Find Academic Supervisors

• Computer Vision
• Image Processing
• Deep Learning
• +7 more

Namik Cho is an active researcher in computer science and engineering with significant contributions to computer vision and image processing. Their work spans multiple domains including super-resolution, HDR imaging, medical image analysis, and lossless compression techniques. Their research interests center around innovative approaches to image processing, with particular expertise in frequency domain analysis and transformer networks. Cho's work often bridges theoretical advances with practical applications, developing efficient algorithms that address real-world constraints in computational resources. Analysis of recent publications (2023-2025) reveals a strong focus on frequency-domain techniques for image enhancement, efficient network design for resource-constrained environments, and medical image analysis applications. The research demonstrates consistent innovation in addressing fundamental challenges in image processing while maintaining practical implementation considerations. Cho has established productive collaborations across multiple institutions, working with researchers like Hochang Rhee, Sunwoo Cho, and Jaehyun Park on various projects spanning computer vision, image processing, and medical applications. The research output shows steady growth with increasing impact in specialized areas of image processing.

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.

Stevenn VolantView profile

Researcher

• Biostatistics
• Bioinformatics
• Statistical experiment design
• +7 more

Stevenn Volant is a researcher at the Bioinformatics and Biostatistics Hub (C3BI) within the Center of Bioinformatics, Biostatistics and Integrative Biology at Institut Pasteur, Paris. He has been actively involved in statistical analysis and the development of R/Shiny pipelines since 2014, supporting a wide range of biological and biomedical research projects. Ph.D in Applied Mathematics and Statistics, AgroParisTech (2012) Master 2 in Mathematics, Statistics Option, Université de Rennes 1 (2008) Biostatistics Engineer, ENSAI (2008) His research focuses on biostatistics, bioinformatics, and computational biology , with particular expertise in single-cell data analysis, statistical experiment design, microbiota studies, and targeted metagenomics . He applies machine learning and advanced statistical modeling to high-throughput genomic data, contributing to studies on infectious diseases, cancer, and immunology. The recent publications highlight his work in host-pathogen interactions (SARS-CoV-2, HIV, tuberculosis), microbiome dynamics , and statistical tool development for experimental design and data analysis. His contributions span both methodological innovation and biological discovery, often through collaborative, interdisciplinary projects. Scientific Contributions: Co-developer of SHADE (SHiny Application for Designing Experiments) Contributor to SHAMAN (SHiny Application for Metagenomic ANalysis) Active participant in multiple Institut Pasteur research consortia and training programs He has played a key role in advising and teaching data analysis to PhD students and biologists, offering courses in R programming, statistics, and reproducible research. His work supports robust, data-driven scientific inquiry across diverse domains, from virology to psychiatry.

• Applied Mathematics
• Partial Differential Equations (PDEs)
• Wave Propagation
• +2 more

Sanghyeon Yu is an Associate Professor in the Department of Mathematics at Korea University, South Korea. His research focuses on applied mathematics, particularly in partial differential equations, wave propagation, metamaterials, and inverse problems. Education: PhD in Mathematics from KAIST, postdoctoral research at ETH Zurich, and a Bachelor's degree in Physics from Korea University. His work explores subwavelength resonators, plasmonics, and elastic stress concentrations through mathematical modeling and computational analysis. He has contributed to understanding wave interactions in bubbly crystals, PT-symmetric metamaterials, and stress localization in composite materials. Recent publications highlight his expertise in super-resolution imaging, homogenization theory, and topological wave phenomena. Collaborations with Prof. Habib Ammari and others dominate his research output. He teaches and conducts research at Korea University's Asan Science Building, with contact details including the email sanghyeon_yu@korea.ac.kr .

