Sébastien Thomassey is an Associate Professor at the National School of Arts and Textile Industries (ENSAIT), specializing in AI-driven solutions for textile engineering. His research focuses on optimizing fashion supply chains through machine learning applications in production management, sales forecasting, and sustainable manufacturing. He leads the Research Human Centered Design Group and coordinates multiple EU-funded projects including H2020's FBD-BModels and Erasmus+'s Digital Fashion initiative. His core research integrates: Supply chain optimization using AI forecasting models Textile manufacturing process automation Sustainable production lifecycle management Human-machine interaction in industrial settings Recent publications demonstrate strong emphasis on AI applications in textile anomaly detection (2024), digital twin technology (2023), and reinforcement learning for process optimization (2021). Awards include: Top 25 Most Cited Article in International Journal of Information Management (2021) He coordinates €5M+ in collaborative grants including: Fashion Trends 4.0 (Région Hauts-de-France) SMDTex Erasmus Mundus doctorate program H2020 FBD-BModels textile supply chain project Leads the Human Centered Design Group developing sensor-based textile tracking systems and industrial AI solutions.
Elham MOHSENZADEH is an Associate Professor and Research Supervisor at JUNIA - HEI (Higher School of Engineering) in France, specializing in electrospinning technology and nanofiber applications. She leads the Multifunctional Textiles & Processes Group and holds Section CNU 61 affiliation. Her research spans multiple interdisciplinary fields including materials science, textile engineering, and biomedical applications. Her research interests focus on developing nanofibers, microfibers, and nanocomposites for diverse applications such as sensors, energy harvesting, filtration systems, and biomedical devices. She has pioneered work in electrospun membranes for air filter clogging detection, gas sensors for NO/NO 2 detection, and thermal management textiles. Her expertise includes electrospinning techniques, nanofiber characterization, and functional textile development. Analysis of her recent publications reveals strong trends in environmental monitoring applications, particularly gas sensing for medical diagnostics (asthma detection through NO monitoring) and energy-efficient building technologies. Her work bridges fundamental materials science with practical engineering solutions for air quality monitoring, personal thermal management, and medical diagnostics. Best Presentation Award, Development of an Electrospun Composite as Substitutive Diaphragmatic Membrane (2017) As a research supervisor, she leads multiple significant projects including ANR-PRCE POCOMA on polymer membranes for thermal comfort, ANR-PRCE SAFIRS on intelligent air filtration, and the Interreg MOTION project developing orthosis for children with neurological disorders. Her work demonstrates strong industry and international academic collaboration across France, Belgium, and the UK. Her laboratory work focuses on the Multifunctional Textiles & Processes Group at JUNIA, where she develops innovative electrospun membranes and nanofiber-based sensors. Current research directions include improving gas sensor sensitivity at lower temperatures, optimizing nanofiber structures for specific applications, and developing dual-mode temperature regulation textiles.
Emmanuel Grolleau is a Full Professor at ISAE-ENSMA (Institut Supérieur de l'Aéronautique et de l'Espace - École Nationale Supérieure de Mécanique et d'Aérotechique) specializing in real-time systems. He is affiliated with the LIAS laboratory (Laboratoire d'Ingénierie des Applications de la Sensorique) where he leads the Real Time Team. His work bridges theoretical scheduling principles with practical embedded system implementations across multiple domains. Professor Grolleau's primary research interests include: Real-Time Scheduling: uniprocessor, multiprocessor, and distributed scheduling with practical considerations like transactions and precedence constraints Model-Based Systems Engineering (MBSE): developing bridges between UML-MARTE and AADL to real-time scheduling tools Unmanned Aerial Vehicles (UAVs): autopilot architecture design and optimization Energy Systems: co-heading the LabCom ANR Laboratoire d'Insertion des Énergies Nouvelles et d'Optimisation des Réseaux (LIENOR) His publication record demonstrates a clear progression from fundamental scheduling theory to applied work spanning avionics, drone technology, and power systems. Recent work shows strong focus on UAV autopilot architectures, model-based frameworks for real-time systems, and energy management in power distribution networks. Professor Grolleau serves on multiple prestigious program committees including Real-Time Networks & Systems (RTNS) since 2012, ACM/SIGAPP Symposium On Applied Computing (SAC) since 2015, DETECT since 2018, and DroneSE in 2023. He has led significant research projects such as PIA CORAC Panda and FUI WARUNA, which developed the Time4Sys pivot meta-model to connect theoretical scheduling with practical implementation. His collaborative work extends across multiple institutions and industries, with publications spanning real-time scheduling theory, UAV systems, energy management, and avionic architectures. The consistent thread through his work is the practical application of real-time scheduling principles to solve complex engineering problems in safety-critical systems.
