جستجوی استاد راهنمای دانشگاهی

Alain Durmus is a Professor at École Polytechnique, affiliated with the applied mathematics department (CMAP). His research focuses on computational statistics, machine learning, and stochastic methods, including Monte Carlo algorithms, Bayesian inference, and optimization. He explores topics such as Markov chain Monte Carlo (MCMC), stochastic approximation, and generative models. His work emphasizes theoretical guarantees for algorithms like Langevin Monte Carlo and Hamiltonian Monte Carlo, with applications to high-dimensional Bayesian inference and inverse problems. Key contributions include hypocoercivity analysis of piecewise deterministic MCMC processes, convergence guarantees for stochastic gradient methods, and the development of efficient sampling techniques. He has also contributed to Bayesian imaging and federated learning through works like the QLSD algorithm. Awarded the Best Student Paper Award at ICASSP 2020 for his work on the Sliced-Wasserstein distance. His teaching spans mathematical statistics, stochastic methods, and probability at École Polytechnique and ENS Paris-Saclay. He has also contributed to conferences and workshops on topics ranging from MCMC convergence to optimization in machine learning.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
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Olivier FARGES is a Senior Lecturer and HDR (Habilitation à Diriger des Recherches) holder at the University of Lorraine, affiliated with ENSGSI (École Nationale Supérieure de Géologie et Sciences Industrielles) within the Groupe INP. He serves as Director of Industrial Partnerships at ENSGSI and is part of the LEMTA Laboratory (CNRS-University of Lorraine), focusing on multiphysics and multiscale modeling of heat transfer in complex environments. His academic roles include teaching courses such as Heat and Mass Transfer, Fluid Mechanics, Scientific Computing Modeling, and Renewable Energy. Dr. FARGES holds a Ph.D. in Energy and New R&D (2014) and an Engineering degree in Energy Engineering (2010), both from the École de Mines Albi. His research emphasizes coupled conductive-radiative heat transfer in porous media, thermal property characterization of heterogeneous materials, and Monte Carlo-based computational methods for energy systems. He has contributed to advancements in photovoltaic system modeling, solar thermal power optimization, and urban climate studies. His work bridges theoretical and applied thermal engineering, with applications in sustainable energy systems, material science, and industrial partnerships. Key research themes include radiative transfer modeling, multiphysics simulation frameworks, and the development of innovative tools for thermal property measurement and energy performance assessment.

• Thermal Engineering
• Energy Systems
• Fluid Dynamics
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Philippe Moireau is a Full Professor in the Department of Applied Mathematics at École Polytechnique, where he is also affiliated with the Center for Applied Mathematics (CMAP). He serves as the head of the Inria Project-Team MΞDISIM (Mathematical and Mechanical Modeling with Data Interaction for Simulation in Medicine) and holds the distinguished position of Ingénieur Général of The Corps des Mines. His primary research focuses on inverse problems and data assimilation for partial differential equation models, with particular emphasis on: Observer-based methods from optimal control perspectives Stabilization approaches for evolution equations Numerical analysis of time-dependent control problems Digital twin applications in cardiovascular medicine Professor Moireau's publication portfolio demonstrates consistent focus on mathematical methods for physical systems, with recurring themes in: Data assimilation techniques for PDE-based models Numerical stabilization and discretization methods Cardiovascular biomechanics and hemodynamics Stochastic modeling of biological systems Epidemiological forecasting and control He leads the ANANKΞ project-team at Inria focused on Analysis And Numerics of physical-Knowledge-based Estimation. His educational contributions include lectures on data assimilation theory at CEMRACS and courses on mathematical modeling in cardiac biomechanics at Institut Polytechnique de Paris.

