جست‌وجوی استاد و پژوهشگر

Sophie Goyardمشاهده پروفایل

استادیار

• Microbiology
• Molecular Biology
• Bioluminescence
• +۴ مورد دیگر

Sophie Goyard is an Assistant Professor (CR IP, PhD) at the Institut Pasteur, specializing in microbiology and molecular biology. She serves as the Program Coordinator for the Amgen Scholars/Institut Pasteur Projects and leads research initiatives focused on bioluminescent systems and infectious diseases. Research Interests: Her work spans microbiology , molecular biology , and bioluminescence , with applications in infectious disease modeling and immunological assays . She has developed highly sensitive bioluminescent methods for detecting allergen-specific IgE and optimized luciferase systems for cellular and in vivo studies. Key Contributions: Goyard's recent publications highlight her expertise in T-cell biology , SARS-CoV-2 seroprevalence , and biofilm formation . Her 2023 study on T-cell migration in familial adenomatous polyposis patients underscores her role in translational immunology. Scientific Recognition: While no specific awards are listed, her consistent publication in high-impact journals ( Frontiers in Immunology , Nature Communications , Science Advances ) reflects significant scholarly influence. Leadership & Collaboration: She coordinates the Amgen Scholars program and collaborates on projects like "Optimization of luciferase activity" and "Design of new bioluminescent systems" with teams at Institut Pasteur.

Christophette Blanchet-Scallietمشاهده پروفایل

مدرس

• Applied Probability
• Statistics
• Stochastic Processes
• +۴ مورد دیگر

Christophette Blanchet-Scalliet is a Lecturer in the Department of Mathematics and Computer Science at École Centrale de Lyon, affiliated with the Camille Jordan Institute (UMR CNRS 5208). She obtained her doctorate in 2001 and her Habilitation to Supervise Research in 2016. Currently serving as Director of the Mathematics and Computer Science Department and Head of the Dual Diploma program at Centrale Lyon-ENSAE, she has been active in academia since 2002, with positions at both École Centrale de Lyon (since 2007) and the University of Nice Sophia-Antipolis (2002-2007). Her research spans applied probability , statistics , stochastic processes , stochastic control , Kriging , and robust optimization . She has developed significant expertise in Ornstein-Uhlenbeck processes, backward stochastic differential equations, Hamilton-Jacobi-Bellman equations, and applications in financial mathematics. Her work bridges theoretical probability with practical applications in risk assessment, insurance, and optimization under uncertainty. Analysis of her recent publications reveals a consistent focus on stochastic processes and their applications, with increasing emphasis on computational methods, sensitivity analysis, and optimization techniques. Her research shows strong connections between theoretical probability and practical applications in finance, insurance, and environmental risk assessment, with growing interdisciplinary collaboration across mathematical fields. Dr. Blanchet-Scalliet has supervised five doctoral students since 2015: Mélina Ribaud (2015-2018), Laura Gay (2016-2019), Thierry Gonon (2019-2022), Benoit Nieto (2021-2024), and Noé Fellmann (2021-2024), typically in co-supervision with colleagues from related research groups. She has secured significant research funding through multiple projects including the CIROQUO Consortium (2020-2028), ANR DREAMES (2021-2025), ANR Oquaido Chair (2015-2020), and others. She is actively involved in the CIROQUO research consortium as co-leader, which brings together multiple academic institutions and industry partners including École Centrale de Lyon, Mines Saint-Etienne, University of Toulouse 3, Stellantis France, BRGM, CEA, IFP Energies Nouvelles, and others to advance research in uncertainty quantification and optimization.