Xiaofei LIView profile

Assistant Professor

• Speech Processing
• Sound Source Localization
• Speech Enhancement
• +9 more

Xiaofei LI is an Assistant Professor at Westlake University, Hangzhou, China, a position he has held since March 2020. Prior to this, he was a post-doctoral researcher and later a starting research scientist at INRIA Grenoble Rhône-Alpes, France, from February 2014 to at least 2019. He is affiliated with the PERCEPTION team and has contributed to major projects such as the ERC VHIA and EARS projects, as well as industry collaboration with Samsung Electronics. Education Bachelor of Electronic Information, Beijing Institute of Machinery, 2007 PhD in Electronics, Peking University, 2007–2013 His research focuses on speech and audio signal processing , particularly in challenging real-world environments. Key areas include multi-microphone speech processing for sound source localization, separation, and dereverberation; single-microphone signal processing for noise estimation, voice activity detection, and speech enhancement; and audio-visual fusion for speaker diarization and tracking. He employs advanced techniques such as LSTM networks , Bayesian inference , convolutive transfer functions , and deep learning in the STFT domain. His recent publications (2017–2019) demonstrate a strong trend toward multichannel speech enhancement , online speaker localization and tracking , and audio-visual integration . His work frequently appears in top-tier IEEE journals such as IEEE/ACM Transactions on Audio, Speech and Language Processing and IEEE Transactions on Pattern Analysis and Machine Intelligence. He has developed and evaluated algorithms using real datasets like LOCATA and AVDIAR, emphasizing robustness in reverberant and multi-speaker environments. Scientific Awards and Recognition No specific awards are mentioned in the provided text. Advising and Grants Currently serves as an Assistant Professor, implying student supervision and research advising, though no specific students are named. Research has been funded by prestigious grants including the European Research Council (ERC) Advanced Grant VHIA #340113 and the EU-FP7 STREP project EARS (#609465). Labs and Research Teams PERCEPTION team, INRIA Grenoble Rhône-Alpes, France Involved in the EARS, ERC VHIA, and Samsung Electronics Lito projects Contributor to the development of the AVDIAR and AVTrack-1 datasets

Guillaume GRACIANIView profile

Lecturer

• Optics
• Biophysics
• Nano-Metrology
• +3 more

Guillaume Graciani is a Lecturer at Telecom SudParis, affiliated with the ISTeC research institute. His work focuses on advanced optical metrology, stochastic interferometry, and light scattering techniques with applications in biophysics and nanotechnology. Key research areas include ultrahigh sensitivity detection, protein dynamics analysis, and novel optical instrumentation design. His research has produced innovations such as the 3D stochastic interferometer for picometer-scale deformation detection and cavity-amplified scattering spectroscopy for studying nanoparticles and proteins. He has published extensively in prestigious journals like Communications Physics and developed methodologies for ultraweak light flux measurement using EM-CCD arrays. Graciani’s expertise spans theoretical optical systems, experimental instrumentation development, and interdisciplinary applications in health sciences. While no formal awards are listed, his prolific publication record (over 15 articles from 2012-2025) demonstrates impactful contributions to optics and biophysical sensing. No specific grant details or lab affiliations are mentioned in the provided texts.

• Wireless Communications
• Coded Caching
• Satellite Communications
• +5 more

ZHAO Hui serves as a post-doctoral Research Fellow at EURECOM's Communication Systems Department, actively contributing to cutting-edge research in wireless communications. With recent publications extending through June 2025, Dr. Zhao maintains an active research profile focusing on theoretical and practical aspects of next-generation wireless systems. Dr. Zhao's research spans several interconnected domains within wireless communications, with particular emphasis on coded caching techniques , satellite communication systems , and MIMO technologies . The work demonstrates sophisticated mathematical analysis of wireless channel characteristics, with specific attention to stochastic modeling of satellite networks , physical layer security mechanisms , and resource allocation optimization . Recent publications show increasing focus on near-field communications and semantic communications , reflecting the evolution of 6G research directions. The research methodology combines theoretical analysis with practical implementation considerations, often addressing real-world constraints in wireless network design. Among notable recognitions, Dr. Zhao was selected as a Best Student Paper Finalist at the ACSSC 2021 conference, one of only eight papers chosen from the total submissions. This recognition highlights the quality and innovation of the research contributions in the field of wireless communications. Dr. Zhao actively collaborates with researchers including Dirk Slock, Petros Elia, and Antonio Bazco-Nogueras, suggesting participation in a well-established research group focusing on information theory and wireless communications. The consistent publication record across top-tier IEEE journals and conferences indicates successful grant funding and institutional support for this research line.

Manon Guille-CollignonView profile

Senior Lecturer

• Bioelectrochemistry
• Electrochemistry
• Fluorescent Measurements
• +5 more

Manon Guille-Collignon is a Senior Lecturer at École Normale Supérieure (Sorbonne University) in Paris, France, with a focus on bioelectrochemistry and electrochemical-fluorescence coupling. Her research examines cellular secretion mechanisms, bioenergetics, and photosynthetic electron transfer using advanced microelectrochemical systems. Education: Ph.D. in Chemistry (2005), UPMC (Université Pierre et Marie Curie) Master’s in Condensed Matter (2005), UPMC Post-Doc with Marie Curie Fellowship (2005/2006) at University of Warwick Research Trends: Recent work spans electrochemiluminescence imaging of liposomes, quinone-based electron harvesting from algae, and dual electrofluorescent probes for exocytosis studies. She integrates microfluidics, TIRF microscopy, and amperometry to analyze cellular processes at molecular and single-cell levels. Scientific Contributions: Recipient of the Institut Universitaire de France Junior Member (2015) Emergences Ville de Paris Research Program (2014) Teaching: Instructs thermodynamics, electrochemistry, and biochemistry courses to L1-L3 and M1 students. Supervised 14 student/postdoc projects. Technical Expertise: Member of the International Society of Electrochemistry (ISE), Bioelectrochemical Society (BES), and French Group of Bioelectrochemistry (GFB).

