جست‌وجوی استاد و پژوهشگر

Marie-Paule Caniمشاهده پروفایل

استاد

• Computer Graphics
• Shape Modelling
• Computer Animation
• +۷ مورد دیگر

Marie-Paule Cani is a Professor of Computer Science at École Polytechnique (since May 2017), on leave from Grenoble INP and Inria . She leads the STREAM team at the LIX laboratory (CNRS, École Polytechnique) and collaborates externally with the IMAGINE team at Inria. Her research focuses on advancing intuitive methods for creating 3D shapes and virtual worlds, emphasizing user control and model-based knowledge. Education: 1987: M.Sc. in Computer Science, École Normale Supérieure & University Paris XI 1990: Ph.D. in Computer Graphics, University Paris XI (advisor: Claude Puech) 1995: Habilitation in Computer Science, Institut National Polytechnique de Grenoble Research Interests: Her work spans shape modeling , computer animation , and procedural modeling . She pioneered methods such as implicit surfaces, physically-based animation, and sketch-based interfaces. Recent projects include combining procedural models with intuitive user interactions to streamline 3D content creation. Awards & Recognition: ERC Advanced Grant (2011) for the EXPRESSIVE project 2011 Eurographics Technical Contributions Award 2012 CNRS Silver Medal 2013 Election to Academia Europaea Leadership & Service: She created and led the STREAM (2017–present), IMAGINE (2011–2016), and ÉV@NCE (2003–2010) research teams. She has served as President of Eurographics (2017–present), Technical Paper Chair of SIGGRAPH 2017 , and on editorial boards of ACM Transactions on Graphics , Computer Graphics Forum , and others. Labs & Teams: Active in the LIX laboratory and collaborates with Inria's IMAGINE team. Her work bridges academic research and industrial applications in computer graphics and animation.

Sidonie Christopheمشاهده پروفایل

عضو هیئت علمی

• Geographic Information Sciences
• Geovisualization
• Map Design
• +۱۲ مورد دیگر

Sidonie Christophe is a Senior Researcher (Directrice de Recherche, DR1) at UMR LASTIG, a joint research unit of Université Gustave Eiffel, IGN-ENSG, and EIVP. She serves as co-director of the LASTIG laboratory and leads research in geovisualization, map design, and interactive spatial data exploration. She also holds a part-time advisory role (60%) at the French Ministry of Higher Education and Research in the domain of digital technology, environment, climate, and sustainable urban development. PhD in Geographic Information Sciences Senior Researcher, DR1, MTECT Co-Director, LASTIG Laboratory (since 2021) Former Team Leader, GEOVIS (Geovisualization, Interaction, and Immersion) Advisor, Environment and Urban Climate, French Ministry of Higher Education & Research Her research centers on innovative methods for 2D/3D and nD geospatial data visualization, with a focus on enabling spatio-temporal understanding through visual and non-visual spatial thinking. Her work integrates principles from geographic information science, human-computer interaction, and computer graphics. Key areas include urban climate visualization, tactile and augmented reality for accessibility, expressive cartographic rendering, and cognitive aspects of map design. She investigates how aesthetic and semiotic choices impact map comprehension and utility. The 15 most recent publications reflect a strong trend in interactive and accessible geovisualization, particularly for urban and environmental applications. Topics include neural map style transfer, 3D urban climate analysis, tactile maps for the visually impaired, augmented reality in geography, and visual analytics for crisis and climate data. There is a consistent emphasis on user-centered design, interdisciplinary integration, and the development of tools for decision-making under uncertainty. Scientific Recognition and Service: Invited speaker at major conferences (IEEEVIS, AGILE, ICC, CPGIS) Co-organizer of international workshops (e.g., GeoVIS, ISPRS AR/VR sessions) Leader of national research projects (ANR ORACLES, ANR ACTIVmap, ANR ECOCIM) Recipient of international mobility grants (AMICI I-SITE FUTURE) Contributor to national glossaries and research strategy (e.g., French photogrammetry glossary) Advising and Grants: Sidonie Christophe actively supervises PhD students and postdoctoral researchers, including Markie Jiang, Maria-Jesus Lobo, and Alexandre Mielniczek. She leads or participates in multiple funded research projects such as ANR ORACLES (marine flooding visualization), ANR ACTIVmap (tactile 3D maps), and ANR ECOCIM (eco-design of city information models). Her advisory role at the MESR involves shaping national research strategy in digital and environmental sciences. Labs and Research Teams: She is a core member of the GEOVIS team (Geovisualization, Interaction, and Immersion) at LASTIG and previously served as its leader. As co-director of LASTIG, she plays a central role in the leadership and strategic direction of the entire laboratory, which comprises over 100 researchers across four teams: ACTE, GEOVIS, MEIG, and STRUDEL.