Bruno Gaujalمشاهده پروفایل

استاد پژوهشی

• Performance Evaluation
• Optimization
• Discrete Event Dynamic Systems
• +۵ مورد دیگر

Bruno Gaujal is a Research Professor at Inria Grenoble-Rhône-Alpes, affiliated with Université Grenoble Alpes. He obtained his PhD from the University of Nice in 1994 under François Baccelli's supervision and has held positions at AT&T Bell Labs, INRIA, and École Normale Supérieure de Lyon. He previously led the MESCAL (now POLARIS) research group focused on large-scale computing until 2015. His research interests center on performance evaluation, optimization, and control of discrete event dynamic systems with stochastic inputs. Specific areas include: Markov Chains and Markov Decision Processes Reinforcement Learning and stochastic optimization Queueing theory and scheduling algorithms Energy-efficient computing in distributed systems Game-theoretic approaches in network optimization Gaujal's recent publications show strong emphasis on reinforcement learning applications in queueing networks, energy optimization for real-time systems, and scalable algorithms for Markov Decision Processes. His work bridges theoretical frameworks like Whittle indices with practical implementations in cloud computing and distributed systems. He has supervised numerous PhD students including Nicolas Gast (now Inria researcher), Anne Bouillard (Huawei researcher), and Emmanuel Hyon (Paris Nanterre professor). Current students include Hélène Arvis and Romain Cravic. Gaujal co-founded RTaW, a startup specializing in real-time network design tools. At Inria, he leads research in the POLARIS group, focusing on optimization methods for large-scale distributed computing infrastructures. His work involves collaborations with 85+ co-authors across institutions globally.

Panayotis Mertikopoulosمشاهده پروفایل

استادیار

• Game Theory
• Machine Learning
• Optimization
• +۴ مورد دیگر

Dr. Panayotis Mertikopoulos is a CNRS researcher (chargé de recherche) at the Laboratoire d'Informatique de Grenoble, part of Université Grenoble Alpes. He is affiliated with the Inria/LIG joint team POLARIS and has held visiting positions at UC Berkeley, EPFL, LUISS University of Rome, and NKUA. His academic journey includes completing his PhD at the University of Athens in 2010 on "Stochastic perturbations in game theory and applications to networks" and his Habilitation à Diriger des Recherches (HDR) in 2019 on "Online optimization and learning in games: Theory and Applications". Dr. Mertikopoulos' research spans several interconnected fields at the intersection of mathematics, computer science, and economics. His primary research interests include: Game theory and its applications to network design and resource allocation Online learning algorithms and their convergence properties Optimization methods for non-convex and stochastic problems Applications to machine learning, signal processing, and wireless networks Quantum game theory and quantum computing applications His extensive publication record shows a clear evolution from foundational work in game dynamics and learning theory toward increasingly sophisticated applications in machine learning and network optimization. Recent work demonstrates growing interest in quantum game theory, non-convex optimization, and the mathematical foundations of deep learning. His research consistently bridges theoretical insights with practical applications, particularly in communication networks and distributed systems. Among his notable achievements is receiving the INFORMS best paper award in the network analytics section in 2022 for his work on "Robust power management via learning and game design". His publications have appeared in top venues including NeurIPS, ICML, COLT, IEEE Transactions, and leading economics and operations research journals. Dr. Mertikopoulos has supervised numerous PhD students and postdoctoral researchers, though specific names are not listed in the available information. He has secured research funding for projects at the intersection of game theory, optimization, and machine learning, with applications to network design and resource allocation. His collaborative work spans multiple institutions across Europe and North America. As a member of the POLARIS research team at Inria/LIG, he contributes to a vibrant research environment focused on parallel and distributed systems. His work often intersects with colleagues researching optimization algorithms, machine learning theory, and network science, creating opportunities for cross-disciplinary collaboration on complex computational problems.

