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نمایش ۷۲۱-۷۳۵ از ۹۸۷ استاد راهنما
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پژوهشگر
Hakim GUEDJOU is a Teacher-Researcher affiliated with CESI School of Engineering and part of the Engineering and Digital Tools research team. His work bridges human-robot interaction and autonomous learning systems with applications in social behavior analysis.
Mohammed Hindawi is a Teacher-Researcher at CESI in Villeurbanne, France, affiliated with the Engineering and Digital Tools research team. His academic foundation includes a Doctorate (2013) and Master of Research (2008) in Computer Science from INSA Lyon, with specializations in knowledge systems, alongside Master's (2006) and Engineering (2005) degrees in Software Engineering from the University of Aleppo, Syria. Research Focus: His work spans machine learning methodologies like dimension reduction and variable selection, applied to digital health and battery technology. Primary domains include: Semi-supervised feature selection algorithms Multimodal sensor data analysis for healthcare AI-driven battery state estimation Frugal AI solutions for resource-constrained environments Educational Activities: Hindawi teaches computer science across CESI's engineering cycle, covering embedded systems, object-oriented programming, databases, AI, and advanced algorithms. He designs curriculum modules and tutors preparatory/engineering students. Research Leadership: As a member of the 'Frugal and Embedded AI' working group, he explores efficient machine learning implementations. He currently supervises PhD candidate Trésor YAO KOFFI's thesis on multimodal machine learning for patient monitoring systems. Publication Trends: Hindawi's recent publications (2023-2024) demonstrate a shift toward applied AI in healthcare and energy systems, building on his foundational work in semi-supervised feature selection (2011-2016). His research consistently addresses real-world constraints through innovative ML architectures.
پژوهشگر
Horchani Leïla is a researcher at CESI , affiliated with the National School of Computer Sciences at the University of Manouba, Tunisia. Her expertise spans Operational Research, Data Science, Urban Mobility, and Advanced Algorithms . PhD in Computer Science, University of Manouba, Tunisia (2013) DEA in Modeling and Management Information Systems, University of Tunis (2002) Master's in Applied Mathematics & Optimization, Tunis El Manar University (1998) Research Interests focus on probabilistic optimization models, sensor network applications, and intelligent urban mobility systems. She has contributed to combinatorial optimization, geolocation technologies, and smart city solutions. Her publications analyze probabilistic algorithms, UAV trajectory planning, and mobility balancing in taxi networks, often integrating Operations Research with Computer Science disciplines. Co-supervised Theses : A. Ghabri (Geolocation in sensor networks, 2017) and S. Sassi Mahfoudh (Probabilistic packing, 2018).
LOUIS Anne is a Research Director at CESI, leading the Industry of the Future application axis. She holds a HDR in electronics (University of Rouen, 2006), a doctorate in High Frequency Electronics and Optoelectronics (University of Limoges, 1998), and a DEA in Electronics (University of Limoges, 1995). Research focuses on decision support tools for industrial systems, AI in predictive maintenance, multimodal transport optimization, and open innovation in SMEs. Supervises 4 PhD students (defenses 2024-2026) and has guided 4 past theses in robotics, bio-waste systems, and network optimization. Active in scientific animation as a member of LINEACT council, Normandie Digital Sciences pole, and doctoral college. Recent publications (2018-2023) span AI-driven maintenance, transport simulations, open innovation, robotic UV treatments, and counterfeit detection. Contact: alouis@cesi.fr
پژوهشگر
Nicolas RAGOT is a Researcher at CESI, actively involved in robotics and digital systems. He works at CESI Caen Campus (14200 Hérouville-Saint-Clair) and CESI Rouen Campus (76800 Saint-Étienne-du-Rouvray), contributing to the Engineering and Digital Tools research team. Education: PhD in Instrumentation and Control of Vision Systems (University of Rouen, 2009) Advanced Studies Diploma (University of Paris XI, 2003) Engineering Diploma (ESIGELEC, 2002) His research focuses on robotics perception , computer vision , and embedded digital electronics , with applications in industrial automation and healthcare. He supervises PhD students working on topics like digital twins in robotics, human-robot collaboration, and pose estimation for texture-less objects. Recent research programs led or participated in: ROJUNACO (Robotics & Augmented Digital Twin for Construction) 2023-2025 FUSION (Framework for Universal Software Integration in Open Robotics) 2023-2027 OASIS (Robotics & AI for Industrial Risk Management) 2022-2024 COLIBRY (Collaborative Robotics for Industry 5.0) 2022-2024
