جستجوی استاد راهنمای دانشگاهی

Christel Lefrançois is a Lecturer at the University of La Rochelle , France, affiliated with the CNRS scientific institute INEE. She conducts research in fish eco-physiology, bioenergetics, and predator-prey interaction kinematics, focusing on physiological and behavioral responses to environmental stressors like temperature shifts, oxygen variation, and pollutant exposure. Academic Rank: Lecturer Institution: University of La Rochelle Research Affiliation: CNRS INEE Laboratory: AMARE Team Her work spans marine environmental toxicology, climate change impacts on aquatic organisms, and nutritional ecology, particularly omega-3 fatty acid availability. She explores how environmental modifications affect fish metabolism, locomotion, and microbial communities, with recent studies on sunscreen pollution and heatwaves. Analysis of her 15 most recent articles reveals trends in marine pollution , climate adaptation , and nutritional constraints affecting fish physiology. Topics include UV filter contamination, omega-3 deficiency impacts, and microbiota disruption from chemical exposure. Her full portfolio includes collaborations across France and international institutions, with publications in journals like Marine Environmental Research , Aquatic Toxicology , and Journal of Experimental Biology , emphasizing interdisciplinary approaches to environmental challenges.

• Fish eco-physiology
• Bioenergetics
• Kinematics of predator-prey interactions
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Djamal Zeghlache is a Professor at Telecom SudParis (now part of Institut Polytechnique de Paris), where he leads research in network virtualization, cloud computing, and software-defined networking. His work spans multiple domains including wireless communications, network security, and AI applications in networking systems. Affiliated with the SAMOVAR laboratory (Services and Architectures for Multimedia and Ubiquitous Access to Resources), he has established himself as a prominent researcher in network and service management. Professor Zeghlache's research interests focus on the intersection of networking and artificial intelligence, with particular emphasis on network virtualization, cloud computing, and software-defined networking. His work addresses critical challenges in network function virtualization, resource allocation, and optimization in distributed systems. In recent years, he has expanded his research to include AI-driven approaches for network management, healthcare monitoring using wireless technologies, and optical network control. His publications demonstrate a consistent trajectory from traditional networking challenges to more complex, AI-integrated solutions for modern network infrastructures. Analysis of his recent publications (2023-2025) reveals a strong trend toward integrating machine learning, particularly deep reinforcement learning and graph neural networks, into network management systems. His work spans multiple application areas including healthcare monitoring (using RF-based techniques for vital sign detection), transportation systems (bike-sharing optimization), and optical networking (disaggregated control frameworks). The interdisciplinary nature of his research connects computer networking with biomedical engineering, urban mobility, and formal methods for system verification. Professor Zeghlache has supervised numerous PhD students and collaborated extensively with researchers across Europe and internationally. His work has resulted in significant contributions to network virtualization, cloud resource management, and network security. He has been involved in multiple research projects focusing on next-generation networking technologies, including 5G, network slicing, and cloud-native network functions.

• Network Virtualization
• Cloud Computing
• Software-Defined Networking
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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.

• Medical Imaging
• Cardiac Function Analysis
• Statistical Atlases
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Stéphane Nottin is a Professor of Exercise Physiology at Avignon Université, affiliated with the STAPS school and the Laboratory of Experimental Cardiovascular Physiology (LaPEC). His research focuses on cardiac adaptations to physical exercise, particularly in extreme conditions like ultra-trail running and the effects of doping substances such as anabolic steroids in strength sports. He uses innovative echocardiographic techniques for his studies, targeting top-tier exercise physiology journals. Key affiliations: LaPEC, Avignon Université, STAPS department Research interests include understanding how the heart adapts to prolonged training, identifying early-stage vascular/cardiac dysfunction, and studying stress-induced cardiac aggravation in athletes. He emphasizes interdisciplinary methods combining physiology, fluid mechanics, and programming to analyze cardiac function. Recent publications address anabolic steroids' impact on bodybuilders' myocardium and long-term exercise effects on young triathletes' cardiac function. No specific grants or awards are listed, though he supervises doctoral students and advocates for open science practices through platforms like HAL Avignon. LaPEC's broader work examines cardiovascular health improvement via exercise/nutrition in cardiometabolic disease contexts, with a continuum from early-stage dysfunction detection to stress-induced pathology studies.