Clément CABRIELمشاهده پروفایل

پژوهشگر

• Single-Molecule Localization Microscopy (SMLM)
• Bioimaging
• Nanophotonics
• +۵ مورد دیگر

Clément CABRIEL is a CNRS Researcher affiliated with the Institut Langevin (ESPCI Paris / PSL University). He specializes in Single-Molecule Localization Microscopy (SMLM) and its applications across bioimaging , nanophotonics , and microfabrication . Key Collaborations: Works with Ignacio Izeddin (Institut Langevin) and international teams on interdisciplinary projects. Research Focus: Develops 3D super-resolution techniques, event-based sensors for high spatio-temporal imaging, and microstructured substrates for cellular modeling and axial calibration. Achievements: Pioneered SMLM calibration tools using fractal-like substrates (2025), explored M2d macrophage differentiation via 3D topographies (2024), and advanced event-based sensor technology for dense single-molecule imaging (2023). His work bridges optics , materials science , and cell biology . Community Engagement: Co-organizes the Young Scientist Network GDR Imabio to foster European bioimaging collaboration and career development for early-career researchers.

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

• Monte Carlo Methods
• Statistical Learning
• Semiparametric Statistics
• +۴ مورد دیگر

François Portier is an Associate Professor at CREST-ENSAI (École Nationale de la Statistique et de l'Analyse de l'Information) and Head of the Master for Smart Data Science program. He serves as Associate Editor for both the Electronic Journal of Statistics and Computational Statistics & Data Analysis . His research spans Monte Carlo Methods , Statistical Learning , and Semiparametric/Nonparametric Statistics , with focus areas including stochastic optimization, probability measure approximation, and local-averaging estimators. Recent work demonstrates strong computational statistics emphasis in adaptive sampling techniques and model validation frameworks. His publication trends reveal concentrated contributions to Stochastic optimization algorithms (2025) Dimension reduction in classification (2025) Covariate shift adaptation (2025) Density model diagnostics (2025) showcasing methodological innovation in high-dimensional statistical inference. Portier maintains active academic service through editorial roles and leads ENSAI's Smart Data Science program, bridging theoretical statistics with data science applications.

Jean-Luc BOUDENNEمشاهده پروفایل

استاد

• Environmental Science
• Analytical Chemistry
• Water Pollution
• +۳ مورد دیگر

Jean-Luc BOUDENNE is a University Professor affiliated with the Transfer Reactivity Analysis Micropollutants Environment (TRAME) team. His research focuses on environmental chemistry, toxicology, and analytical methods for water quality monitoring. Academic Rank: Professor Research Team: TRAME His work addresses critical issues in Environmental Science and Analytical Chemistry , particularly the fate of UV filters in coastal ecosystems, disinfection byproducts in swimming pools, and optimization of sample extraction techniques. Recent studies highlight his leadership in assessing health risks from micropollutants and biopesticides . Key trends in his publications include: Development of 3D-printed analytical devices for environmental monitoring Ecotoxicology of sunscreen components in marine ecosystems Statistical optimization of extraction methods for contaminants Public health risk assessment of industrial and recreational water use

• Nuclear Magnetic Resonance (NMR)
• Solid-State NMR
• Dynamic Nuclear Polarization
• +۵ مورد دیگر

Stéphane VIEL is a Professor at Aix-Marseille University specializing in Nuclear Magnetic Resonance (NMR) spectroscopy and materials characterization. He serves as Head of the NMR group within the Applied Spectrometry for Structural Characterization (SACS) team at the Institute of Radical Chemistry (ICR). His research focuses on developing advanced NMR methodologies, particularly solid-state NMR enhanced by Dynamic Nuclear Polarization (DNP), to overcome sensitivity limitations and achieve high-resolution structural analysis of complex materials. His research spans multiple domains including polymer chemistry, materials science, and analytical chemistry. VIEL's work specifically targets structural elucidation, nanostructuring properties, and aging mechanisms of polymer materials. His recent publications reveal a strong emphasis on applying DNP-NMR techniques to study crystallization processes, polymorph transformations, and molecular dynamics in confined environments. His research bridges fundamental science with practical applications in pharmaceuticals, energy storage, and healthcare materials. VIEL's teaching activities concentrate on analytical chemistry and spectroscopy at both Bachelor's and Master's levels. His educational contributions complement his research by training the next generation of scientists in advanced characterization techniques. Junior Member of the University Institute of France (2015-2020) As leader of the NMR group at ICR, VIEL directs research initiatives that combine molecular spectroscopy with materials chemistry to address challenges in health and environmental science. His work on DNP-NMR has significantly advanced the field by improving detection limits by several orders of magnitude, enabling previously impossible structural analyses of complex materials.