SAHNOUN M'hammed is a Research Director at CESI LINEACT, affiliated with Normandy University. His work spans automation, robotics, and optimization in industrial systems, with a focus on Human-Robot Collaboration , Industry 4.0/5.0 , and Sustainable Logistics . He has advised numerous PhD students across international institutions including Neoma-bs, Université de Batna, and Université Le Havre Normandie. Education: HDR (Normandy University, 2019), Doctorate in Automation (Paul Verlaine University, 2007), DEA Robotics (Pierre and Marie Curie University, 2002) Research Themes: Simulation and optimization of industrial flows, renewable energies, collaborative robotics, and human-centric production systems His research explores multi-agent systems for scheduling optimization, fog computing in smart factories, and predictive maintenance strategies. Recent projects include AntihPert (human behavior modeling in production) and OPTIMAN (human-centered machining workshop optimization). Publications since 2019 demonstrate expertise in cyber-physical systems , transportation task allocation , and bio-waste management . Key journals include IEEE Transactions on Industrial Informatics , Journal of Manufacturing Systems , and Computers & Industrial Engineering . He actively participates in scientific organization, serving as reviewer and chair for conferences like MIM, Codit, and CyMaEn.
پژوهشگر
Nicolas Minesi is a researcher specializing in plasma physics, combustion, and laser spectroscopy, with recent publications focused on nanosecond discharges and plasma-assisted combustion. His work involves advanced diagnostics for aerospace applications, including planetary entry simulations and rocket engine combustion monitoring. Key research areas: Plasma dynamics, Combustion diagnostics, Laser absorption spectroscopy Recent collaborations: Raymond Spearrin, Gabi Daniel Stancu, Christophe O Laux Scientific contributions include: 2024 Student Excellence Award Finalist 15+ publications (2024-2025) on combustion diagnostics, nanosecond discharges, and planetary entry simulations Development of MHz-rate laser absorption techniques for dynamic combustion environments Investigations into ambient air ionization mechanisms via nanosecond discharges Studies of vorticity generation and hydrodynamic effects in pulsed plasma systems
Renaud Seguier is a Professor in the Department of Electronics and Telecommunications at the Institute of Electronics and Telecommunications of Rennes, School of Engineering. His research spans computer vision, signal processing, and human-computer interaction with a focus on facial analysis and speech processing. His primary research interests include computer vision (specializing in micro-expression recognition, 3D facial modeling, and gaze estimation), signal processing (speech emotion recognition and source-filter decomposition), and affective computing (emotion detection from multimodal inputs). Key methodologies involve deep learning architectures like variational autoencoders, GANs, and specialized metric learning techniques. His recent publications demonstrate strong trends in multimodal emotion analysis combining audiovisual inputs, unsupervised learning for human activity recognition , and generative model applications for facial editing and deepfake detection. The work shows increasing sophistication in temporal modeling of facial dynamics and disentangled feature representations. As an academic advisor, he supervises numerous students including Jingting Li, Samir Sadok, and Mouath Aouayeb who frequently appear as first authors on publications. His collaborative network includes Catherine Soladie, Simon Leglaive, and Amine Kacete across multiple institutions. His laboratory work centers on the 3D facial analysis pipeline involving texture reconstruction, motion denoising, and micro-expression spotting systems. Current projects integrate RGB-D sensing with deep learning for unconstrained environments, particularly focusing on health diagnostics from facial cues and real-time gaze estimation.