Yohan PETETINمشاهده پروفایل

دانشیار

• Monte Carlo methods
• Bayesian filtering
• Hidden Markov models
• +۵ مورد دیگر

Yohan PETETIN is an Associate Professor at Telecom SudParis (Institut polytechnique de Paris) in the CITI Department. His research focuses on Bayesian filtering, Monte Carlo methods, hidden Markov models, and multi-object tracking. He has authored over 20 peer-reviewed articles since 2011, with notable contributions in IEEE Transactions on Signal Processing and other top venues. His work bridges statistical signal processing with machine learning applications. PhD: Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles Markoviens (2013, Telecom SudParis) HDR: Generative models for time series data (2023, Institut polytechnique de Paris) Research interests emphasize sequential Monte Carlo algorithms, particle filtering optimizations, and deep learning integration for time-series analysis. Recent work explores expressivity comparisons between recurrent neural networks and hidden Markov models. Teaching includes courses on probabilistic graphical models, Bayesian filtering, and deep learning across undergraduate and graduate programs at Telecom SudParis and affiliated institutions.

Ammar Mianمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Machine Learning
• Riemannian Geometry
• +۴ مورد دیگر

Ammar Mian is an Associate Professor at Université Savoie Mont Blanc, affiliated with the LISTIC lab and Polytech Annecy-Chambéry. He holds a PhD from CentraleSupélec (2016-2019) and conducted postdoctoral research at Aalto University (2019-2020). His research focuses on statistical signal processing, machine learning, and Riemannian geometry with applications in remote sensing and frugal computations. He leads the Qanat project, an experiment tracking tool for reproducible research. Research interests include covariance-based methods for SAR image analysis, robust detection algorithms for sonar and GPR systems, and optimization on Riemannian manifolds. His work emphasizes reproducibility in ML and efficient computational techniques for resource-constrained environments. Key contributions include real-time SAR time-series change detection, robust classification using second-order deep learning models, and novel methods for handling missing data in EEG signals. His recent articles (2023-2025) explore reproducibility frameworks, GPR-based object classification, and Riemannian geometry applications. No awards listed, but maintains active collaborations through LISTIC and industry partnerships. Advises students via internship programs (e.g., Federated ML energy cost analysis). Lab work involves developing open-source tools like Qanat for experiment management and reproducibility.

• Security of IT infrastructures
• Security of Android applications
• Security of C embedded software
• +۶ مورد دیگر

Pr. Jean-François Lalande is a Professor at CentraleSupélec, affiliated with Inria's PIRAT and CIDRE teams. His research focuses on the security of IT infrastructures, Android applications, and C embedded software, including access control policies, intrusion detection tools, and software code analysis. He has led projects like PEPR DefMal (2022–2028) and ANR LYRICS (2011–2015) for privacy-preserving cryptographic protocols in NFC systems. Roles: Conference Chair (EICC 2025, EICC 2020), Workshop Organizer (IWSMR, 3SL, COLSEC, SHPS). Editorial: Guest Editor for journals like Information Technology and Future Generation Computer Systems . His work spans malware analysis, privacy in mobile systems, and security validation. He has served on technical program committees for international conferences (IEEE, ACM) and national events like SSTIC. His students include PhD graduates such as Romain Brisse and Tomas Miranda Concepcion.

