جستجوی استاد راهنمای دانشگاهی

Gabriel Peyré is a CNRS Research Professor at the Department of Mathematics and Applications (DMA) of École normale supérieure (ENS) in Paris, France. A specialist in data science and artificial intelligence, he is renowned for his work on optimal transportation theory and its applications to imaging, machine learning, and neural network training. His research bridges mathematical theory with computational algorithm design, earning him the CNRS Silver Medal (2021) and multiple European Research Council (ERC) grants, including the 2024 Advanced Grant. Research interests include: Optimal Transport Machine Learning AI Theory Image Processing Computational Mathematics Neural Network Training His recent publications focus on advancing optimal transport methods in AI, with applications in neural network learning, spatial transcriptomics, and unsupervised data analysis. He has developed algorithms for large-scale optimal transport computations and contributed to theoretical understanding of transformer models and residual networks. Scientific awards: CNRS Silver Medal (2021) ERC Advanced Grant (2024) ERC Consolidator Grant (2016) ERC Starting Grant (2011) Blaise-Pascal Prize from the Academy of Sciences (2017) He supervises PhD students and postdoctoral researchers, including Raphaël Barboni, Valérie Castin, and Geert-Jan Huizing. His work involves collaborations with institutions like INRIA, MIT, and Heriot-Watt University. Peyré's affiliations include the Center for Data Sciences at ENS, where he contributes to interdisciplinary projects in biology and physics.

• Optimal Transport
• Machine Learning
• Data Science
• +۴ مورد دیگر

Micheal Davies is a Professor of Signal and Image Processing at the University of Edinburgh and a Visiting Professor at the Laboratoire de Physique (LPENSL) at ENS Lyon (2023-2024). His research focuses on sparse representations, compressed sensing, machine learning, and inverse problems. He holds a First-Class Honours degree in Engineering from the University of Cambridge (1989) and a PhD in Nonlinear Dynamics from University College London (1993). His collaboration with the SiSyPh team at ENS Lyon centers on self-supervised deep learning methods for scientific imaging. Key contributions include advancing theoretical frameworks for inverse problems and algorithmic strategies in imaging. He delivered talks at the MLSP seminar and lectures for master's students in physics and computer science. Awards: Fellow of the Royal Academy of Engineering (2017), Fellow of the Royal Society of Edinburgh (2018). Research Impact: Pioneered work in compressed sensing for MRI and sparse signal processing. His visit strengthens interdisciplinary research at ENS Lyon, bridging physics, AI, and signal processing. Current projects with J. Tachella aim to eliminate ground-truth data reliance in scientific imaging.

• Signal and Image Processing
• Sparse Representations
• Compressed Sensing
• +۳ مورد دیگر

Cesar Federico Caiafa holds an Adjunct Professor position in the Engineering Department at the University of Buenos Aires (FIUBA) and serves as an independent researcher at IAR (Instituto Argentino de Radioastronomía) and CONICET (National Council for Scientific and Technical Research). He completed his Electronics Engineering degree in 1996 and earned a PhD in Engineering from the University of Buenos Aires in 2007. His research focuses on tensor factorizations and parsimonious representations applied to astronomy, biomedicine, neuroscience, and computational imaging. As a visiting professor at Laboratoire de Physique (LPENSL) from January to February 2025, he collaborates on developing novel machine learning methods for microwave tomography, including unrolled neural networks and self-supervised learning. His visit strengthens Franco-Argentine ties in computational imaging and aims to establish an international research network (IRN-CNRS). Key collaborations include work with Nelly Pustelnik (unrolled networks), Pierre Borgnat (neuroscience signal processing), and Julien Tachella (SiSyPh team). He will also deliver specialized courses on tensorial methods in machine learning and signal processing at ENS Lyon and Inria. His research spans interdisciplinary fields such as brain connectomics, medical imaging analysis, and optimization algorithms for high-dimensional data. His contributions bridge theoretical advancements with practical applications in healthcare and astronomy.

