جست‌وجوی استاد و پژوهشگر

Matthieu EXBRAYATمشاهده پروفایل

مدرس

• Machine Learning
• Data Analysis
• Data Visualization
• +۷ مورد دیگر

Matthieu EXBRAYAT is a Lecturer in Computer Science at the University of Orleans, affiliated with the Fundamental Computer Science Laboratory of Orléans (LIFO). He holds significant administrative responsibilities including Vice President for Digital and Educational Innovation at the University of Orleans and previously served as Co-head of the IT department (2019-2021) and Head of the IMIS Computer Science Masters (2015-2021). His research spans multiple domains with a primary focus on Machine Learning applications in data analysis and visualization, time series analysis, and computer vision applications in archaeology. His work demonstrates strong interdisciplinary connections between computer science and archaeological documentation, particularly in ceramic sherd classification using deep learning techniques. Earlier in his career, he contributed significantly to high-performance databases and probabilistic relational learning, especially Markov logic networks. Analysis of his publication history reveals an evolving research trajectory: beginning with database systems and parallel join algorithms in the early 2000s, progressing through Markov logic networks and clustering algorithms in the 2010s, and more recently focusing on machine learning applications in archaeological imaging and programming language analysis. His recent publications show a clear shift toward practical applications of machine learning in diverse domains while maintaining theoretical rigor. Throughout his career, EXBRAYAT has maintained extensive collaborative research networks, with Lionel Martin appearing as a co-author on 20 publications, followed by Guillaume Cleuziou (14), Jacques-Henri Sublemontier (9), and Quang-Thang Dinh (7). These collaborations span multiple research domains and demonstrate his ability to work across disciplinary boundaries. As an educator, he teaches distributed information systems, programming languages, databases, AI/data mining, and geographic information systems. He has also been actively involved in scientific mediation, delivering numerous public lectures on artificial intelligence topics since 2017, including conferences such as "Living in harmony with AIs" (2023) and "Artificial Intelligence: My new toaster is an AI!" (2019). His administrative contributions are substantial, including roles as Communications Correspondent for the Computer Science Disciplinary Center, Facilitator of the DataCenters ComUE Centre Val de Loire reflection group, and Vice President of Digital Resources at the Leonardo da Vinci Confederal University. He also served as LIFO website webmaster for over a decade (2002-2014) and was responsible for the STIC degree program (now IT degree) from 2004-2008.

• Signal Processing
• Image Matching
• Computer Vision
• +۵ مورد دیگر

Guillaume Bourmaud is an Associate Professor at the University of Bordeaux , affiliated with the Bordeaux Institute of Technology and the Signal and Image Processing department. He is a member of the MOTIVE research team within the IMS Bordeaux laboratory. Research Interests : His work focuses on Signal processing and image matching algorithms Computer vision and Transformer-based architectures Statistical learning for geospatial and atmospheric data SLAM (Simultaneous Localization and Mapping) with probabilistic models Publications Trends : Recent work spans computer vision (2024), geospatial navigation (2023), and atmospheric measurement techniques (2022), with emphasis on kriging, Gaussian processes, and feature correspondence evaluation.

• Computer Science
• Signal and Image Processing
• Remote Sensing
• +۵ مورد دیگر

Christian Germain is a Professor of Computer Science at Bordeaux Sciences Agro, an engineering school specializing in agronomy. He focuses on information technologies and their applications to agriculture and environmental science, conducting research in image analysis at the IMS laboratory. His work spans remote sensing, embedded agricultural imaging, and digital tool development for vineyards. Key Roles: Co-holder of the AgroTIC business chair (29 corporate sponsors), Scientific Director of DigiLab (open platform for wine-growing experiments). Research Themes: Remote sensing, agricultural imaging systems, covariance pooling in machine learning, and texture analysis for material science. His recent publications highlight collaborations with industry and academic partners, emphasizing applications in vineyard health monitoring, carbon composite modeling, and vine disease detection. Germain’s team utilizes CNNs, Gaussian mixture models, and SAR imaging techniques to advance agricultural and materials engineering. He has contributed to international conferences and journals, integrating computational methods with real-world agricultural challenges, including proximal sensing for crop management and 3D microstructure simulation.