Olivier RichouxView profile

Professor

• Topological Acoustics
• Wave Propagation
• Acoustic Metamaterials
• +3 more

Olivier Richoux is a Professor at the Institute of Acoustics, Department of Acoustics, University of Le Mans. His research spans topological acoustics, wave propagation in complex media, and acoustic metamaterials, with significant contributions to understanding nonlinear wave phenomena and topological edge states in engineered structures. Richoux's research interests focus on topological acoustics and nonlinear wave dynamics in structured media. His work explores how topological principles can create robust acoustic waveguides resistant to defects, and investigates soliton formation in periodic systems. Key areas include Su-Schrieffer-Heeger analogues in acoustic lattices, coherent perfect absorption in asymmetric networks, and energy spreading in disordered systems. His experimental-theoretical approach bridges fundamental physics with practical applications in sound control. Analysis of his recent publications (2021-2025) reveals a strong trend toward topological wave engineering in acoustic systems, particularly using Su-Schrieffer-Heeger models to create defect-immune wave paths. His work increasingly integrates nonlinear dynamics with topological concepts, examining soliton propagation and energy localization in disordered media. The research demonstrates consistent focus on experimental validation through acoustic waveguide networks and metamaterial implementations. Richoux actively collaborates within the Institute of Acoustics research ecosystem, particularly with teams working on ultrasonics, metamaterials, and nonlinear acoustics. His work frequently appears in high-impact journals including Physical Review B, Physical Review Applied, and Journal of the Acoustical Society of America, demonstrating strong integration within the international acoustics community. His laboratory work centers on acoustic waveguide networks and metamaterial testbeds for studying topological phenomena. The research group utilizes advanced measurement techniques including laser ultrasonics and optical methods for acoustic characterization, with facilities supporting both theoretical modeling and experimental validation of complex wave phenomena.

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.

Gregory HouzetView profile

Lecturer

• Microwave Engineering
• RF and Wireless Communications
• Antenna Design
• +4 more

Gregory Houzet serves as a Lecturer at University Savoie Mont Blanc, holding dual roles at the CROMA Laboratory (UMR CNRS 5130) and the University Institute of Technology (IUT) of Chambéry. At the IUT, he heads the Multimedia and Internet Professions (MMI) department and serves as Educational Manager for the DAEU B digitally oriented program. His responsibilities span teaching signal processing fundamentals, electronics for connected objects, and digital literacy across multiple technical curricula. Houzet's research centers on high-frequency electromagnetic systems with emphasis on Microwave Engineering, RF Communications, and Antenna Design. His work explores Ferroelectric Materials for tunable devices, Metamaterials for wave manipulation, and High-Frequency Characterization techniques extending to 100 GHz. Key applications include biomedical sensors, 5G/6G communications, and corrosion-resistant measurement systems. He employs both theoretical modeling and experimental validation through the CROMA Laboratory's advanced facilities. Analysis of his 2022-2025 publications reveals three dominant trends: (1) biomedical sensing through hyperfrequency measurements of physiological fluids, (2) low-cost characterization of corrosive materials using modified SMA probes, and (3) reconfigurable antenna systems for 4G/5G via printed electronics and metamaterials. His work consistently bridges academic research with practical engineering solutions, often featuring cost-effective methodologies and interdisciplinary collaboration within the CROMA framework. Houzet actively contributes to laboratory innovation through the CROMA research unit (UMR CNRS 5130), where his team develops measurement protocols for extreme environments and novel RF components. His leadership in the MMI department drives curriculum development for digital professions while maintaining strong ties to industry applications in IoT and sustainable technology.

