جستجوی استاد راهنمای دانشگاهی

Sihem Mesnagerمشاهده پروفایل

استاد

• Symmetric Cryptography
• Coding Theory
• Finite Fields
• +۱۰ مورد دیگر

Sihem Mesnager is a University Professor of Mathematics at the University of Paris VIII, affiliated with the Laboratory of Analysis, Geometry and Applications (LAGA) at Paris XIII (CNRS) and the AGC3 research group (Algebra, Geometry, Combinatorics) . She holds an adjunct professorship at Télécom Paris within the MIC2 Mathematics team of the Computer Science and Networks department (INFRES). Her work bridges pure mathematics and applied cryptography, focusing on Boolean functions, bent functions, and coding theory for secure communication and data protection. PhD in Mathematics, University of Pierre and Marie Curie (Paris VI), Sorbonne University (2002) Habilitation (HDR) in Mathematics, University of Paris VIII (2012) Research Interests Dr. Mesnager specializes in symmetric cryptography and coding theory , particularly their applications to secure digital communication, error correction, and post-quantum cryptographic protocols. Her algebraic approach employs finite fields, exponential sums, algebraic geometry, and finite geometry to analyze and construct cryptographic primitives like S-boxes, APN functions, and optimal linear codes. She also investigates algorithmic aspects of computer algebra in these domains. Scientific Awards George Boole International Prize (2020) PEDR Excellence Scientific Award (2019-2022) PEDR Excellence Scientific Award (2014-2017) Publications & Projects Her recent work includes constructing weightwise perfectly balanced Boolean functions for the FLIP cipher, optimizing Inner Product Masking schemes via coding theory, and developing post-quantum secure functional encryption using multivariate cryptography. She has contributed to Reed-Muller codes, BCH codes, and Gaussian sum-based linear codes with one-dimensional hulls, emphasizing their applications in side-channel attack resistance and quantum error correction.

Frédéric PASCALمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Adaptive Radar Detection
• +۵ مورد دیگر

Frédéric Pascal is a Full Professor at CentraleSupélec, part of the University of Paris-Saclay, and a member of the Laboratoire des Signaux et Systèmes (L2S). His research focuses on statistical signal processing, machine learning, and robust estimation techniques, with applications in radar detection, covariance matrix estimation, and information geometry. He has held roles including Coordinator of AI activities at CentraleSupélec and Head of the "Signals and Statistics" group at L2S. His academic journey includes a PhD from University Paris X – Nanterre (2006) and an HDR (2012) from University Paris-Sud. His work emphasizes adaptive signal processing in non-Gaussian environments, with contributions to robust covariance estimation, M-estimators, and applications in radar systems and biomedical signal processing. He has authored over 100 journal/conference papers and serves as an Associate Editor for IEEE Transactions on Signal Processing and Elsevier Signal Processing. Current research interests include AI transparency, data-driven methods for industry 4.0, and health-related signal analysis.

Maxime Ferreira Da Costaمشاهده پروفایل

دانشیار

• Signal Processing
• Data Science
• Inverse Problems
• +۶ مورد دیگر

Maxime Ferreira Da Costa is an Associate Professor at CentraleSupélec, Université Paris-Saclay, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on theoretical and algorithmic foundations of data science, particularly in inverse problems, structured signal processing, and physical layer security. Key research areas include super-resolution, system calibration, and privacy-enhancing communication schemes. He holds a Ph.D. from Imperial College London (2018), and previously worked at USC and Carnegie Mellon University. Notable achievements include an ANR Young Researcher Grant (2023) for projects on data science and physical layer security. He actively contributes to conferences and workshops, with recent presentations at Institut Henri Poincaré and Université Paris-Saclay. Education: Ph.D. in Electrical Engineering, Imperial College London (2018) M.Sc. Electrical Engineering, Imperial College London (2012) Engineer Diploma, CentraleSupélec (2012) Research interests span continuous inverse problems, off-the-grid methods, wireless security, and sensing systems. Current projects explore goal-oriented resource allocation, fake path injection for privacy, and preconditioned optimization techniques. His work bridges signal processing theory with practical applications in imaging, telecommunications, and sensing.

Alain Trouvéمشاهده پروفایل

استاد

• Shape Spaces and Applications
• Imaging Processing and Computational Vision
• Computational Biology
• +۳ مورد دیگر

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.