• Applied Probability
• Financial Mathematics
• Numerical Analysis
• +۴ مورد دیگر

Jean-François CHASSAGNEUX is a Full Professor in Finance at Université Paris, holding a permanent position at CREST (Center for Research in Economics and Statistics). His academic career focuses on the intersection of probability theory, numerical analysis, and financial mathematics, with particular expertise in backward stochastic differential equations and their applications to financial modeling. His research interests span Applied Probability, Financial Mathematics, Numerical Analysis, Stochastic Analysis, Backward Stochastic Differential Equations (BSDE), Large Population Stochastic Control, Non-linear pricing methods, and Markets with imperfections . His work bridges theoretical mathematics with practical financial applications, particularly in derivative pricing, risk management, and sustainable finance. CHASSAGNEUX has developed novel numerical methods for solving complex financial models, including probabilistic approaches to non-linear partial differential equations and advanced techniques for hedging problems. His publication record demonstrates consistent contributions to top journals in mathematics and finance, with a recent focus on sustainable finance applications including carbon markets and impact investing. His collaborative work with researchers like D. Crisan, G. Pagès, and A. Richou has significantly advanced the field of probabilistic numerical methods for financial engineering. As an educator, CHASSAGNEUX teaches advanced courses in Financial Mathematics (APM_4FI02_AE) and Numerical Methods in Financial Engineering (APM_5FI10_AE), training the next generation of quantitative finance professionals. His teaching combines theoretical foundations with practical computational techniques essential for modern financial engineering.

Cédric HERZETمشاهده پروفایل

پژوهشگر ارشد

• Statistical Learning Theory
• Optimization
• Operational Research
• +۳ مورد دیگر

Cédric HERZET is a Permanent Member of CREST at INRIA Rennes, focusing on statistical learning theory, optimization, and inverse problems. His work bridges theoretical analysis with practical algorithm development. Primary affiliation: INRIA Rennes (French National Institute for Research in Digital Science) Research Interests: Statistical Learning Theory, Optimization algorithms, Operational Research, Inverse Problems. His work particularly addresses sparse approximation, compressed sensing, and iterative thresholding methods. Key Contributions: Development of Bayesian pursuit algorithms, geometric analysis of subspace clustering with outliers, and analysis of state evolution in dense graph message passing. His theoretical work has direct applications in signal/image processing and machine learning. Software: Created MATLAB implementations of sparse approximation algorithms and Bernoulli-Gaussian Lab toolbox for Bayesian pursuit simulation.

Fabien Lotteمشاهده پروفایل

عضو هیئت علمی

• Brain-Computer Interfaces
• Machine Learning
• Neuroscience
• +۳ مورد دیگر

Fabien Lotte is a Senior Researcher (Directeur de Recherche DR2) at Inria, affiliated with the University of Bordeaux. He leads the Potioc team, focusing on Brain-Computer Interfaces (BCI) and related technologies. Research Interests: Brain-Computer Interfaces (BCI) for motor imagery and neurofeedback Machine learning on Riemannian manifolds for EEG analysis Reproducibility in neural engineering research Passive BCI for cognitive/affective state estimation Neuroergonomics and adaptive systems Scientific Contributions: His recent work explores Riemannian geometry for BCI, including feature fusion, visualization techniques, and performance prediction via median nerve stimulation. He has developed open-source tools like BioPyC and contributed to large datasets for BCI reproducibility. Scientific Awards: 2023 : Nature Mentorship Award (Mid-Career Category) 2023 : Lovelace-Babbage Prize from French Academy of Science Advising and Collaborations: Supervised multiple PhD students (Léa Pillette, Jelena Mladenovic, David Trocellier) and postdocs. Leads ANR projects (STIM-BCI, BCI4IA) and ERC-funded initiatives (BrainConquest, SPEARS).