Patrick Girard is a Full Professor in Data Engineering at ISAE-ENSMA (Institut Supérieur de l'Aéronautique et de l'Espace - École Nationale Supérieure de Mécanique et d'Aérotechnique) in France. He is a member of the Data Engineering Team within the LIAS (Laboratoire d'Ingénierie des Applications de la Sensorique) laboratory, with dual affiliations at ISAE-ENSMA in Chasseneuil and ENSIP in Poitiers. Professor Girard's research focuses on Human-Computer Interaction , with particular expertise in task modeling , interactive systems design , and programming by demonstration techniques. His work spans theoretical foundations to practical applications in model-based development, user interface design methodologies, and educational technology for programming. He has made significant contributions to task model validation, simulation techniques, and the application of these principles in complex systems engineering, particularly in aerospace contexts. His publication history demonstrates a consistent evolution from foundational work on programming by demonstration and task-oriented architectures in the 1990s to more recent applications in complex systems engineering and avionics. A distinctive trend in his work is the integration of formal methods with practical HCI approaches, creating robust frameworks for developing user-centered interactive systems with safety-critical applications. Professor Girard has mentored numerous researchers throughout his career, with frequent collaborations showing sustained relationships with scholars like Loé Sanou, Nicolas Guibert, and Thomas Lachaume. His research has involved multiple interdisciplinary projects, often connecting academic research with industrial applications, particularly in the aerospace sector. Within the LIAS laboratory, Professor Girard works in the Data Engineering Team alongside colleagues in Automatic Control and Real Time research groups. The laboratory maintains strong industry partnerships, especially with aerospace companies, enabling practical implementation of theoretical research in real-world engineering contexts. His work bridges computer science theory with practical engineering applications, particularly in the French aerospace ecosystem.
Jean-Denis Gabano serves as an Associate Professor specializing in Automatic Control and Systems at the University of Poitiers. He maintains dual affiliations with the Laboratory of Automated Systems Engineering (LIAS) at both ENSIP (École Nationale Supérieure d'Ingénieurs de Poitiers) and ISAE-ENSMA campuses, reflecting his integrated role across these engineering institutions. His research program bridges theoretical control systems with practical industrial applications, particularly in energy storage technologies. Dr. Gabano's research expertise centers on fractional-order calculus applications for modeling complex physical phenomena. His work primarily focuses on electrochemical systems, particularly battery impedance characterization across time and frequency domains, and thermal system identification. He has developed sophisticated identification algorithms that enable precise modeling of diffusion processes in batteries and heat transfer in thermal systems. His methodological innovations in fractional-order modeling have significant implications for improving battery management systems and thermal control in industrial applications. Analysis of his publication record reveals a clear research trajectory evolving from fundamental thermal system identification toward increasingly sophisticated battery impedance modeling. His most recent work (2023-2024) demonstrates advanced applications of fractional calculus to lithium-ion battery characterization, addressing critical challenges in impedance spectroscopy and parameter estimation. The interdisciplinary nature of his research connects control theory with electrochemistry and materials science, yielding practical methodologies for energy storage system optimization. Dr. Gabano leads research within the Automatic Control team at LIAS laboratory, focusing on developing advanced system identification methods using fractional calculus. His research group maintains strong collaborative ties with industry partners, particularly in the energy sector, to translate theoretical developments into practical engineering solutions. Current projects emphasize time-domain identification techniques as alternatives to traditional frequency-domain approaches, potentially reducing testing time for battery characterization while maintaining accuracy.