• Robotics
• Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Nicolas Perrin-Gilbert is a CNRS Research Fellow at the Institute of Intelligent Systems and Robotics (ISIR), which is part of Sorbonne University in Paris, France. He has been working at ISIR since 2013 as part of the MLIA (Machine Learning and Artificial Intelligence) team, where he conducts research at the intersection of robotics and machine learning. His office is located at ISIR, Campus Pierre et Marie Curie, 4 place Jussieu, BC173, 75005 Paris. Dr. Perrin-Gilbert's research focuses on developing advanced algorithms for robot locomotion, control, and learning. His work bridges theoretical machine learning with practical robotics applications, particularly in reinforcement learning, motion planning, and humanoid robotics. He has made significant contributions to quality-diversity algorithms, state representation learning, and controller synthesis for dynamic multi-agent systems. His approach often combines insights from control theory with modern machine learning techniques to solve complex robotics challenges. His publication record shows consistent research output since 2010, with a notable acceleration in recent years. His work spans both theoretical contributions (such as unifying frameworks for motion planning and diversity search) and practical implementations (including humanoid robot control systems). A key trend in his research is the integration of motion planning techniques with reinforcement learning, creating more efficient and robust learning systems for robotics applications. Dr. Perrin-Gilbert is the primary maintainer of xpag , a modular reinforcement learning library with JAX agents that supports standard reinforcement learning and goal-conditioned reinforcement learning. This open-source project demonstrates his commitment to developing practical tools for the research community. The library has gained significant traction with 27 GitHub stars and 6 forks, and is used by other researchers in the field. His collaborations span multiple institutions and research groups, with frequent co-authorship with researchers from ISIR including Olivier Sigaud, Alexandre Chenu, and Stéphane Doncieux. He has also collaborated with researchers from other institutions on projects involving humanoid robots like COMAN and Cassie, demonstrating the applied nature of his work.

• Hyperpolarization
• Nuclear Magnetic Resonance
• NMR Spectroscopy
• +۳ مورد دیگر

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)

• Materials Science
• Crystallography
• Nanotechnology
• +۵ مورد دیگر

Dr. Mark Levenstein is a Permanent Researcher at the Interdisciplinary Laboratory on Nanometric and Supramolecular Organization (LIONS) within the Nanosciences and Innovation for Materials, Biomedicine and Energy Institute (NIMBE) at CEA, University of Paris-Saclay. He leads the liquid-phase platform of the FastNano project (PEPR DIADEM), focusing on advanced materials development through innovative experimental approaches. Dr. Levenstein's research spans multiple cutting-edge areas in materials science. His work on non-classical crystal nucleation and growth pathways has provided new insights into inorganic materials formation. He specializes in developing fluidic sample environments for in situ X-ray scattering , creating automated platforms for high-throughput materials synthesis and characterization. More recently, he has applied these techniques to optimize cementitious and carbonation reactions in low-CO2 cements , addressing critical sustainability challenges in construction materials. Analysis of Dr. Levenstein's recent publications reveals a strong focus on microfluidic and X-ray characterization techniques applied across diverse fields from pharmaceuticals to marine biology. His work consistently bridges fundamental materials science with practical applications, demonstrating expertise in both experimental design and advanced characterization methods. The interdisciplinary nature of his research is evident in collaborations spanning materials science, marine ecology, and pharmaceutical development. Finalist, Art in Science contest, MMC2019, Manchester, UK Winner, Research Image Contest, Beckman Institute, 2020 2019 Chemical Science HOT Article recognition Dr. Levenstein's research is supported through the FastNano project and has resulted in numerous high-impact publications in top journals including IUCrJ, ACS Sustainable Chemistry & Engineering, Lab on a Chip, and Chemical Science. His work demonstrates strong international collaboration and translation of fundamental research into practical applications across multiple industries. Dr. Levenstein leads research activities within the LIONS Laboratory, focusing on developing advanced experimental platforms that combine microfluidics with X-ray characterization techniques. His team works at the intersection of engineering, materials science, and applied physics to solve complex problems in materials synthesis and characterization.