• Speech Processing
• Artificial Neural Networks
• Binaural Sound Localization
• +۷ مورد دیگر

Dr. Jean-Luc Zarader is a Professor at Sorbonne University's Faculty of Engineering , serving as Deputy Director of the UFR of Engineering. Based at ISIR (Institute of Intelligent Systems and Robotics) in Paris, his research focuses on speech processing , neural networks , and binaural sound localization with applications in humanoid robotics and aircraft diagnostics. Key research areas: Speech coding, Nonlinear signal processing, Humanoid auditory systems, Fault diagnosis Active collaborations with teams: ACIDE, MLIA, IRIS Research Trends (2017-1996): 2017-2012: Whale bioacoustics, Aircraft diagnostics, Binaural localization 2007-2000: Speaker verification, Predictive coding, Neural network applications 1999-1996: Doppler lidar analysis, Speech compression His scientific contributions span multiple disciplines, including: Neural network optimization for signal processing Acoustic feature extraction Humanoid robot perception systems Aerospace fault detection Bioacoustic pattern recognition

• Computer Vision
• Autonomous Vehicles
• Deep Learning
• +۴ مورد دیگر

Franck Davoine is a CNRS Senior Researcher (equivalent to Full Professor) at the LIRIS Laboratory (UMR 5205) affiliated with INSA Lyon, where he coordinates the Imagine research team and contributes to environmental transition initiatives. His career spans 25+ years across France, Sweden, and China, including roles at Heudiasyc Lab (UTC), LIAMA Beijing, and Peking University. His research integrates computer vision , autonomous systems , and deep learning , with emphasis on: Sensor fusion for LiDAR and event cameras Real-time perception in autonomous vehicles Uncertainty modeling using Dempster-Shafer theory Self-supervised depth estimation He has supervised 14 PhD students and 21+ interns, with current projects funded by ANR (e.g., ANNAPOLIS for autonomous navigation). Awards include: Most Influential Paper over the Decade (2023) Grenoble INP PhD Thesis Prize (1995) Recent publications focus on neural networks for calibration, depth estimation, and event-based vision, reflecting his lab's work on efficient embedded systems for robotics.

Marie-Christine ROUSSETمشاهده پروفایل

استاد

• Knowledge Representation
• Information Integration
• Ontology-based data access
• +۷ مورد دیگر

Marie-Christine ROUSSET is a Professor of Computer Science at the University of Grenoble Alpes (UGA) in France, where she is a member of the LIG (Laboratoire d'Informatique de Grenoble) in the SLIDE group. Previously affiliated with Paris-Saclay (LRI), she has established herself as a leading researcher in Knowledge Representation and Information Integration. She holds the distinguished position of Senior member of the Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) and serves as co-responsible for the chair Explainable and Responsible AI within MIAI Grenoble Alpes. Her research focuses on ontology-based data access, logic-based mediation between distributed data sources, query rewriting using views, data linkage, and distributed reasoning for the Semantic Web. She skillfully combines artificial intelligence and database techniques to address complex information integration challenges, with applications spanning biomedical informatics, educational technology, and trustworthy AI. Her work demonstrates consistent innovation from foundational research to practical implementations, as evidenced by her co-authorship of the book 'Web Data Management' published by Cambridge University Press. Professor ROUSSET's recent publications (2019-2022) reveal a growing emphasis on data privacy, RDF graph anonymization, and interactive ontology engineering, while maintaining her strong contributions to semantic web technologies and knowledge representation. Her research shows increasing attention to trustworthy AI concerns, aligning with her leadership roles in relevant projects. Scientific Recognition Senior member of Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) Junior member of Institut Universitaire de France (IUF) from 1997 to 2002 Chevalier de l'Ordre National du Merite (July 11, 2011) EurAI Fellow (nominated ECCAI Fellow in 2005) Best Paper Award at AAAI'96 for 'Verification of Knowledge Bases based on Containment Checking' Professor ROUSSET maintains an active role in the scientific community through editorial work and organizational leadership. She serves on the Editorial Board of Communications of the ACM (CACM) and has held significant roles including PC chair of EGC 2019, Workshops co-Chair of WWW 2018, and Area Chair of IJCAI 2017. Her consistent service on program committees of major international conferences demonstrates her standing in the field. Her laboratory, the SLIDE group within LIG, focuses on semantic web technologies, knowledge representation, and data integration. The group maintains strong connections with the international research community and participates in collaborative projects addressing cutting-edge challenges in artificial intelligence and data management, with particular emphasis on trustworthy and explainable AI systems.