• Inverse Problems
• Data Assimilation
• Partial Differential Equations
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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
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Cristina Butucea is a Professor of Statistics and Machine Learning at ENSAE-CREST, Institut Polytechnique de Paris . She was nominated an IMS Fellow (2019) for her contributions to nonparametric and high-dimensional statistics. She has co-organized major conferences like Fréjus 2018 , Luminy 2019-2020 , and Oberwolfach 2021 , and serves as Associate Editor for ALEA . Fields of interest: Nonparametric statistics, quantum statistics, differential privacy, inverse problems, machine learning. Awards: IMS Fellowship, conference organization leadership. Research trends: Focuses on optimal estimation under privacy constraints, quantum state reconstruction, high-dimensional inference, and adaptive nonparametric methods. Email: Cristina.Butucea@ensea.fr | Cristina.Butucea@ip-paris.fr

• Nonparametric Statistics
• High-Dimensional Statistics
• Inverse Problems
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Olivier ALLIX is a Professor at the Laboratoire de Mécanique et Technologie (LMT) at École Normale Supérieure de Cachan (ENS-Cachan). His research focuses on computational mechanics, including multiscale modeling of composite materials, structural failure analysis, and non-intrusive coupling strategies. He has held leadership roles such as Head of LMT-Cachan and Vice-president of the International Association for Computational Mechanics (IACM). Expertise: Computational structural mechanics, material failure, inverse problems, and multiscale approaches. Editorial Roles: Associate editor of multiple journals including Computational Mechanics and Computer Methods in Applied Mechanics and Engineering . Awards: IACM Fellow, Euromech Fellow, and recipient of the Gay-Lussac Humboldt Prize (2019). His work integrates experimental mechanics with computational methods, emphasizing big data applications and model validation. He has organized major conferences like the World Congress on Computational Mechanics and co-led international research initiatives such as the IRTG ‘Virtual Material and Structures’ with Hannover University. Teaching includes advanced courses on structural dynamics, composite materials, and computational mechanics at the Master’s level. His research group collaborates with industries like Safran, IFPEN, and DGA on projects involving fatigue analysis, mooring systems, and composite testing.

• Computational Mechanics
• Structural Mechanics
• Multiscale Modeling
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Sonia Fliss is a Full Professor at ENSTA Paris , affiliated with the Department of Applied Mathematics and the POEMS laboratory (UMR CNRS-INRIA-ENSTA) . She teaches applied mathematics courses on partial differential equations (PDEs) , finite element methods , and periodic homogenization to undergraduate and graduate students. Doctor in Applied Mathematics (2009) Authorized to supervise research (2019) Research Interests : Sonia Fliss specializes in the modeling and numerical analysis of wave propagation in periodic, quasi-periodic, and random media . Her work includes transparent boundary conditions , guided waves , and asymptotic methods for acoustic, electromagnetic, and elastic wave phenomena . Recent Publications highlight her contributions to the Half-Space Matching Method , edge states in honeycomb structures , and scattering problems in unbounded domains . Her numerical techniques address multi-scale waveguides and time-harmonic propagation . Laboratory : As a member of the POEMS team, she collaborates on interdisciplinary projects involving mathematical analysis , computational physics , and engineering applications in domains like defence, energy, and transport .

• Wave Propagation
• Mathematical Analysis
• Numerical Analysis
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Frédéric JEAN is a Professor and Director of the Unité de Mathématiques Appliquées (UMA) at ENSTA Paris. His expertise lies in nonlinear control theory and geometric control, with applications in neurophysiology modeling. He holds an HDR (Habilitation à Diriger des Recherches) and advises doctoral students across multiple domains. Research Interests: Frédéric’s research focuses on geometric control theory, sub-Riemannian geometry, optimal control, and nonholonomic systems. His work bridges theoretical advancements with practical applications in neurophysiology and aerospace engineering. Recent studies include trajectory planning for planetary landings and robust control strategies under uncertainty. Teaching: He teaches courses on applied differential geometry, dynamical systems analysis, and control systems at ENSTA Paris. Publications: His recent work emphasizes sub-Riemannian geometry, stochastic control, and aerospace applications. Key trends include optimal control in constrained systems and geometric approaches to robotic and biological movement. Students: Co-supervised over 10 doctoral theses, including those by Clara Leparoux (2023), Meryem Kafnemer (2022), and Sofya Maslovskaya (2018). Labs/Teams: Active within the Optimization and Commande team at UMA, contributing to interdisciplinary research in control systems and applied mathematics.