• Exercise Physiology
• Cardiovascular Adaptations
• Doping Effects
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Irene Netchine is a leading researcher in pathophysiology of fetal growth , affiliated with Sorbonne University and Saint-Antoine Hospital in Paris, France. Her work focuses on the IGF system and parental imprinting in growth-related disorders, including Silver-Russell syndrome (SRS) , Beckwith-Wiedmann syndrome (BWS) , and Temple syndrome (TS14) . PU-PH (HDR) at Sorbonne University Team Leader at Saint-Antoine Hospital Her research spans genomic imprinting , epigenetic methylation , and IGF signaling , with a focus on molecular mechanisms of 11p15 and 14q32 imprinting disorders. She has developed high-throughput sequencing panels for diagnosing fetal growth anomalies and pioneered iPSC models with balanced methylation for studying imprinting pathologies. Recent publications highlight her expertise in cellular modeling of imprinting disorders, environmental influences on methylation, and clinical-genomic correlations in rare growth syndromes. She leads the RADICO-IDMET cohort and contributes to international consensus statements on SRS management.

• Fetal Growth Disorders
• Genomic Imprinting
• Epigenetics
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Glenda Comai is a Permanent Researcher (CRCN CNRS) at the Department of Developmental and Stem Cell Biology, CNRS UMR 3738, Institut Pasteur, Paris, where she has worked since 2016 in Prof. Shahragim Tajbakhsh's laboratory. She serves on the Advisory Board of the French Society of Developmental Biology (SFBD) since 2019 and recently obtained her HDR (Habilitation à diriger des Recherches) from Université Paris Saclay in 2024. Her research focuses on understanding the mechanisms underlying tissue patterning during development, with special emphasis on craniofacial muscles. Using 3D-imaging, mouse genetics, and single cell/single nuclear omics approaches, she investigates how heterogeneity at the level of stem cells and myofibers is established and how this impacts muscular genetic disorders. Her work reveals significant differences between extraocular muscle stem cells and those from limb muscles, particularly in their regenerative capacity and molecular signatures. Analysis of her recent publications shows a strong trend toward understanding the transcriptional and epigenetic regulation of muscle stem cell identity, particularly how anatomical location influences stem cell behavior. Her research spans developmental biology, stem cell biology, and muscle physiology, with particular emphasis on craniofacial development and muscle regeneration mechanisms. AFM PRC Grant (2024-2026) ANR JCJC Grant (2021-2024) AFM Trampolin Grant (2020-2021) As Principal Investigator, Dr. Comai leads two major research projects: AFM-Trampolin: Cellular and genetic basis for robustness of cranial myogenic populations (ENCRYPT) and ANR-JCJC: Mechanisms defining functional heterogeneity of anatomically distinct myogenic populations (MUSE). Her laboratory employs advanced techniques including bioimage analysis, confocal microscopy, stem cell culture, and various molecular biology methods to investigate muscle development and regeneration.

• Developmental Biology
• Stem Cell Biology
• Muscle Development
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Martial Mermillod is a Full Professor at Grenoble Alpes University , directing the Laboratoire de Psychologie et NeuroCognition (LPNC, CNRS UMR 5105) since 2021. His academic career spans institutions like Clermont-Ferrand 2 and Liège University, with a focus on Neural Computation, Psychology, and Cognitive Neuroscience . As a holder of MIAI 3IA Chairs (2019-2023, 2023-2025) and CNRS delegations, he bridges AI and human cognition. PhD in Cognitive Sciences (University of Liège, 2004) Post-doctoral research at LPNC (2004-2005, Fyssen Foundation) Master's DEA (University of Savoie, 2000) His research explores bio-inspired AI , particularly lifelong learning in neural networks, the Predictive Brain Hypothesis (top-down cognitive modulation), and the role of spatial frequencies in threat detection . He has developed models for robust AI against adversarial attacks, inspired by human brain mechanisms. Key contributions include: 15+ peer-reviewed articles (2015-2023) on AI, emotion recognition, and cognitive neuroscience Grants from MIAI 3IA , CEA , ANR , and NVIDIA Leadership in national and international projects (MIT MISTI, Franco-German AI4HP) He leads the Vision & Emotion team at LPNC and has held administrative roles including UGA Council member and co-founder of the Institut Carnot Cognition steering committee.