Jérémie DEQUIDTمشاهده پروفایل

پژوهشگر

• Soft Robotics
• Robotics Simulation
• Control Systems
• +۳ مورد دیگر

Jérémie DEQUIDT is a Researcher at Inria affiliated with Polytech Lille, University of Lille, serving as Thematic Group Facilitator for CO2 (Control and Scientific Computing) and Member of the Scientific Council. His offices are located at Inria Building A (Haute Borne) and Polytech Lille's Scientific City. His research spans soft robotics, simulation, control systems, and medical robotics. He develops modeling and simulation techniques for sensorimotor perception in soft robots and medical applications like prostate biopsy/brachytherapy using bio-inspired phantoms. His work integrates computer graphics, haptics, and deformable object robotics to advance medical interventions and robotic control systems. He supervises PhD students including Thomas Moupfouma (Sensorimotor perception in soft robotics through modeling and simulation) and Sizhe Tian (Simulation of needle insertion in bio-inspired active prostate phantom). As a core member of the DEFROST research team within CRIStAL laboratory (University of Lille/CNRS/Centrale Lille joint unit), he contributes to foundational research on deformable objects and robotics, with leadership roles in scientific governance and thematic coordination.

Hanan Sametمشاهده پروفایل

استاد

• Spatial Data Structures
• Geographic Information Systems (GIS)
• Computer Vision
• +۳ مورد دیگر

Hanan Samet is a Distinguished University Professor in the Computer Science Department at the University of Maryland, College Park. He holds affiliations with the Center for Automation Research and the Institute for Advanced Computer Studies (UMIACS). His academic journey includes a PhD from Stanford University (1975) in Computer Science, following degrees in Engineering (UCLA) and Operations Research/Computer Science (Stanford). Affiliations: University of Maryland, College Park (since 1975) Roles: Professor, Founding Editor-in-Chief of ACM Transactions on Spatial Algorithms and Systems, Founder of ACM SIGSPATIAL Samet's research focuses on spatial data structures, spatial databases, GIS, computer vision, and information retrieval. His seminal work includes the Foundations of Multidimensional and Metric Data Structures , an award-winning book addressing spatial indexing and query optimization. He pioneered frameworks like NewsStand for map-based news exploration and Coronaviz for pandemic visualization. Key contributions span spatial synonyms for approximate search, SAND spatial browser for digital government, and trajectory analysis systems for aviation safety and urban mobility. His work bridges theory and practice, influencing databases, graphics, and geographic systems. Education: B.S. Engineering, UCLA M.S. Operations Research, Stanford M.S./Ph.D. Computer Science, Stanford Samet has advised numerous students and led NSF-funded projects on spatio-textual data, similarity search, and spreadsheet analysis. His honors include the ACM Paris Kanellakis Award (2011), IEEE Wallace McDowell Award (2014), and UCGIS Research Award (2009). His labs and teams focus on spatial algorithms, visualization, and GIS applications. Notable projects include VASCO (spatial index demo), MARCO (image databases), and CHOLERA (disease tracking).

Xavier Décoretمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Real-time Rendering
• Visibility
• +۳ مورد دیگر

Xavier Décoret is a researcher at INRIA since October 2003, specializing in computer graphics with core expertise in real-time rendering, visibility algorithms, and level-of-detail techniques. He teaches courses at Grenoble (Master IVR program) and École Polytechnique, including Java programming, virtual image creation, and advanced image synthesis. His educational background includes: PhD in Computer Graphics under François Sillion Post-doctoral position at MIT under Frédo Durand Décoret's research spans non-photorealistic rendering, shadow computation, and GPU-accelerated algorithms. He develops practical graphics tools including XdkWRL (VRML parsing), Argstream (command-line arguments), and XdkBibTeX (BibTeX handling), emphasizing real-world implementation for interactive systems. His 2008 publications reveal trends toward efficient interactive rendering techniques, with contributions in dynamic stylization, label placement, soft shadows, and GPU voxelization—highlighting innovations in plausible visual effects for real-time applications. He is affiliated with the Artis research team at INRIA, focusing on virtual reality and computer graphics advancements.

