Nicolas Duchateau is an Associate Professor at Université Lyon 1 and researcher at the CREATIS lab in Lyon, France. He is also a Junior member of the prestigious Institut Universitaire de France (IUF) and serves as Associate Editor for the Neurocomputing journal. His academic career includes positions at Universitat Pompeu Fabra in Barcelona and INRIA Epione in Sophia-Antipolis, with his current role at Polytech Lyon's Biomedical Engineering department since 2016. His research focuses on characterizing diseases from medical imaging populations, with methodological development centered on statistical atlases and machine learning approaches to represent populations. On the applicative side, he concentrates on cardiac function and imaging modalities such as echocardiography and magnetic resonance. His work spans computational anatomy, pattern statistics, representation learning, manifold learning, auto-encoders, information fusion, cardiac imaging, shape and deformation analysis, risk stratification, and image synthesis. Duchateau's recent publications reveal strong trends in multimodal data fusion, particularly combining echocardiography with clinical records for patient stratification. He has made significant contributions to representation learning for cardiac population analysis, with increasing emphasis on diffusion models, uncertainty estimation, and domain adaptation techniques. His work bridges deep learning methodologies with clinical cardiology applications, focusing on interpretable AI solutions for cardiac function assessment. Junior Member of Institut Universitaire de France (2021) PhD prize for knowledge transfer from Universitat Pompeu Fabra (2014) Young Investigator Award at Euroecho conference (2010) Duchateau actively supervises numerous PhD and Master's students, including Anita Salvador, Thierry Judge, Pierre-Elliott Thiboud, and Romain Deleat-Besson. He has secured major research funding including a €75k grant from the Institut Universitaire de France (2021-2026), a €251k French ANR Young Researchers grant for the "MIC-MAC" project (2019-2024), and a €139k grant from the Fédération Française de Cardiologie for the "MI-MIX" project (2020-2023). He leads a vibrant research team at CREATIS lab focused on medical image analysis, where his group develops computational approaches to characterize cardiac diseases through population analysis. The team works at the intersection of machine learning, medical physics, and clinical cardiology, with ongoing projects spanning echocardiography analysis, MRI processing, synthetic data generation, and clinical decision support systems.
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Emmanuel Caruyer is a CNRS Researcher at the Empenn research unit within IRISA / Inria Rennes Bretagne Atlantique since 2014. He serves as Scientific Director of the Neurinfo imaging platform since November 2020 and leads the French side of the MMINCARAV associate team, an Inria-funded collaboration between Empenn and EPFL's Signal Processing Laboratory 5 in Lausanne. His academic background includes: PhD in Computer Science and Image Processing from Nice Sophia Antipolis University (2008-2012) under Dr. Rachid Deriche Postdoctoral research at University of Pennsylvania Medical School (2012-2014) with Prof. Ragini Verma Master of Science in Computer Science from University of Rennes 1 and École Normale Supérieure de Cachan (2005-2007) European Master from Universitat Politècnica de Catalunya (2006) Caruyer specializes in medical image acquisition and processing with deep expertise in diffusion MRI for brain white matter analysis. His work bridges theoretical mathematics with practical neuroimaging applications, focusing on optimal sampling protocol design and advanced reconstruction algorithms. He has pioneered techniques for multishell diffusion MRI acquisition that significantly improve data quality and efficiency in brain connectivity studies. His publication record demonstrates consistent innovation in diffusion MRI methodology, particularly in developing mathematically rigorous approaches for gradient table optimization and signal reconstruction. These contributions have established new standards for acquisition protocol design in the field. He received the prestigious Prix de thèse AFRIF '13 for his doctoral work on Q-space diffusion MRI. Prix de thèse AFRIF '13 Caruyer actively shapes the field through service as co-organizer of the 2nd HARDI Reconstruction Challenge, committee member for Computational Diffusion MRI workshops (CDMRI'13, CDMRI'14), and reviewer for top journals including IEEE Transactions on Medical Imaging and Magnetic Resonance in Medicine. His software contributions include Phantomas for structural connectivity evaluation and an open-source web application for diffusion MRI sampling scheme generation, both widely adopted in the research community. As Scientific Director of Neurinfo, he leads a major imaging platform supporting advanced neuroimaging research across multiple institutions, integrating hardware, software, and methodological expertise for cutting-edge brain studies.