• Neural Computation
• Psychology
• Cognitive Neuroscience
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Giacomo Nardi is an Associate Professor (Maître de Conférences) at INSA Rouen starting September 2025, holding a PhD in Mathematics from Sorbonne University. His research bridges mathematical analysis and biomedical image processing through extensive collaborations with the Institut Pasteur in Paris, focusing on developing advanced mathematical frameworks for biological imaging applications. His educational background includes: PhD in Mathematics, Sorbonne University (Pierre and Marie Curie), thesis: "On a characterization of the relaxation of a generalized Willmore functional" under supervisors S. Masnou and E. Mascolo. Nardi's work spans Biomedical Image Processing and Mathematical Analysis , evolving from foundational contributions in calculus of variations (Willmore functionals, BV functions) to cutting-edge biomedical applications. His current research employs topological data analysis and geometric methods to solve complex biological imaging problems including 3D segmentation of hematopoietic stem cells, receptor dynamics analysis, and retinal degeneration quantification. This interdisciplinary approach integrates deep mathematical theory with practical biological challenges through close partnerships with biologists at the Institut Pasteur. Analysis of his publication trajectory reveals a decisive shift toward biomedical applications since 2016, with recent work (2022-2025) dominating in high-impact imaging journals and conferences. His research consistently applies geometric measure theory and functional analysis to innovate in cellular dynamics modeling and 3D image segmentation, demonstrating exceptional versatility across pure mathematics and translational biomedical research. Nardi actively mentors the next generation of researchers: Postdoctoral supervision: Somoballi Ghoshal (2024-present, Institut Pasteur) on persistent homology-based 3D cell segmentation in cardiac tissue. PhD supervision: Benjamin Dorra (defense March 5, 2025, Medetia/Institut Pasteur) on AI-driven histopathology for ciliopathy models, resulting in ISBI 2024 award-winning publications. He co-develops the open-source Fshapestk (Functional shapes toolkit) and collaborates with J.-C. Olivo-Marin's Quantitative Image Analysis Unit at the Institut Pasteur, maintaining strong ties between theoretical mathematics and experimental biology through ongoing joint projects in cellular dynamics and tissue imaging.

• Biomedical Image Processing
• Mathematical Analysis
• Computational Geometry
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Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur , Nice, France, and holds a Chair in Artificial Intelligence. He serves as Director of the Institut 3IA Côte d’Azur and leads the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. He is an associate editor for The Annals of Applied Statistics and founded the Statlearn workshops . Research Interests: Statistical learning in high dimensions Learning on networks and functional data Deep latent variable models Adaptive learning with uncertain labels Applications in Medicine, Image Analysis, Astrophysics, and Humanities Notable Contributions: Developed multiple R packages including HDclassif , FisherEM , and FunLBM . Created the Linkage.fr platform for network analysis with textual edges. PhD Students: Current: Seydina Niang (Deep Generative Models), Kilian Burgi (Marine Diversity Monitoring), Baptiste Pouthier (Multimodal Learning) Former: Giulia Marchello (Dynamic Networks), Rémi Boutin (Network Analysis), Dingge Liang (Recommender Systems), Nicolas Jouvin (Latent Variable Models), Alexandre Saint-Dizier (Image Aggregation), Warith Harchaoui (Optimal Transport), Pierre-Alexandre Mattei (Sparse Clustering), Rawya Zreik (Temporal Networks), Anastasios Bellas (Anomaly Detection), Camille Brunet (Sparse Clustering) Contact: Email: charles.bouveyron@univ-cotedazur.fr / charles.bouveyron@inria.fr Postal: Equipe Maasai, Inria Sophia Antipolis, 2004 route des Lucioles, 06902 France