• Computational Physics
• Numerical Electromagnetics
• Applied Optics
• +10 more

Thomas Grosges is a Full Professor at the University of Technology of Troyes (France) since 2013, specializing in computational and numerical physics, electromagnetics, applied mathematics, and plasmonics. His academic journey includes an Associate Professorship at UTT (2004-2013), post-doctoral research at UCL/VKI (Belgium) and CORIA/UTT (France), a PhD in Theoretical Physics from Catholic University of Louvain (1999), and a Professorial Thesis (Habilitation) at University of Technology of Compiegne (2007). Research Domains: Computational Physics, Numerical Electromagnetics, Applied Mathematics, Near-Field Optics, Plasmonics, Photothermal Therapy Key Projects: Numerical solver developments for near-field microscopy, optimization of plasmonic nanostructures, adaptive meshing techniques, and applications in biomedical imaging/cryptography His scientific output includes 89 publications with 10,392 reads, focusing on: Photothermal cancer therapy using metallic nanoparticles 3D adaptive remeshing for electromagnetic/thermal simulations Positron Emission Tomography (PET) image reconstruction Plasmonic field enhancement and uncertainty propagation Development of pseudo-random generators using chaotic systems Professor Grosges holds a patent on tomographic encryption methods and has contributed to pedagogical works on the European Credit Transfer System (ECTS) and near-field optical microscopy signal processing.

• Multi-objective optimization
• Artificial intelligence
• Renewable energies
• +4 more

Ouahib GUENOUNOU is a Researcher-Lecturer at CESI (Conservatoire d'Enseignement des Industries) in Le Mans, France. His primary research focuses on multi-objective optimization, artificial intelligence, renewable energy systems, and modeling/control of engineering systems. He holds a PhD in Automatic Systems from Toulouse University (2009), a Master's in Automatic and Signal Processing from Bejaia University (2003), and an Engineering degree in Electronics (1999). His work integrates fuzzy logic, genetic algorithms, and particle swarm optimization (PSO) to address challenges in photovoltaic energy conversion, MPPT (Maximum Power Point Tracking), and control systems. Key contributions include self-adaptive MPPT algorithms, hybrid energy systems optimization, and TSK fuzzy model simplification. He has published extensively in IEEE journals and conferences, with notable work on real-time implementation of fuzzy controllers and multi-objective optimization frameworks. GUENOUNOU teaches Electronic Control and Photovoltaic Energy at CESI, reflecting his expertise in applying theoretical models to practical engineering problems. His research team, Engineering and Numerical Tools, emphasizes computational methods for energy systems and control engineering.

Charles SOUSSENView profile

Professor

• Inverse Problems
• Sparse Approximation
• Signal Processing
• +3 more

Charles SOUSSEN is a Professor at CentraleSupélec and L2S (Laboratoire des Signaux et Systèmes). He holds a PhD from Université Paris-Sud (2000) and a Habilitation from Université de Lorraine (2013). His research focuses on inverse problems, sparse approximation, signal processing, and biomedical applications. He leads the Signals and Statistics Pole at L2S since 2020 and has coordinated the French ANR project BECOSE (2016–2020). His work spans areas like hyperspectral imaging, optical spectroscopy, and algorithm development for sparse representations. Education : Ecole Nationale Supérieure en Informatique et Mathématiques Appliquées (ENSI-MAG), Grenoble (1996) PhD in Signal and Image Processing, L2S, Université Paris-Sud (2000) Habilitation à Diriger des Recherches, Université de Lorraine (2013) Research Interests : His primary research areas include inverse problems, sparse approximation, signal processing algorithms, biomedical imaging, and hyperspectral data analysis. He develops methods for signal deconvolution, source separation, and modal analysis with applications in astronomy, geology, and medical diagnostics. Grants & Projects : ANR BECOSE (2016–2020): Sparse approximation algorithms for ill-conditioned inverse problems Labs & Teams : He is affiliated with L2S and leads the Signals and Statistics research group. His work involves collaborations in biomedical optics, hyperspectral analysis, and algorithm development for engineering and medical applications.

Sanghyeon YuView profile

Professor

• Plasmonics
• Subwavelength Resonators
• Mathematical Analysis
• +4 more

Sanghyeon Yu is a Professor in the Department of Mathematics at Korea University. His primary research focuses on plasmonics, subwavelength resonators, and mathematical analysis of physical phenomena. He has contributed extensively to understanding stress concentration in fluid dynamics, non-Hermitian systems in metamaterials, and acoustic waveguide behaviors in bubbly crystals. Education details not explicitly provided in text Key research interests include exploring subwavelength-scale phenomena, exceptional points in parity-time symmetric systems, and sparsity-based spectral estimation techniques. Recent work emphasizes quantitative analysis of Stokes flow dynamics between circular cylinders and guided acoustic modes in structured materials. His publications demonstrate trends in interdisciplinary mathematical physics, combining analytical methods with applications in optics, acoustics, and materials science. Collaborations involve institutions like ETH Zurich and Springer publications. No awards were explicitly mentioned in the provided text. Advising activity details are not listed here, though collaborations with researchers like Habib Ammari and Brian Davies suggest active team involvement. Research outputs include journal articles and book chapters addressing advanced topics in applied mathematics and engineering physics.