Heike Steinمشاهده پروفایل

پژوهشگر ارشد

• Computational Neuroscience
• Data-driven Modeling
• Dynamical Systems
• +۳ مورد دیگر

Heike Stein is a CNRS Research Fellow at the Institute of Intelligent Systems and Robotics (ISIR), a joint unit of Sorbonne University and the French National Centre for Scientific Research (CNRS), where she has worked since 2025. She holds a PhD from the University of Barcelona and completed postdoctoral training at École Normale Supérieure (ENS). Her research in computational neuroscience focuses on reverse-engineering brain function through data-driven modeling. Key areas include: Analyzing neuronal population dynamics underlying perception and cognition Developing machine learning methods for neural data interpretation Modeling animal behavior via video-tracking time-series analysis Creating theoretical frameworks for large-scale neuroscience datasets At ISIR, she contributes to interdisciplinary research in intelligent systems, leveraging collaborations to address challenges in systems neuroscience.

Chevaleyre Yannمشاهده پروفایل

استاد

• Artificial Intelligence
• Multi-Agent Systems
• Computational Social Choice
• +۳ مورد دیگر

Chevaleyre Yann is a Professor of Computer Science at LAMSADE, Paris-Dauphine PSL University, where he has been working since 2017. Previously, he served as Professor of Computer Science at Paris-Nord University from 2009 to 2017 and as Director of the "data science" team at the LIPN laboratory. His academic journey includes a Lecturer position at LAMSADE, Paris-Dauphine University from 2002 to 2009, a Habilitation thesis at Paris-Dauphine University in 2009, and a Doctorate at Pierre and Marie Curie University under the supervision of Jean-Daniel Zucker from 1998 to 2001. Professor Chevaleyre's research spans multiple areas within artificial intelligence and computer science, with particular expertise in multi-agent systems, computational social choice, and machine learning. His work on preference modeling, voting theory, and resource allocation has significantly contributed to the field of computational social choice. More recently, his research has expanded into adversarial machine learning, robust classification, and generative models, reflecting the evolving landscape of AI research. His publication record demonstrates consistent contributions across multiple domains, with recent work focusing on precision-recall optimization in generative models, the role of randomization in adversarial robustness, and novel approaches to graphical bilinear bandits. His research shows a clear trajectory from foundational work in multi-agent systems toward more contemporary challenges in machine learning security and evaluation. Professor Chevaleyre has maintained active collaborations with researchers across France and internationally, evidenced by his extensive co-authorship network. His work bridges theoretical computer science with practical applications in areas ranging from robotics to bioinformatics.

Pascal Bruniauxمشاهده پروفایل

استاد

• Textile Engineering
• 3D Garment Simulation
• Human Body Modeling
• +۵ مورد دیگر

Pascal Bruniaux is a Full Professor and Research Supervisor at the École Nationale Supérieure des Arts et Industries Textiles (ENSAIT) in Roubaix, France, where he leads research in textile engineering and human-centered design. His work focuses on the intersection of textile science, digital technologies, and human anatomy, with particular emphasis on creating innovative solutions for garment design and customization. Professor Bruniaux's research spans four major themes: (1) Modeling, simulation and multi-scale perception of textile materials; (2) Modeling of the Human/Clothing/Environment interface; (3) Analysis and classification of typical and atypical human morphologies; and (4) Development of personalized co-creation processes integrating 3D virtual prototyping, human perception, and knowledge. His work bridges the gap between traditional textile manufacturing and cutting-edge digital technologies, with applications ranging from everyday apparel to specialized medical garments and protective clothing. His recent publications demonstrate a strong trend toward integrating artificial intelligence and machine learning with 3D garment design and virtual prototyping. The research spans disciplines including textile engineering, computer science, human factors engineering, and sustainable fashion, with particular emphasis on improving garment fit, comfort, and customization through digital technologies. His work addresses critical challenges in the fashion industry, including reducing waste through virtual prototyping and improving accessibility for people with atypical body morphologies. Professor Bruniaux has received significant recognition for his contributions to the field: 2016 Gratitude Diploma from Gheorghe Asachi Technical University of Iasi for supporting and promoting their interests PEDR Award for PhD students and research supervision, received annually from the French Ministry of National Education since 2021 Throughout his career, Professor Bruniaux has supervised numerous PhD students and led multiple significant research projects funded by various programs including FUI, Erasmus Mundus, and European H2020. His research has been supported by collaborations with institutions across Europe and Asia, demonstrating the international impact of his work. He has also contributed to the development of innovative teaching methods in textile and apparel design, integrating digital tools into the curriculum. As a key member of the GEMTEX laboratory at ENSAIT, Professor Bruniaux leads the Human Centered Design Group, which focuses on developing technologies that place human needs at the center of textile and apparel innovation. The group's work has applications in diverse areas including adaptive clothing for people with physical disabilities, personalized sportswear, and advanced protective garments.