• Statistical Learning
• High-Dimensional Data Analysis
• Network Statistics
• +۷ مورد دیگر

Yanick Crow is the Head of the Neurogenetics and Neuroinflammation research team, focusing on understanding genetic and immunological mechanisms underlying neuroinflammatory disorders. His work has centered on Aicardi-Goutières syndrome (AGS) and type I interferonopathies, linking monogenic mutations to autoinflammatory pathways. The team investigates nucleic acid-driven inflammation, interferon signaling defects, and potential therapies targeting these mechanisms. Research interests include the interplay between genetic mutations, interferonopathies, and autoimmune diseases such as systemic lupus erythematosus. Collaborations with clinicians and immunologists allow translational studies connecting basic science to clinical applications, with a focus on developing novel treatments for rare and complex immune disorders. Key contributions involve identifying genetic drivers of autoimmunity and proposing JAK inhibitors as therapeutic candidates in interferonopathies. Team members include clinicians (PU-PH), postdoctoral researchers, and PhD students, reflecting multidisciplinary expertise. Publications highlight advancements in understanding cGAS-STING pathway dysregulation, COPA mutations, and mitochondrial nucleic acid roles in pathogenic inflammation. Ongoing work explores molecular mechanisms and clinical trials for interventions in neuroimmunological conditions.

• Neurogenetics
• Neuroinflammation
• Autoinflammatory Disorders
• +۳ مورد دیگر

François-Xavier LAPÉBIE is a researcher affiliated with the Aging Team under the ARTERRE Axis of the Center for Epidemiology and Population Health Research (CERPOP, UMR 1295), a joint unit between INSERM and University Toulouse III Paul Sabatier. His work focuses on vascular epidemiology, particularly evaluating risks and treatments for atherosclerosis, venous thromboembolism, and systemic sclerosis. He contributes to large-scale observational studies and clinical trials, emphasizing evidence-based strategies for managing vascular diseases. His research spans clinical epidemiology, interventional cardiology, and rheumatology, with a strong emphasis on translational studies bridging basic science and clinical practice. Key areas include the optimization of anticoagulant therapies, assessment tools for vascular damage in systemic sclerosis, and diagnostic accuracy of imaging modalities like contrast-enhanced ultrasonography. Publications highlight his work on chronic limb-threatening ischemia, venous stent durability, and cancer-associated thrombosis. Collaborations involve multidisciplinary teams across Europe, addressing gaps in vascular care through cohort studies and randomized trials. His contributions aim to refine risk stratification and improve patient outcomes in high-risk vascular conditions. LAPÉBIE’s studies frequently integrate real-world clinical data, such as the COPART registry, to evaluate long-term amputation risks and treatment efficacy. Methodologically, he emphasizes rigorous observational designs and standardized diagnostic protocols to enhance clinical decision-making in complex vascular cases.

• Vascular Medicine
• Epidemiology
• Cardiovascular Diseases
• +۳ مورد دیگر

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.