Zeina ELRAWASHDEHمشاهده پروفایل

پژوهشگر

• Measurements and Controls
• Fiber-Optic Sensors
• Artificial Intelligence of Things (AIoT)
• +۳ مورد دیگر

Zeina ELRAWASHDEH is a Researcher Lecturer at the Institut Catholique d'Arts et Métiers (ICAM), based at the Grand Paris Sud campus. Her research focuses on Measurements and Controls, with a particular emphasis on fiber-optic sensors, multi-agent systems, and IoT integration for smart infrastructure. She collaborates with prestigious research laboratories globally to develop innovative solutions in energy optimization, smart cities, and precision engineering. Her expertise spans applied research in fiber-optic displacement sensors, algorithm optimization for sensor performance, and user-centric building automation systems. Zeina’s work bridges theoretical advancements with practical applications in manufacturing, energy, and urban systems. She actively contributes to international academic discourse through peer-reviewed publications and participates in ICAM’s strong industry partnerships for applied research outcomes. Zeina’s research portfolio demonstrates a trajectory toward integrating AI and IoT with traditional engineering challenges, addressing technical gaps in sensor networks, multi-agent coordination, and precision machining. Her recent work highlights advancements in smart city infrastructure and energy-efficient building systems. While no awards are explicitly mentioned, her involvement in ICAM’s research initiatives underscores her commitment to impactful, industry-relevant science. Collaborations with global companies and academic institutions position her at the forefront of applied engineering research.

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.

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).

Irène Marcoviciمشاهده پروفایل

استاد

• Probability Theory
• Dynamical Systems
• Cellular Automata
• +۴ مورد دیگر

Irène Marcovici is a Professor in the Department of Mathematics at the University of Rouen Normandy, affiliated with the Raphaël Salem Mathematics Laboratory (LMRS). She leads the Probability and Dynamical Systems team within LMRS and participates in the ALEA and SDA2 working groups of the GDR Informatique Fondamentale et ses Mathématiques. PhD in Mathematics (2013, University of Paris Diderot) Habilitation à Diriger des Recherches (2021, University of Lorraine) Her research focuses on probability theory , dynamical systems , and cellular automata , with significant contributions to percolation theory, self-organization phenomena, and combinatorics on words. She investigates how randomness influences complex systems, particularly through probabilistic cellular automata and their ergodic properties. Analysis of her recent publications (2021-2025) reveals a strong emphasis on percolation dynamics (e.g., corner percolation with directional bias), self-stabilization mechanisms in cellular systems, and combinatorial structures like Kolakoski sequences. Her work bridges theoretical computer science and pure mathematics, often employing stochastic methods to solve problems in symbolic dynamics and discrete geometry. Supervision & Collaborations: Currently supervising Maxence Poutrel (with Jérôme Casse) Previously supervised Pierrick Siest (2021-2024), Jocelyn Begeot (now Associate Professor), and Pierre-Adrien Tahay (now PRAG at Telecom Nancy) Regular collaborations with Régine Marchand, Nazim Fatès, and Pascal Moyal Laboratory Context: As leader of the Probability and Dynamical Systems team at LMRS, she contributes to France's national research infrastructure in fundamental mathematics, with connections to CNRS and international working groups focused on automata theory and discrete probability.

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.

José PICHERALمشاهده پروفایل

استاد

• Signal Processing
• Acoustic Imaging
• Array Processing
• +۵ مورد دیگر

José Picheral is a Professor at CentraleSupélec, affiliated with the Laboratory of Signals and Systems (L2S). He holds a PhD (2003) and HDR (2017) in high-resolution signal processing methods and inverse problems. His research focuses on array processing, source localization, acoustic imaging, and vibration analysis, with applications in aeroacoustics, automotive systems, and industrial monitoring. He has supervised multiple PhD students and contributed to projects like Valeo’s smartphone-based car key replacement system. Education: Engineering Degree: Supélec (1999) and Politecnico di Milano (1999, Erasmus-TIME) PhD: Paris Sud University (2003) Habilitation (HDR): Université Paris Sud (2017) Research Interests: High-resolution methods for distributed sources, sparse signal processing, acoustic imaging, asynchronous measurements, and sensor array design. Current projects include spatial source covariance estimation, EEG spectrum analysis, and automotive applications using smartphone localization. Key Contributions: Over 50 publications in top journals/conferences (e.g., IEEE Transactions, ICASSP). Notable work on MUSIC algorithm robustness, DAMAS optimization, and sparse approaches for tip-timing signals. Advising & Collaboration: Supervised 7 PhD students. Collaborations with SAFRAN, Valeo, and academic teams in Bayesian inference and inverse problems. Active in L2S’s Inverse Problems Group and SYCOMORE team. Labs/Teams: Member of L2S’s Signal Processing and Statistics group, leading research in systems and control, telecommunications, and energy systems.