Driss Mehdi is a Full Professor in the Department of Automatic Control and Systems at the University of Poitiers, affiliated with the LIAS-ENSIP laboratory. His research spans control theory, renewable energy systems, and industrial process optimization, with a strong focus on stability analysis and applied control solutions. Research Interests: Prof. Mehdi's work integrates theoretical rigor with practical applications. Key areas include: Advanced stability analysis using Linear Matrix Inequalities (LMI) for multidimensional systems Design of robust/fuzzy controllers for renewable energy (photovoltaic/wind hybrid systems) Optimization of industrial processes (e.g., wastewater biofiltration) His recent publications (2018–2025) emphasize renewable energy control and environmental applications, reflecting a consistent trend toward sustainable technology solutions. Articles frequently utilize LMI-based methodologies and address real-world challenges in energy management and process control. Prof. Mehdi collaborates extensively on industrial projects, including wastewater treatment optimization and solar energy systems, though specific grant/lab details are not provided in the source text.
Guillaume Mercere is a Full Professor in Automatic Control and Systems at ENSIP (Ecole Nationale Supérieure d'Ingénieurs de Poitiers), University of Poitiers. He maintains dual affiliations with Laboratory LIAS at both ENSIP in Poitiers and ISAE-ENSMA in Chasseneuil, conducting research in system identification and control theory. Professor Mercere teaches automatic control and signal processing at the Master's level, with additional expertise in numerical optimization, machine learning, and time series analysis. His teaching materials are available upon request, reflecting his commitment to educational transparency. Research Focus: Model learning, system identification, estimation theory, state space modeling, gray box modeling, linear parameter varying (LPV) systems, linear fractional representation (LFR), and subspace-based methods Application Areas: Electrical engineering, aeronautics, heat transfer, flexible/cable-driven manipulators, vehicle tire/road interactions, and image processing Analysis of his recent publications reveals a strong emphasis on recursive estimation methods (particularly total least squares), theoretical developments in LPV system representations, predictive control methodologies, and noise covariance estimation for Kalman filtering. His work bridges theoretical advances in identification methodologies with practical applications across multiple engineering domains, demonstrating both depth and breadth in his research program. Professor Mercere leads the Automatic Control Team at Laboratory LIAS, where he collaborates with researchers on theoretical and applied projects. His research group focuses on developing identification methodologies with practical implementation in real-world engineering systems, maintaining an active publication record through 2025 that demonstrates ongoing contributions to the field of system identification and control engineering.
Slim TNANI is an Associate Professor with HDR (Habilitation à Diriger des Recherches) in Automatic Control and Systems at the University of Poitiers' Institute of Technology (IUT). He maintains dual affiliations with the Laboratory of Engineering Applications of Dynamics and Systems (LIAS) at both the ENSIP (École Nationale Supérieure d'Ingénieurs de Poitiers) and ISAE-ENSMA (Institut Supérieur de l'Aéronautique et de l'Espace) campuses in France. His research spans electrical engineering with particular focus on control systems, power electronics, and renewable energy integration. Key interests include fault diagnosis in electrical machines, synchronous generator control, energy storage systems, and power network stability. His work demonstrates strong theoretical foundations combined with practical industrial applications, particularly in wind energy systems and hybrid power generation. Analysis of his publication record shows consistent research trajectory with increasing focus on renewable energy integration since 2015. His work bridges traditional power systems engineering with modern control theory, showing particular expertise in parameter estimation techniques for fault detection and advanced control strategies for energy storage integration. Recent publications emphasize optimization of hybrid renewable systems and stability analysis for power networks with high renewable penetration. As an HDR-qualified researcher, TNANI supervises doctoral candidates and leads research projects within the LIAS laboratory. His work has resulted in significant industrial collaborations, particularly in power electronics applications for renewable energy systems. The laboratory environment supports both theoretical research and practical implementation through specialized teams in automatic control, data engineering, and real-time systems. His research group maintains strong connections with international partners, evidenced by collaborations with Tunisian researchers on grid interconnection studies and renewable energy projects. The LIAS laboratory provides infrastructure for experimental validation of control systems in power electronics and renewable energy applications.