• Tensor Factorizations
• Parsimonious Representations
• Machine Learning
• +۵ مورد دیگر

Valentin Emiya is an Associate Professor in Computer Science at Aix-Marseille University (AMU), affiliated with the QARMA team at CNRS LIS (Laboratory of Informatics and Systems). His research focuses on Artificial Intelligence, Machine Learning, and Signal Processing, with a particular interest in bridging AI and physical sciences. He is actively involved in: Estimating the environmental impact of AI with Constance Douwes Learning fast transforms and exploring sparse supports in AI Popularizing science through the annual Treize Minutes Marseille event since 2013 Coordinating computer science training in the Licence MPCI program His work emphasizes interdisciplinary collaboration, particularly between AI and physical domains, and he contributes to academic outreach and education initiatives.

• Artificial Intelligence
• Machine Learning
• Signal Processing
• +۱ مورد دیگر

Jean-François Giovannelli is a Professor at IMS Bordeaux , affiliated with the Université de Bordeaux . His work focuses on Signal and Image Processing within the SPECTRAL team. Collaborations span institutions like CEA , CNRS , and industry partners ( STMicroelectronics , Stellantis ), emphasizing Bayesian methods and MCMC algorithms for diverse applications. Current Affiliation : Professor, IMS Bordeaux, Université de Bordeaux Research Group : Signal and Image Processing Team : SPECTRAL Collaborations : CEA, CNRS, CESTA, Thales, NXP Research Interests include: Bayesian inference for inverse problems MCMC sampling techniques Signal/image reconstruction Mass spectrometry data analysis Adaptive optics in astronomy Medical imaging algorithms Article Trends show a focus on Bayesian modeling across disciplines: biomedical data, astronomical imaging, and microwave mapping. Key methods include MCMC samplers , regularized inversion , and statistical validation . Professional Contributions involve developing tools like NiftyRec for tomography and advancing Adaptive Optics restoration algorithms. His work bridges theoretical statistics and practical applications in healthcare, space, and defense.

• Signal and Image Processing
• Bayesian Statistics
• Markov Chain Monte Carlo (MCMC) Methods
• +۴ مورد دیگر

Cédric HERZET is a Permanent Member of CREST at INRIA Rennes, focusing on statistical learning theory, optimization, and inverse problems. His work bridges theoretical analysis with practical algorithm development. Primary affiliation: INRIA Rennes (French National Institute for Research in Digital Science) Research Interests: Statistical Learning Theory, Optimization algorithms, Operational Research, Inverse Problems. His work particularly addresses sparse approximation, compressed sensing, and iterative thresholding methods. Key Contributions: Development of Bayesian pursuit algorithms, geometric analysis of subspace clustering with outliers, and analysis of state evolution in dense graph message passing. His theoretical work has direct applications in signal/image processing and machine learning. Software: Created MATLAB implementations of sparse approximation algorithms and Bernoulli-Gaussian Lab toolbox for Bayesian pursuit simulation.

• Statistical Learning Theory
• Optimization
• Operational Research
• +۳ مورد دیگر

Céline Meillier is an Associate Professor at the University of Strasbourg, affiliated with ICube research laboratory in Strasbourg, France. She serves as co-president of the GRETSI 2025 conference alongside Vincent Mazet, demonstrating her leadership position within the signal and image processing community. Her research focuses on modeling and analysis of spatially structured signals, with applications in astronomy hyperspectral data, functional MRI, and brain connectivity analysis. Methodologically, she specializes in sparse decomposition problems integrating expert knowledge into regularization constraints and multiple test thresholding procedures for low signal-to-noise event detection. ANR JCJC DynaSTI Project (2023-2026): Modeling Spatial-Temporal Dynamics of Functional Connectivity in resting fMRI DYNAMIC project funded by GdR ISIS (2019-2021) Dr. Meillier teaches multiple courses including Image Processing and Registration, Data Processing, Statistics, and Random Signal Processing for the FIP TI Santé program. Her research has practical applications in functional MRI for Alzheimer's, depression, optogenetics in small animals, and schizophrenia in humans. She has supervised three doctoral students: Argheesh Bhanot (2017-2021), Emma Menant (2023-present), and Massyl Moudoud (2023-present), as well as one post-doctoral fellow Duong Hung Pham (2018-2019).

