. She is affiliated with LIMOS (Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes), a research laboratory at Clermont Auvergne University focused on computer science, modeling, and optimization systems, where she continues her investigations into the theoretical foundations of practical optimization algorithms.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +۵ مورد دیگر

Alain Durmus is a Professor at École Polytechnique, affiliated with the CMAP (Applied Mathematics Department). His research focuses on computational statistics, machine learning, and stochastic methods for Bayesian inference and generative models. Key areas include Monte Carlo methods, stochastic optimization, and Riemannian geometry in optimization. He has contributed to theoretical advancements in Markov processes and their applications in sampling algorithms. Affiliations: École Polytechnique (CMAP), ENS Paris-Saclay (teaching roles), and other academic collaborations. Teaching: Courses on mathematical statistics, stochastic methods, probability theory, and optimization at ENS Paris-Saclay and École Polytechnique. Research Interests: Development of Monte Carlo algorithms, stochastic approximation schemes, and theoretical analysis of Markov chain convergence. Specializes in Bayesian statistics, generative models, and high-dimensional inference. His work bridges theory and application, with notable contributions to Langevin dynamics, nonreversible MCMC methods, and federated learning. He has received a Best student paper award at ICASSP 2020 for collaborative research on Bayesian computation using Sliced-Wasserstein distances. Alain’s publications span prestigious journals like the Annals of Applied Probability and SIAM, with a focus on algorithmic innovation and rigorous mathematical foundations.

• Fluid Mechanics
• Acoustics
• Turbulence Modeling
• +۴ مورد دیگر

Denis Jeandel is a Researcher at the Laboratory of Fluid Mechanics and Acoustics (LMFA), University Claude Bernard Lyon 1, France. He specializes in turbulence modeling, flow instability analysis, and fluid dynamics. His work encompasses a wide range of applications, including electric arc dynamics, combustion chamber aerodynamics, and numerical methods for multiphysics problems. He is part of the Turbulence & Instabilities research team and contributes to advancements in computational fluid dynamics (CFD), finite element methods, and boundary layer studies. Research Interests: Turbulence Modeling and Simulation Electric Arc Behavior in Circuit Breakers Internal Aerodynamics of Combustion Chambers Thermal-Hydraulic Phenomena Lagrangian Stochastic Modeling for Atmospheric Dispersion Shock-Wave and Multiphase Flow Dynamics Publications Highlight Key Contributions: His work spans numerical methods for second-order turbulence closures, coupled electromagnetic-aerothermal simulations, and boundary layer stability analysis. Recent publications focus on improving accuracy in high-speed flow simulations and electric arc initiation modeling. Awards and Recognition: No specific awards mentioned in the provided texts, though his sustained research output indicates active recognition in the field. Lab and Team Involvement: He collaborates with the Turbulence & Instabilities team at LMFA to address complex fluid dynamics challenges, leveraging state-of-the-art experimental and numerical tools.

Tony LELIEVREمشاهده پروفایل

استاد

• Applied Mathematics
• Multiscale Modeling
• Molecular Simulation
• +۷ مورد دیگر

Tony Lelièvre is a Professor of Applied Mathematics at the Ecole Nationale des Ponts et Chaussées, part of the Institut Polytechnique de Paris. He holds a PhD (2004) and Habilitation (2009), specializing in multiscale modeling, molecular simulation, and stochastic processes. His research focuses on free energy computations, numerical analysis of complex fluids, and computational statistical physics. He co-authored two books and over 100 papers, receiving significant awards like the ERC Consolidator Grant (2013-2019) and the Grand prix Alcan. Lelièvre has organized numerous international workshops and serves on editorial boards of journals like ESAIM:M2AN and SIAM/ASA Journal of Uncertainty Quantification. His work bridges applied mathematics, numerical analysis, and computational science with applications in materials science and industrial fluid dynamics. Education: PhD in Applied Mathematics, 2004 Habilitation à Diriger des Recherches, 2009 Research Interests: Multiscale modeling of complex fluids Molecular dynamics and free energy calculations Stochastic methods for metastable systems Numerical analysis of PDEs and SDEs Computational statistical physics Recent Contributions: His work on adaptive biasing force methods and hybrid Monte Carlo techniques has advanced the simulation of rare events and free energy landscapes. He contributed to variance reduction techniques in molecular simulations and mathematical analysis of parallel replica algorithms. Awards: ERC Consolidator Grant (2013-2019), Prix CS 2002, Grand prix Alcan (2010), Ordway Visiting Professorship (2012-2013), and several teaching awards. Professional Activities: Organized major conferences like CEMRACS 2013 and IPAM Long Program on Energy Landscapes (2017). Co-edits journals and authored influential textbooks on magnetohydrodynamics and free energy computations.

Francois Chaumetteمشاهده پروفایل

استاد پژوهشی

• Robotics
• Computer Vision
• Visual Servoing
• +۱۷ مورد دیگر

François Chaumette is a Senior Research Scientist (Directeur de recherche) at Inria, affiliated with IRISA and the Centre Inria de l'Université de Rennes. He has been a key researcher in robotics and computer vision since 1990 and led the Lagadic research team from 2004 to 2017. His research interests are centered on robot vision, particularly visual servoing and active perception . He has made foundational contributions to image-based and position-based visual servoing, and his work integrates control theory, computer vision, and robotics. His research spans applications in mobile robotics, aerial systems, medical robotics, space robotics, and soft object manipulation. The recent publications highlight a consistent focus on visual servoing under complex constraints—such as motion blur, occlusions, and deformations—applied to drones, cable-driven robots, and space systems. There is a strong emphasis on robustness , stability analysis , and hybrid sensing (e.g., vision + proximity, vision + force). His work with the RemoveDebris mission demonstrates real-world impact in space robotics. AFCET/CNRS Prize for best Ph.D. in Automatic Control Best paper awards at RFIA 1996 & 2004 Best paper in IEEE T-RA (2002) Best paper in IEEE RA-L (2019) Best paper in IEEE RAM (2020) IEEE Fellow (2013) He has advised over 30 Ph.D. students, many of whom have become active researchers in robotics. He has served in editorial roles for top journals including IEEE Transactions on Robotics , IEEE Robotics and Automation Letters , and the International Journal of Robotics Research . He was elected to the IEEE RAS Administrative Committee (2016–2018) and served on ERC grant panels for robotics. Chaumette is the main developer of ViSP (Visual Servoing Platform), a widely used C++ library for visual tracking and servoing. His leadership in both theoretical advances and software tools has significantly shaped the visual servoing community.