Find Academic Supervisors

• Signal and Image Processing
• Statistical Machine Learning
• Low-Rank Approximations
• +15 more

Matthieu Puigt is a Professor at Université du Littoral Côte d'Opale, specializing in signal and image processing with a focus on statistical machine learning , low-rank approximations , and sparse component analysis . His research extends to hyperspectral data fusion, unmixing, and restoration, as well as applications in chemistry and computational imaging . He leads the SPECIFI research team. Research Themes : Blind source separation, compressive learning, sensor calibration, and big data analysis Applications : Audio signal processing, environmental monitoring via drones, and urban air quality assessment His recent work includes developing VAE-based hyperspectral image emulators, tensor decomposition methods for multisensor data, and frameworks for butterfly species recognition. He advised Valentin Mullet's 2022 thesis on blockchain traceability systems and actively contributes to IEEE and GRETSI conferences.

Olga KloppView profile

Professor

• Nonparametric Estimation
• High-Dimensional Inference
• Network Models
• +4 more

Olga Klopp is a Professor of Statistics at ESSEC Business School and a permanent member of the CREST research center. Her work focuses on nonparametric estimation, high-dimensional inference, network models, and matrix completion. Research spans theoretical and applied statistics Specializes in sparse/high-dimensional data and network analysis Develops algorithms for matrix completion and graphon estimation Recent research includes tensor decomposition for economic networks, detection of change-points in dynamic networks, and handling missing data in epidemic modeling. She has received ERC and ANR grants for innovative projects. PhD Students: Solenne Gaucher, Mokhtar Alaya, Guillermo Martin Key methodological contributions in low-rank modeling and robust estimation

Caroline FossatiView profile

Professor

• Signal Processing
• Hyperspectral Imaging
• Medical Imaging
• +2 more

Caroline Fossati is a Full Professor at Ecole Centrale de Marseille and a member of the Institut Fresnel - GSM Signal Processing lab. Her work bridges signal processing, hyperspectral imaging, and medical diagnostics, with a focus on Alzheimer's disease detection using PET imaging and tensor-based noise reduction techniques. Current Affiliations: Ecole Centrale de Marseille, Institut Fresnel Past Affiliations: Université Paul Cézanne (1996-1997) Her research spans: Medical Imaging: Developing advanced algorithms for Alzheimer's diagnosis through 18F-FDG PET scans and multi-level feature extraction. Hyperspectral Processing: Innovating tensor-based methods for noise reduction, dimensionality reduction, and target detection in complex imaging environments. Optical Engineering: Investigating CMOS sensor design, photolithography simulations, and defect characterization in optical materials. Publications demonstrate expertise in multidimensional signal processing, array modeling, and biomedical applications. While no specific awards are documented in the provided text, her extensive co-authorship network and technical contributions across disciplines underscore her academic impact.

Cesar Federico CaiafaView profile

Adjunct Professor

• Tensor Factorizations
• Parsimonious Representations
• Machine Learning
• +5 more

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.

Rob H. BisselingView profile

Professor

• Parallel Algorithms
• Sparse Matrix/Tensor Computations
• Graph and Hypergraph Algorithms
• +5 more

Rob H. Bisseling is a Full Professor in Scientific Computing at Utrecht University's Mathematical Institute and a visiting professor at ENS de Lyon's LIP laboratory (March–May 2024). He holds a BSc/MSc in Mathematics (cum laude) from the Catholic University of Nijmegen and a PhD in Theoretical Chemistry from the Hebrew University of Jerusalem. His research focuses on parallel algorithms, sparse matrix/tensor computations, and hypergraph partitioning, with applications in high-performance computing and numerical methods. He has authored a seminal textbook on parallel scientific computing and contributes to pedagogical resources like video lectures. During his visit to LIP, Bisseling collaborates with the ROMA team under Bora Uçar to advance parallel algorithms for large-scale irregular applications. His work includes developing tools like PMondriaan for sparse matrix partitioning and promoting knowledge transfer through lectures on BSP programming. He engages with researchers, PhD students, and engineers at LIP, extending collaborations to Lyon's Institut Camille Jordan and LabPhys for tomographic reconstruction and statistical physics modeling. His academic career includes roles at Royal Dutch Shell and as Utrecht University's Director of Education (2012–2015). He advocates interdisciplinary approaches, bridging computational methods with applied sciences and engineering challenges.