• Signal Processing
• Image Processing
• Statistical Signal Processing
• +۵ مورد دیگر

David Coeurjolly is a Research Director at the French National Center for Scientific Research (CNRS) affiliated with LIRIS laboratory at Claude Bernard University Lyon 1. He leads the Origami research team and holds several leadership positions including Director of GdR Informatique Géométrique et Graphique and Co-lead of PEPR ICCARE. As co-founder of the DGtal library and General Chair of the Graphics Replicability Stamp Initiative, he significantly influences computational geometry research. His research spans digital geometry, discrete algorithms, Monte Carlo rendering, and geometry processing. Core innovations include work on optimal transport, low-discrepancy sampling, digital surface regularization, and curvature estimation methods. His approaches combine theoretical mathematics with practical implementations for computer graphics and scientific computing applications. Recent publications demonstrate strong focus on: Efficient transport algorithms (BSP-OT, Rectified Flows) Sampling theory innovations (Sobol' sequences, Owen scrambling) Digital geometry foundations (Gauss digitization, Laplace-Beltrami operators) Geometric transformations (bijective rotations, plane probing) Awards include: 🎫 Best Paper Award, SIGGRAPH Asia 2024 🎫 SGP Software Award 2016 He leads multiple ANR grants including SSLAM (point cloud ML), StableProxies (geometry processing), and MoCaMed (medical physics). His lab develops open-source tools like DGtal and maintains active collaborations through international initiatives like the Graphics Replicability Stamp.

• Digital Geometry
• Geometry Processing
• Point Sampling
• +۴ مورد دیگر

Stephanie Bidon is a Professor in the Department of Electronics, Optronics and Signal Processing (DEOS) at ISAE-SUPAERO, University of Toulouse, specializing in statistical signal processing for radar and navigation systems. Her career spans over 15 years of research leadership with significant contributions to adaptive radar techniques and GNSS signal processing. Her educational background includes: Engineer degree from ENSICA, Toulouse (2004) MS degree from ENSICA, Toulouse (2005) PhD from INP Toulouse (2008, with Léopold Escande and Thales prizes) Habilitation à Diriger des Recherches from INP Toulouse (2015) Professor Bidon's research centers on statistical signal processing for extracting signals from noise in radar and navigation contexts. She develops Bayesian estimation frameworks and sparse representation techniques applied to critical challenges including Space-Time Adaptive Processing for detecting moving targets, phase tracking in degraded GNSS environments, and waveform co-design for radar-communications convergence. Her work bridges theoretical advances with practical implementations in aerospace systems. Her scientific recognition includes: Léopold Escande prize (2008) Thales PhD prize (2009) She actively mentors the next generation of engineers through supervision of eight PhD students on cutting-edge topics from GNSS phase tracking to radar-communications integration. As an Associate Editor for IEEE Transactions on Radar Systems (2022-2025) and former Vice Chair of the IEEE AESS Radar Systems Panel, she shapes global research directions while securing collaborative funding through EU and industry partnerships. Her Communication and Information Theory research group provides experimental SDR radar data resources and focuses on robust adaptive antenna systems for aerospace applications. The group maintains strong industry ties with Thales and other defense/aerospace leaders, facilitating technology transfer of advanced signal processing techniques to operational radar and navigation systems.

• Statistical Signal Processing
• Radar Signal Processing
• GNSS Signal Processing
• +۵ مورد دیگر