David Coeurjollyمشاهده پروفایل

استاد پژوهشی

• Digital Geometry
• Geometry Processing
• Point Sampling
• +۴ 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.

Julie Schollerمشاهده پروفایل

دانشیار

• Statistics
• Data Analysis
• Data Mining
• +۷ مورد دیگر

Julie Scholler serves as an Associate Professor of Mathematics at the University of Tours, working within the Economic Sciences Section of the UFR of Law, Economics and Social Sciences. She holds the position of Head of the first year of the MÉcEn (Master's in Business Economist) program and has been actively teaching since at least 2013 across various levels of the economics curriculum. Her research interests focus on advanced statistical methodologies and data science applications, particularly in exploratory data analysis, factor analysis, unsupervised classification, and machine learning techniques. She specializes in applying these methods using R programming, with expertise in RMarkdown, RShiny, and data visualization techniques. Her work spans from fundamental statistical theory to practical Big Data solutions with R optimization. Analysis of her recent publications reveals a strong emphasis on classification methods, particularly boosting algorithms, random forests, and ensemble methods. Her work demonstrates expertise in prediction error estimation, with applications across various domains including economics, social sciences, and business analytics. She maintains a consistent focus on practical implementation of statistical methods using R, with particular attention to reproducible research practices through RMarkdown. Professor Scholler has developed extensive teaching materials covering statistics, data analysis, and R programming across multiple academic levels. Her courses include Descriptive Statistics, Inferential Statistics, Bayesian Statistics, Dynamical Systems, and specialized topics in Data Mining and Big Data. She appears to be actively involved in student projects and practical applications of statistical methods, as evidenced by student-produced dashboards on topics ranging from world happiness to Covid vaccination data.

Olivier Bouriaudمشاهده پروفایل

استاد

• Forest Ecology
• Climate Change Adaptation
• Plant Ecology
• +۷ مورد دیگر

Olivier Bouriaud is a Professor at Stefan cel Mare University of Suceava in Romania, with dual research appointments at the French National Institute for Agriculture, Food, and Environment (INRAE) and the National Geographic and Forest Information Institute in France. His academic career spans European forest research with significant contributions to understanding forest dynamics, carbon sequestration, and climate change impacts on forest ecosystems. PhD in Ecophysiology from French National Institute for Agriculture, Food, and Environment (INRAE), 1999-2003 Forestry degree from Ecole Nationale du Génie Rural, des Eaux et Forêts, 1996-1999 Bouriaud's research focuses on forest ecology, climate change adaptation, and biodiversity conservation. His work examines how forest ecosystems respond to environmental changes, with particular attention to carbon cycling, tree species diversity, and forest management practices. He investigates the relationship between forest productivity and biodiversity across different spatial scales and environmental gradients. His innovative approaches combine traditional forest inventory methods with advanced remote sensing technologies to monitor forest health and productivity. His recent publications reveal a clear research trajectory focusing on forest productivity in relation to climate change, wood density and carbon stocks, tree species diversity patterns, and advanced forest inventory techniques. Bouriaud employs sophisticated statistical methods including small area estimation and remote sensing data integration to address pressing questions about forest resilience to climate change and anthropogenic disturbances. His work bridges theoretical ecology with practical forest management applications. Bouriaud maintains extensive collaborative networks across European research institutions, evidenced by his numerous co-authored publications with researchers from multiple countries. His work often appears in high-impact journals covering forest science, ecology, and climate change research. As part of Schulze Ernst Detlef's Lab, Bouriaud contributes to research on forest carbon dynamics and ecosystem functioning. His work with the French National Forest Inventory demonstrates his commitment to developing robust methodologies for forest monitoring at national and continental scales.