• Atmospheric Dispersion
• Turbulent Flows
• Urban Environmental Engineering
• +۳ مورد دیگر

Lionel Soulhac is a Professor at INSA Lyon and serves as the Deputy Director of the Laboratoire de Mécanique des Fluides et d’Acoustique (LMFA) . He leads the AIR team (Atmospheric and Industrial Research) and is affiliated with the École Centrale de Lyon . His academic focus lies in the Department of Fluid Mechanics, Acoustics, and Energy (MFAE). Dr. Soulhac’s research emphasizes urban atmospheric dispersion , turbulent flows in complex environments , and numerical modeling of pollutant transport . He has contributed to developing operational models like SIRANE and BUILD , which address air quality and emergency response scenarios in urban and industrial settings. Teaching responsibilities include courses on fluid mechanics, environmental engineering, and pollution dynamics at École Centrale de Lyon and the University of Lyon. Research highlights include studies on concentration fluctuations , vegetation effects in urban canopies , and inverse modeling for source characterization . His work integrates experimental wind tunnel studies and computational fluid dynamics (CFD) to assess dispersion patterns and urban microclimates. Recent projects, such as the DIPLOS initiative, focus on pollutant dispersion in street networks and risk assessment methodologies. His contributions bridge academic research with practical applications in environmental safety and urban planning.

• Wave Propagation
• Computational Mathematics
• Numerical Analysis
• +۶ مورد دیگر

Vanessa Mattesi is a Researcher affiliated with the Inria Team MAGIQUE-3D and CERFACS . She holds a Ph.D. in Applied Mathematics from the University of Pau and the Adour Region (2011-2014), advised by Sébastien Tordeux. Her postdoctoral research focuses on multiscale modeling of wave propagation in complex media, with emphasis on numerical methods like Trefftz discontinuous Galerkin and boundary element approaches. Education: Ph.D. in Applied Mathematics (2011-2014), University of Pau M.Sc. in Mathematical Engineering (2011), University of Toulouse III Research Interests: Her work centers on wave propagation phenomena, particularly in heterogeneous media. Key areas include: High-order numerical methods for Helmholtz and acoustic equations Equivalent source modeling of small heterogeneities Domain decomposition and absorbing boundary conditions Her methods integrate discontinuous Galerkin formulations with boundary element techniques for enhanced accuracy in complex geometries. Grants & Activities: Member of Inria Bordeaux-Sud Ouest committee. Active contributor to conferences such as ACOMEN (2017), EAGE (2016), and Waves (2013). Labs/Teams: MAGIQUE-3D project team (Inria) and CERFACS collaborative research groups.

Paul Thevenonمشاهده پروفایل

استادیار

• GNSS Signal Processing
• Navigation Systems
• Satellite Communication
• +۳ مورد دیگر

Dr. Paul Thevenon is an Assistant Professor at ENAC (National Civil Aviation School) in Toulouse, France, since July 2013. He holds a Habilitation à Diriger les Recherches (2023) and focuses on GNSS signal processing, integrity monitoring, and hybrid sensor systems. His research integrates advanced algorithms for cycle slip detection, multipath mitigation, and hybrid navigation filters using 5G/ LTE signals. Education: Bachelor's in Electronic Engineering from École Centrale de Lille (2004) Master's in Space Telecommunications from ISAE (2007) PhD in Signal Processing from ENAC (2010) Research Themes: GNSS robustness against malicious signals (e.g., evil waveforms) Multi-receiver systems for RTK positioning enhancement Integration of non-GNSS signals (5G, LTE) for hybrid navigation Factor graph approaches for carrier phase processing Awards: 2023: Habilitation à Diriger les Recherches Technical Contributions: Developed CNN-based multipath detection systems Advanced RTK ambiguity resolution via multi-rover arrays Validated TDCP measurements in low-cost PPP-IMU filters Labs/Teams: Core member of the SIGNAV lab, specializing in signal processing and navigation systems.

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.