• Nonlinear Control Theory
• Geometric Control
• Optimal Control
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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.

• Signal Processing
• Data Science
• Inverse Problems
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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.

• Shape Spaces and Applications
• Imaging Processing and Computational Vision
• Computational Biology
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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.

• Statistical Signal Processing
• Machine Learning
• Riemannian Geometry
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Claire Prada is a CNRS Research Director at the Institut Langevin , specializing in laser ultrasound , guided wave propagation , and time-reversal acoustics . Her work bridges fundamental wave physics and applied nondestructive testing. Research Pillars : Zero-group-velocity (ZGV) Lamb modes for material characterization Anisotropic wave propagation and negative refraction phenomena Time-reversal operator decomposition for structural monitoring Passive acoustic defect localization with ambient noise Technological Innovations : Fourier-domain reconstruction algorithms for 3D imaging Single-pixel photoacoustic microscopy Adaptive projection methods for rib-cage ultrasound focusing Wave Physics Discoveries : Documentation of power flux skewing in anisotropic plates Identification of beating resonance patterns in elastic media Experimental validation of negative reflection in chaotic waveguides Medical & Industrial Applications : Quantitative elastography for tissue stiffness measurement Jet engine blade damage detection Cortical bone femoral neck assessment Thin layer thickness measurement via ZGV resonance shifts

• Ultrasonics
• Guided Waves
• Time-Reversal Acoustics
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Alexandre Ern is a Senior Researcher at CERMICS (École des Ponts ParisTech) and INRIA Paris (SERENA team), where he has contributed since 1995 and 2016, respectively. He holds a professorship at École des Ponts (since 1997) and previously served as Associate Professor at École Polytechnique (2010–2022). Since 2015, he heads the Master's program in Applied Mathematics at École des Ponts. Ern is Co-Editor-in-Chief of the IMA Journal of Numerical Analysis (since 2024) and Associate Editor for multiple top journals including SIAM Journal on Scientific Computing and ESAIM Mathematical Modelling and Numerical Analysis. His research focuses on numerical methods (finite elements, discontinuous Galerkin, hybrid high-order schemes), a posteriori error estimation , and applications in fluid/solid mechanics and environmental flows (hydrology, porous media). He develops structure-preserving discretizations for complex PDEs and explores computational geosciences and wave propagation. Ern's publications emphasize robust error analysis , high-order discretizations , and computational efficiency for elliptic, parabolic, and hyperbolic systems. Recent work explores hybrid high-order methods for wave equations, Maxwell's equations, and interface problems, often leveraging polynomial-degree-robust techniques and unfitted meshes. Scientific Awards: Paul Caseau Prize (2020) Nominee, AMIES Prize (2020) ENPC Best PhD Award (supervision) UPE Best PhD Award (supervision) Frontiers of Science Award, Int. Congress Basic Science (2024) He actively advises PhD students (31+ supervised) and postdocs, with projects funded by industrial partners including CEA, EDF, and Safran. His team collaborates with CERMICS and INRIA's SERENA lab, focusing on computational mechanics, model reduction, and large-scale fracture networks.

• Numerical Analysis
• Finite Element Methods
• Computational Mechanics
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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.

• Signal Processing
• Acoustic Imaging
• Array Processing
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R Rekha is a researcher affiliated with Charles University , contributing to computational methods in numerical analysis and mechanics. She has participated in prestigious conferences like ESOP 2019 , including program committees and publications. Research Interests include stress-assisted diffusion, elasto-acoustic wave propagation, virtual element methods, and hybrid high-order schemes. Her work focuses on developing robust numerical methods for solving complex partial differential equations in engineering and physics applications. Publications (2025–2021) demonstrate expertise in computational mechanics, with a focus on virtual element methods for stress-assisted diffusion, coupled systems, and biharmonic equations. Key trends include error analysis, polygonal mesh discretization, and multiphysics modeling. Conference Contributions span program committee roles and authored papers at events like ESOP, indicating active engagement in academic communities.

• Computational Mechanics
• Numerical Analysis
• Scientific Computing
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