Jing-Rebecca Li is a Professor and Research Scientist at ENSTA Paris, affiliated with the Applied Mathematics Unit (UMA) and INRIA Saclay as part of the IDEFIX research team. Her work bridges advanced mathematical techniques with medical imaging applications, particularly in diffusion MRI. She maintains a dual affiliation between ENSTA Paris, a leading engineering school in France, and INRIA, the French national research institute for digital science and technology. HDR (Habilitation à Diriger des Recherches) in Mathematics, Université Paris-Sud, 2013 Ph.D. in Mathematics, Massachusetts Institute of Technology, 2000 B.Sc. in Mathematics, University of Michigan, 1995 Dr. Li's research focuses on developing sophisticated numerical methods to solve partial differential equations with applications in diffusion magnetic resonance imaging. Her work spans brain and cardiac imaging, numerical linear algebra, machine learning algorithms for inverse problems in PDEs, and natural language processing tools. She has pioneered approaches to simulate diffusion MRI signals in complex biological tissues, enabling more accurate interpretation of imaging data for neuroscience and cardiology applications. Her research has significant implications for understanding brain microstructure and cardiac tissue organization through non-invasive imaging techniques. Her recent publications demonstrate a clear trend toward increasingly sophisticated modeling of biological tissues, with growing emphasis on cardiac applications alongside her foundational work in brain imaging. She has developed robust computational frameworks that incorporate permeable interfaces, geometrical deformations, and realistic neuronal geometries to better simulate diffusion MRI signals. Her work increasingly integrates machine learning with traditional numerical methods, creating hybrid approaches that leverage the strengths of both paradigms for microstructure estimation. Householder Prize for the best dissertation in Numerical Algebra (2002) Dr. Li has supervised numerous doctoral students across multiple institutions, with a focus on computational methods for diffusion MRI. Her current research is supported by significant grants including the Engineering for Health (E4H) interdisciplinary center project investigating biomarkers for Multiple Sclerosis through diffusion MRI (2023-2025). Previously, she led the ANR-funded SIMUDMRI project (2010-2014) and participated in the US-French Collaboration project on Computational Imaging of the Aging Cerebral Microvasculature (2013-2016). Her work demonstrates strong interdisciplinary collaboration between mathematics, computer science, and medical imaging communities. As leader of the IDEFIX research team at INRIA Saclay, Dr. Li directs a group focused on inversion methods for differential equations applied to imaging and physics problems. Her team has developed the SpinDoctor software package, a widely used MATLAB toolbox for diffusion MRI simulation that has become a standard tool in the field. The team maintains strong collaborations with Neurospin (CEA) and international research groups working on advancing diffusion MRI methodology and applications.
Paulo Loureiro de Sousa is a CNRS Research Engineer with a Habilitation to Supervise Research (HDR) at the University of Strasbourg. He serves as Operational Manager of the Human Neuroimaging division of the IRIS platform and is a former President of the French Society of Magnetic Resonance in Biology and Medicine (SFRMBM). His research focuses on quantitative MRI (qMRI) methodological development and clinical/preclinical biomarker discovery. Education: Habilitation to Supervise Research (HDR), University of Strasbourg (2018) Ph.D. in Physics/NMR, Universidade de Pernambuco (1999) M.Sc. in Medical Physics, Universidade de São Paulo (1995) B.Sc. in Physics, Universidade de São Paulo (1993) His work bridges MRI methodology (multi-parametric imaging, signal modeling, rapid imaging) with clinical applications in neuromuscular disorders and brain imaging. Publications emphasize technical advancements in T2 mapping, SSFP sequences, and UTE imaging for tissue characterization in neurological and muscular diseases. De Sousa supervises research theses in quantitative MRI, with completed theses on myelin imaging, neuromuscular disorders, and catatonia. His professional background spans institutions in France (CNRS, CEA, University of Paris-Sud) and Brazil (UFPE, USP), with expertise in NMR, MRI sequence development, and clinical research translation.
- Quantitative MRI (qMRI)
- Medical Imaging
- Muscle MRI
- +۳ مورد دیگر
Daniel ABERGEL is a Professor at Sorbonne University and Director of UMR8640, affiliated with the Department of Chemistry at École Normale Supérieure (ENS) in Paris. His research focuses on advanced nuclear magnetic resonance (NMR) techniques, particularly dynamic nuclear polarization (DNP) and protein dynamics. Research interests include: Development of DNP methodologies for metabolic pathway analysis Theoretical modeling of protein dynamics using molecular simulations Applications of hyperpolarized NMR in biomolecular systems Novel approaches in solid-state and solution NMR spectroscopy Quantum phenomena in spin systems including MASER effects His recent publications (2019-2024) demonstrate strong focus on: DNP methodology development, hyperpolarized metabolic monitoring, protein dynamics modeling, and quantum spin phenomena. Research consistently bridges theoretical physics with biochemical applications. As Director of UMR8640, he leads research in physical chemistry and biophysics, with collaborations evident in multidisciplinary publications addressing enzyme kinetics, materials characterization, and quantum control in NMR.