Thomas Cokelaerمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Data Analysis
• Machine Learning
• +۱۲ مورد دیگر

Thomas Cokelaer is a researcher at the Institut Pasteur, affiliated with the Bioinformatics and Biostatistics HUB and based at the Platform Biomics. Since October 2018, he has led the SALSA (dry-lab) group, focusing on bioinformatics software development and data analysis. His interdisciplinary background spans astronomy, plant modeling, systems biology, and drug discovery. Institution: Institut Pasteur Unit: Bioinformatics and Biostatistics HUB Platform: Platform Biomics Group: SALSA (dry-lab group) leader His research interests center on bioinformatics, next-generation sequencing (NGS) analysis, machine learning, data visualization, and reproducible research. He develops and maintains several widely used open-source tools including Sequana (NGS pipelines), BioServices (biological web services), Bioconvert (format conversion), Sequanix (GUI for Snakemake), and GDSCTools (pharmacogenomics). He is deeply committed to promoting reproducibility in computational research through initiatives like reprohackathons. The recent publications reflect a strong trend in computational biology, with a focus on developing bioinformatics methodologies, improving data analysis workflows, and applying these tools to diverse biological problems such as fungal pathogenesis, cancer pharmacogenomics, mitochondrial metabolism, and viral evolution. The work spans genomics, transcriptomics, systems biology, and software engineering. He has contributed to numerous scientific software projects as Principal Investigator, including Sequana, Bioconvert, GDSCTools, DREAMTools, and Sequanix. He has also taught courses in scientific programming with Python and reproducible data analysis at the Institut Pasteur. His collaborative network includes researchers from EMBL-EBI, Sanger Institute, INRIA, and international partners within the Pasteur Network. The SALSA group hosts master’s students and visiting researchers annually.

• Virtual Reality
• Human-Computer Interaction
• Collaborative Virtual Environments
• +۴ مورد دیگر

Thierry Duval is a Professor in the Department of Computer Science (INFO) at IMT Atlantique , Brest campus. His work focuses on Virtual Reality (VR) , Human-Computer Interaction (HCI) , and 3D Interaction in collaborative environments.

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.

Simon Lacoste-Julienمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Deep Learning
• +۵ مورد دیگر

Simon Lacoste-Julien is an Associate Professor at Université de Montréal, affiliated with the Department of Computer Science and Operations Research (DIRO). He also serves as the Associate Scientific Director of Mila – Quebec Institute of Artificial Intelligence and holds the position of Vice President Lab Director at Samsung SAIT AI Lab Montreal (SAIL). His research focuses on machine learning, optimization, and their applications in areas like deep learning, generative models, causality, and computer vision. Lacoste-Julien has held academic positions at INRIA in Paris and has a PhD from UC Berkeley, with postdoctoral work at the University of Cambridge. He teaches advanced graduate courses on probabilistic graphical models and structured prediction. His work includes contributions to optimization algorithms (e.g., Frank-Wolfe methods), causal discovery, and generative models. Lacoste-Julien has supervised numerous students and postdocs, and his awards include being a CIFAR Fellow and Canada CIFAR AI Chair. His research spans theoretical foundations and practical applications, with a strong emphasis on scalable and efficient machine learning techniques.

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.

Romain Raveauxمشاهده پروفایل

دانشیار

• Image Analysis
• Machine Learning
• Structural Pattern Recognition
• +۵ مورد دیگر

Romain Raveaux is an Associate Professor at the LIFAT Computer Science Laboratory, University of Tours, affiliated with Polytech Tours. His research focuses on Image Analysis, Machine Learning, Structural Pattern Recognition, Graph Matching, Graph Neural Networks, Discrete Optimization, Reinforcement Learning, and Transfer Learning . Email: romain.raveaux@gmail.com , romain.raveaux@laposte.net Address: 64 av. Jean Portalis, Tours, France, 37200 Phone: +33 (0)2 47 36 14 27 Research Interests Graph Matching and Neural Networks Discrete Optimization for Pattern Recognition Transfer Learning in Graph-Based Models Historical Document Analysis Scientific Trends His recent work bridges Graph Neural Networks with Mixed-Integer Programming , focusing on Image Semantic Segmentation and Graph Cycle Detection . Earlier studies emphasize Genetic Algorithms for graph classification and Graph Edit Distance optimization in pattern recognition.