Loïc Adamمشاهده پروفایل

دانشیار

• Incremental Preference Elicitation
• Multicriteria Decision Making
• Decision Theory under Risk and Uncertainty
• +۸ مورد دیگر

Loïc Adam serves as an Associate Professor in Data Engineering at ISAE-ENSMA since 2024, concurrently holding a lecturer position in Digital Information. Previously, he was an ATER at Université de Technologie de Compiègne (2023-2024) where he taught operational research and data analysis courses. He completed his PhD in Computer Science at UTC in 2023 and currently serves on ENSMA's improvement council and scientific expertise commission (2025-2027). His research spans decision theory under risk and uncertainty, with core expertise in incremental preference elicitation, multicriteria decision-making, and uncertainty modeling through possibility theory and belief functions. He actively investigates information fusion, machine learning applications, and game-theoretic approaches to preference learning, focusing on resolving inconsistent preferences and developing robust decision support frameworks under severe uncertainty. Adam's publication record (2020-2024) reveals consistent contributions at the intersection of artificial intelligence and decision science, particularly in preference modeling and uncertainty quantification. His work demonstrates methodological innovation in applying possibility theory to inconsistency resolution and developing imprecise probabilistic models for label ranking, with publications spanning top venues in AI, fuzzy systems, and decision theory. Scientific Awards: No major awards or fellowships were documented in the source material. He currently supervises M2 intern Aicha AIT HAMMOUCH on auto-detection of erroneous models through personalized data elicitation in uncertain environments. As a member of LIAS Laboratory's Data Engineering team, he collaborates across ENSIP (University of Poitiers) and ISAE-ENSMA sites, contributing to interdisciplinary projects that bridge theoretical decision science with real-world engineering applications in control systems and real-time data processing.

Brice Chardinمشاهده پروفایل

دانشیار

• Data Engineering
• Clustering Algorithms
• Query Relaxation
• +۵ مورد دیگر

Brice Chardin is an Associate Professor in Data Engineering at ISAE-ENSMA since 2013, affiliated with the LIAS (Laboratoire d'Ingénierie des Applications de la Connaissance et des Systèmes) Data and Model Engineering team. His work bridges academic research and industrial applications, focusing on data management solutions for critical systems. His research spans clustering algorithms under dissimilarity constraints , RDF query relaxation for explaining empty/overabundant results, pattern mining through the RQL language, and energy data management . Key projects include Chronos (a NoSQL system for industrial sensor data) and collaborations with energy companies SRD and Nexeya for predictive consumption analysis. Recent publications (2021-2024) emphasize constrained clustering techniques and cooperative query processing for RDF knowledge bases, revealing a strong trend toward practical solutions for industrial data challenges. His work integrates machine learning with database theory to address real-world data imperfections. PhD in Computer Science from INSA Lyon (2011) Postdoctoral position at LIRIS (2012-2013) on ANR DAG project Specialized in industrial data management since 2011 EDF collaboration Chardin actively supervises academic projects including drone simulation with Ardupilot and Smart Data mining initiatives. His industrial partnerships focus on energy sector applications, particularly predictive analysis for electricity distribution and storage systems. Current work involves developing clustering algorithms with error bounds and query relaxation frameworks for semantic web technologies.