Jérôme BEUGNON is a Full Professor at Sorbonne Université and a member of the Kastler Brossel Laboratory at ENS-PSL. He holds a PhD from Institut d'Optique (2007) and completed his habilitation in 2018. His research focuses on ultracold quantum gases, including two-dimensional Bose gases and Rydberg Bose gases. He coordinates major grants such as the ERC Consolidator TORYD (2020-2025) and the ANR BOQUATRA project. BEUGNON teaches advanced courses in quantum statistical physics, thermodynamics, and quantum mechanics at the undergraduate and graduate levels. His experimental work explores superfluidity, quantum phase transitions, and coherence dynamics in ultracold atomic systems. Notable projects include the realization of Townes solitons and studies of many-body dephasing in spin-orbit-coupled systems. He has led pioneering experiments on light-matter interactions and coherence phenomena in Bose-Einstein condensates. Collaborations with theorists like Jean Dalibard and Sylvain Nascimbene have advanced understanding of low-dimensional quantum fluids. His lab employs advanced optical trapping and imaging techniques to probe quantum many-body systems.
Radu Patrice Horaud is a Researcher at Inria Grenoble Rhône-Alpes and a member of the RobotLearn team at Laboratoire Jean Kuntzmann, Université Grenoble Alpes. He holds a PhD from 1981 and has been a director of research since 1998. His work focuses on computational vision, audio-visual fusion, robotics, and machine learning, with applications in human-robot interaction and multimodal perception. Notable achievements include coordinating European projects, receiving two ERC grants (Advanced and Proof of Concept), and founding the Perception team. His research spans over 200 publications and includes contributions to 3D sensors, embodied audition, and Bayesian methods in inverse problems. Education: PhD (1981), postdoctoral work at SRI International (1982-1984), and CNRS researcher (1984-1998). Research Interests: Audio-visual scene analysis, robotics, machine learning, and multimodal fusion. His work emphasizes integrating audio and visual data for robotics and healthcare applications, such as the SPRING project in gerontological healthcare. Key Awards: ERC Advanced Grant (2014-2019), ERC Proof of Concept (2018-2019). His lab has also secured funding for projects like Socially Pertinent Robots in Gerontological Healthcare (SPRING). Labs/Teams: Formerly led the Perception team (2007-2021), now part of RobotLearn. Active in the Multidisciplinary Institute in Artificial Intelligence (MIAI) since 2019.
- Computer Vision
- Audio Signal Processing
- Robotics
- +۵ مورد دیگر
Gaspard HUBERمشاهده پروفایل
پژوهشگر
Gaspard Huber is a Researcher at the French Alternative Energies and Atomic Energy Commission (CEA), working within the IRAMIS institute, NIMBE unit, and LSDRM laboratory (Laboratory of Structure and Dynamics by Magnetic Resonance) since 2002. His research focuses on hyperpolarization techniques to overcome NMR's sensitivity limitations, with applications spanning metabolomics, biosensors, and materials science. His educational background includes: Chemical Engineering degree from ESCIL (now CPE Lyon), 1993 PhD from CEA Grenoble (1996, supervised by Dr. Jacques Gaillard) Habilitation to lead research (2006) Post-doctoral positions at University of Florence (1996-1998) and BIP-CNRS Marseille (1998-1999) Huber's research centers on hyperpolarization methodologies using parahydrogen and laser-polarized noble gases, enabling breakthroughs in metabolomic profiling of microscopic specimens and xenon-based biosensors . Current projects include ANR-funded SOFTNMR (flow NMR techniques) and HELPING (noble gas polarization), supporting active PhD and post-doctoral recruitment. His work bridges fundamental NMR physics with biomedical applications through supramolecular host-guest chemistry. Publication trends since 2013 reveal three interconnected domains: 1) Hyperpolarization engineering (SABRE/PHIP techniques for signal enhancement), 2) Supramolecular biosensors (cryptophanes/cucurbiturils for xenon detection), and 3) Metabolomic applications using HR-μMAS NMR. Key innovations include oxygen-carrying cucurbituril derivatives and single-scan diffusion-ordered NMR for hyperpolarized mixtures. He actively supervises students, with a new PhD fellow starting October 2024, and collaborates internationally through the HELPING project with laboratoire Kastler Brossel. His LSDRM laboratory provides specialized infrastructure for magnetic resonance research in materials and biomedicine. Notable patents include: 'Cucurbituril derivatives as oxygen carriers' (2017) NMR-based rubber characterization methods (2001) Reactor-NMR coupling devices (2000) Living cell analysis via NMR (2000)