Nicolas Durand is a Professor at the National School of Civil Aviation (ENAC), specializing in Air Traffic Management through the integration of Metaheuristics , Combinatorial Optimization , and Interval Methods . His work focuses on hybridizing Evolutionary Algorithms with Machine Learning to enhance Conflict Resolution and Trajectory Prediction in aviation systems. His research spans Unmanned Aerial Systems (UAS) , ADS-B Data Analysis , and 3D Collision Avoidance . Recent projects include Behavior Cloning for conformal automation and Median Regression for lateral deconfliction. Collaborations with institutions like ONERA and ISAE-SUPAERO highlight his interdisciplinary approach. Publications reveal a trend toward Explainable AI in ATM, with applications in Dynamic Conflict Resolution , Mass Estimation , and UAS Integration . His work bridges theoretical optimization (e.g., Global Optimization , Interval CP ) with operational needs, emphasizing Controller Expertise and Uncertainty Quantification .
پژوهشگر
David Gianazza is a Researcher at the National School of Civil Aviation (ENAC) , focusing on air traffic management and trajectory prediction. His work combines machine learning and operational research to address challenges in aircraft conflict resolution, airspace configuration, and performance modeling. Specializes in neural networks and metaheuristic optimization Key research areas: air traffic complexity , 3D trajectory planning , and workload modeling His 15 most recent articles (2024-2012) demonstrate expertise in ADS-B data analysis, mass/thrust estimation, and hybrid deterministic-stochastic search algorithms. Collaborations include researchers like Nicolas Durand , Richard Alligier , and Jean-Marc Alliot across institutions in France, Netherlands, and USA. Publications highlight applications of ant colony optimization , A* algorithms , and gradient boosting machines to air traffic problems. His work bridges theoretical computer science and practical ATM solutions , with emphasis on safety and efficiency.
Thierry Klein is a Professor of Statistics at École Nationale de l'Aviation Civile (ENAC) and a member of the Toulouse Institute of Mathematics. His research spans statistical methodology with applications in diverse fields including aviation safety, environmental science, and computational mathematics. Dr. Klein's research focuses on advanced statistical methods, particularly in sensitivity analysis, probability theory, and machine learning. His work on Sobol indices has contributed significantly to variance-based sensitivity analysis, while his research on Wasserstein spaces has advanced the understanding of probability distributions in metric spaces. He has developed innovative methods for Gaussian process regression and has applied statistical techniques to problems in aviation safety, coastal flooding prediction, and paleoenvironmental reconstruction. His recent publications demonstrate a strong focus on sensitivity analysis methods, particularly Sobol indices, with applications across multiple domains. He has also made significant contributions to the theory of Wasserstein spaces and their applications in statistical learning. His work bridges theoretical statistics with practical applications in engineering, environmental science, and aviation. Dr. Klein is currently involved in three major funded projects: the PEPR PDE-AI project (2023-2028) on Partial Differential Equations for Artificial Intelligence, the ANR GATSBII project (2025-2029) as project leader, and the ANR MBAP-P project (2025-2029) as a member. These projects reflect his interdisciplinary approach, combining statistical theory with applications in artificial intelligence, engineering, and environmental science.
پژوهشگر ارشد
François Brucker serves as a Research Fellow in the Computer Science department with affiliation to the READ research laboratory. His academic profile is registered with ORCID identifier 0000-0002-2971-1207. His research interests span multiple domains within Computer Science, with particular emphasis on Artificial Intelligence, Data Analysis, Software Engineering, Machine Learning, and Information Systems. These research areas reflect contemporary challenges and innovations in computational theory and application. No scientific awards or honors are mentioned in the available documentation. His professional activities appear to focus on research within the READ laboratory framework while contributing to computer science education through teaching responsibilities.