José Rouillard is a Lecturer-Researcher in Computer Science (section 27) at Université de Lille, affiliated with the CRIStAL laboratory (Centre de Recherche en Informatique, Signal et Automatique de Lille) where he works in the Brain-Computer Interface (BCI) research team. His academic position combines teaching responsibilities with active research in human-computer interaction, particularly focusing on novel interface technologies and assistive applications. Dr. Rouillard's primary research interests center around Brain-Computer Interfaces with particular expertise in Steady-State Somatosensory-Evoked Potentials (SSSEP). His work explores multimodal interaction techniques, virtual reality integration with BCI systems, and applications for individuals with motor disabilities such as Duchenne muscular dystrophy. Recent publications (2023-2024) demonstrate continued innovation in BCI technology, including Wizard of Oz studies on user perception, advanced SSSEP recording techniques with cEEGrid systems, and multimodal cobot interaction frameworks using MQTT protocol. His research bridges theoretical neuroscience with practical applications for assistive technologies. As an educator, Dr. Rouillard has created one of the most comprehensive App Inventor 2 teaching resources available, with 76 detailed projects covering the full spectrum of mobile application development. His course materials progress from basic applications to advanced implementations involving Bluetooth communication with Arduino, Firebase database usage, and AI APIs including OpenAI's ChatGPT and DALL-E. His teaching spans multiple academic years, with documented student projects from 2013 through 2023 across various Master's programs including MMD IAE, MIAGE, and e-Services. Supervised PhD thesis: "Hybrid brain-machine interface to overcome disability caused by Duchenne muscular dystrophy" (Alban Dupres, 2016) Supervised PhD thesis: "Filtrage somesthésique pour des interfaces cerveau-ordinateur utilisant des stimulations vibro-tactiles" (Jimmy Petit, 2022) Dr. Rouillard maintains a strong educational presence through his extensive online resources, including YouTube video tutorials, NextCloud file sharing for course materials, and detailed project guides. His student projects demonstrate practical applications of mobile development across diverse domains including health monitoring, gaming, social networking, and educational tools. The breadth of his educational impact is evident in the hundreds of student applications documented from 2013-2023, showcasing his commitment to practical, hands-on learning in computer science education.
Ibrahim Dellal is a Lecturer in Data Engineering at ISAE-ENSMA, France, affiliated with the LIAS laboratory. His research focuses on semantic web technologies, particularly query processing over uncertain RDF knowledge bases. He addresses critical problems like empty answers and overabundant query results through cooperative approaches that explain and refine unsuccessful queries, contributing significantly to knowledge representation and database systems. Education: PhD in Computer Science from ISAE-ENSMA (2019) on managing large knowledge bases with incomplete and uncertain data Research Interests: Data Engineering and Semantic Web Technologies Uncertainty Handling in Knowledge Bases Cooperative Query Processing Query Result Explanation and Refinement RDF Systems and Knowledge Representation His 2017-2020 publications demonstrate consistent focus on cooperative query answering for uncertain RDF knowledge bases, specifically tackling the empty answer problem through explanation generation and the overabundant answers problem via query refinement. These works advance semantic web and database research by bridging user interaction with automated query optimization under data uncertainty. He is an active member of the Data Engineering team at LIAS laboratory, which operates across ISAE-ENSMA's Chasseneuil campus and collaborates with teams in Automatic Control and Real Time systems for interdisciplinary engineering research.
Yassine Ouhammou is an Associate Professor in computer science at École Nationale Supérieure de Mécanique et d'Aérotechnique (ENSMA), where he is a member of the "Real-Time and Embedded Systems" research team at LIAS laboratory. His work focuses on critical real-time embedded systems with applications in avionics, drones, and control command systems. His research interests include: Software architectures for critical real-time embedded systems Design and analysis of critical real-time systems regarding their temporal performances Model-based design using domain specific languages (MoSaRT, AADL, Capella, Time4Sys) Real-time scheduling and dimensioning Knowledge repositories for expertise capitalization, reuse and reproducibility Collaborative engineering for complex systems design Model-driven engineering and formal methods Dr. Ouhammou's publication record shows consistent contributions to real-time systems, embedded architectures, and model-based approaches. His recent work demonstrates increasing emphasis on drone technology and avionic applications, with numerous collaborations on autopilot design, scheduling optimization, and verification methodologies. His research bridges theoretical computer science with practical aerospace engineering challenges, addressing safety-critical aspects of embedded systems. Professional service includes: PC Member of MEDES 2020, INISTA 2020, WIMS, SADASC 2020 PC Chair of DETECT 2019 General Co-Chair of RTNS 2018 PC Member of multiple international conferences since 2017 Dr. Ouhammou collaborates extensively with researchers in the field of real-time systems, particularly with Emmanuel Grolleau and other members of the LIAS laboratory. His work often involves interdisciplinary teams addressing complex challenges in aerospace and embedded systems engineering, with practical applications in drone technology and avionic systems.