• Diffusion Magnetic Resonance Imaging
• Numerical Methods for Partial Differential Equations
• Numerical Linear Algebra
• +۴ مورد دیگر

Laura Sanchez Gonzalez is a Senior Lecturer (HDR) at the University of Lorraine, France, specializing in biomaterials and encapsulation. Her research bridges food science and biomedical engineering, with a focus on developing functional materials using natural polymers for food preservation and tissue engineering applications. Her core research areas include: Biomaterials development using chitosan, alginate, gelatin, and pectin Nanoliposome and microsphere encapsulation technologies Antimicrobial edible films for food packaging 3D bioprinting of hydrogels for regenerative medicine Physicochemical characterization of polymer-based delivery systems Essential oil integration for active food packaging Analysis of her 2020-2024 publications reveals a strategic shift toward advanced biomaterials for tissue engineering, particularly nanoliposome-functionalized GelMA hydrogels for 3D bioprinting. Her work demonstrates consistent innovation in translating food science principles (e.g., edible films) to biomedical applications (e.g., drug delivery scaffolds), with increasing emphasis on sustainable agro-resources and multi-scale material design. No scientific awards or honors were documented in the available materials. As an HDR-qualified academic (Habilitation à Diriger des Recherches), she supervises PhD students and leads research projects, though specific grant details are unreported. Her work operates within collaborative frameworks at the University of Lorraine, likely involving food science and biomaterials research units focused on both industrial food applications and regenerative medicine.

• Biomaterials
• Food Science
• Encapsulation
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Sylvie Garcia is a Group Leader and Head of the 'Humanized mice and in vivo analysis of human Plasmodium infections' group at the Institut Pasteur, Paris. Her research focuses on developing humanized mouse models to study malaria (Plasmodium species) and other human diseases, with an emphasis on immune system reconstitution and host-pathogen interactions. She leads projects on Plasmodium vivax blood-stage infections, innate CD8 T cell responses during Salmonella infections, and the creation of novel humanized mouse models (e.g., CH1-2hSa mice). Her work bridges fundamental immunology, parasitology, and translational medicine, aiming to improve therapies and vaccines. Her group collaborates with the Institut Pasteur's Drug Discovery & Screening platform and has contributed to studies on HIV replication dynamics, T cell memory, and humanized stem cell-derived cardiac progenitors. Key achievements include establishing models for sustained Plasmodium vivax infections and demonstrating the role of Bcl-2 in regulating non-cognate CD8 T cell survival during Salmonella infections. Garcia's research also explores the mechanisms of immune responses in chronic diseases and the use of humanized mice to study human-specific pathogens. Her lab is part of the Biology of Plasmodium Infection and Transmission department, with a focus on advancing in vivo models for malaria transmission and immune evasion studies. Her team includes PhD students (Barbara Mouyama, Dorenda Schoorl) and collaborators like Sabine Thiberge (co-author on key malaria studies). The group has published extensively in high-impact journals like Nature Communications and PLoS One, with a focus on translational immunology and parasitology.

• Malaria Research
• Immunology
• Humanized Mice Models
• +۳ مورد دیگر

Guy Claireaux serves as a University Professor at the University of Western Brittany, France, conducting cutting-edge research on marine fish physiological responses to environmental stressors including hypoxia, ocean acidification, and warming. His work primarily focuses on European sea bass and polar cod, examining cardiovascular, respiratory, and metabolic adaptations critical for survival in changing ocean conditions. His research spans hypoxia tolerance mechanisms, thermal biology, and synergistic stressor effects, employing controlled experiments to investigate oxygen transport systems, metabolic plasticity, and behavioral responses. This work reveals individual variation in physiological resilience and has direct applications for fisheries management and marine conservation under climate change, emphasizing the vulnerability of key species to anthropogenic pressures. Analysis of his recent publications shows concentrated investigation into transgenerational acclimation potential and multi-stressor interactions, particularly ocean acidification combined with warming or oil pollution. His findings highlight both adaptive capacities and critical thresholds in fish physiology, providing essential data for predicting ecosystem impacts and informing marine protection policies in warming oceans. Dr. Claireaux collaborates internationally through projects including Flamenco, OASYS, PACIO, and Seawise, addressing marine environmental challenges through integrated physiology-ecosystem approaches. Though previously affiliated with an unspecified laboratory at the University of Western Brittany, his current work maintains strong institutional ties while contributing to global understanding of fish responses to anthropogenic ocean change.

• Marine Biology
• Fish Physiology
• Environmental Physiology
• +۳ مورد دیگر