Salah Bourennane is a Lecturer and researcher in the Computer Science department at the Fresnel Institute , with a focus on advanced machine learning and multidimensional signal processing techniques. His research interests span: Machine Learning (deep learning, transfer learning, optimization algorithms) Signal Processing (hyperspectral analysis, tensor decomposition, noise reduction) Medical Imaging (diagnostic systems, brain mapping, cancer detection) Computer Vision (face recognition, surveillance systems, 3D verification) Security Applications (mask detection, concealed object classification, steganalysis) Recent publications highlight his work in: Tensor-driven methods for low-resolution face recognition Hybrid multilinear-linear architectures for camera network identification Deep learning frameworks for medical diagnostics (e.g., breast cancer segmentation, Alzheimer's disease mapping) Optimization algorithms (e.g., grey wolf optimizer variants) for sensor systems and image processing Real-time pandemic response technologies like social distance monitoring His work is implemented within the Fresnel Institute research laboratory, specializing in computer vision and data science applications.
Jean-Marc Lasgouttes is a Researcher at Inria Paris working within the Astra project team, a joint research initiative between Inria Paris and Valeo. He also holds a teaching position at INSA Rouen Normandie's Department of Mathematical Engineering, where he has instructed courses including Boosting methods (until 2024), Functional Data Analysis (until 2018), and general Data Analysis (until 2024) for both the Mathematical Engineering department and the Specialized Master's program in Data Science. Dr. Lasgouttes' primary research focuses on probabilistic modeling of large systems using statistical physics tools, with particular emphasis on Intelligent Transportation Systems. His work spans traffic flow modeling, vehicle platooning, urban traffic prediction, and geopositioning systems. He frequently employs Markov Random Fields, statistical physics approaches, and game theory to address complex transportation challenges, bridging theoretical statistical methods with practical applications. His research methodology often involves developing novel algorithms like the ★-IPS family for incremental GMRF estimation. Analysis of his recent publications reveals a consistent trajectory of applying advanced probabilistic models to increasingly sophisticated transportation scenarios. His work demonstrates strong expertise in spatio-temporal modeling, with publications covering car-following dynamics, landmark-based positioning, cooperative ITS, and autonomous vehicle systems. The interdisciplinary nature of his research connects statistical physics, machine learning, and transportation engineering to solve real-world mobility challenges. At INSA Rouen Normandie, Dr. Lasgouttes has developed comprehensive teaching materials for data analysis courses, including practical applications of principal component analysis and correspondence analysis using real-world datasets such as European protein consumption patterns and Titanic passenger data. His educational approach emphasizes hands-on implementation with R programming, reflecting his commitment to practical statistical applications. The Astra project team serves as Dr. Lasgouttes' primary research environment, facilitating collaboration between academic researchers and industry partners to address contemporary transportation challenges. His work has contributed to significant research events including the 2018 workshop on Large Random Networks and Constrained Walks honoring Guy Fayolle's 75th birthday, and the 2012 interdisciplinary workshop on inference associated with the Travesti ANR grant.
Yannick GUINET is a Professor at the University of Lille, affiliated with the Materials and Transformations Unit (CNRS UMR 8207). As a member of the Molecular and Therapeutic Materials research team, he specializes in using Raman spectroscopy to analyze phase transformation mechanisms and stabilization of amorphous states in therapeutic materials, including small molecules and proteins, through excipient interactions. His research focuses on: Low-frequency Raman spectroscopy for metastable state analysis Co-amorphous and deep eutectic solvent formulations Drug stabilization mechanisms using amino acids Solid-state loading techniques for mesoporous carriers Phase transitions in amino acids and pharmaceutical compounds Molecular confinement effects in nanoporous materials Professor Guinet's publications demonstrate consistent focus on pharmaceutical material characterization, with recurring themes including amorphous solid dispersions, polymorphic transformations, Raman spectroscopy methodologies, and nanoconfined drug behavior. His work frequently appears in high-impact journals covering physical chemistry, pharmaceutical sciences, and materials engineering.
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