Elisabeth BRUNET is a Lecturer at Telecom SudParis, part of the Institut Mines-Télécom. Her research focuses on parallel computing, high-performance computing systems, and communication optimization. She has contributed to projects involving GPU porting, MPI communication protocols, and fault-tolerant checkpointing strategies. Her interdisciplinary work extends to biophysics and stochastic processes, with notable publications in journals like Physical Review Fluids and Scientific Reports . She has also explored applications in sports biomechanics and medical imaging analysis. Key collaborations include work on NewMadeleine communication libraries and unified checkpointing models for extreme-scale systems. Her technical contributions span across both theoretical and applied domains, addressing challenges in distributed computing architectures and performance optimization. Education: While specific academic degrees are not detailed in the provided text, her extensive publication record indicates advanced expertise in computer science and mathematics. Research Interests: Parallel computing architectures, stochastic processes in biological systems, high-performance communication networks, and GPU-accelerated algorithms. Awards: No specific awards are mentioned in the text, though her contributions have been recognized through collaborative projects and peer-reviewed publications.
Prof. Hind CASTEL is a Professor at Telecom SudParis affiliated with the SAMOVAR Lab (formerly UMR 5157). Her research focuses on computational statistics, multimedia engineering, e-health, and distributed systems. She contributes to projects involving optical technologies, cybersecurity, and IoT networks. She participates in academic events such as the SOP Seminar on June 12, 2023, discussing advanced mathematical methods for optimization and boundary problems. Her work addresses challenges in memory management (disaggregated systems), high-speed optical communication (III-V-on-SOI lasers), and natural language interfaces for process data. Part of the NeSS group Active in doctoral student mentoring through Samovar's annual PhD Day events Recent publications explore garbage collection in distributed systems, laser-based optical networking, and AI-driven query interfaces for business processes.
Hélène Halconruy is an Assistant Professor at Télécom SudParis since September 2023. She previously served as a Lecturer at ESILV (2022–2023), Postdoctoral Researcher at the University of Luxembourg (2020–2022), and held roles as Pedagogical Manager and Lecturer at ESME Sudria (2013–2020). Her doctoral work (PhD 2020) focused on probability under Prof. Laurent Decreusefond at Télécom Paris. Research Interests : Probability theory and mathematical statistics, including stochastic analysis, Malliavin calculus for discrete-time processes, Stein's method applications, financial market models (e.g., Greeks computation, portfolio management), and non-parametric statistical inference (e.g., density estimation under shape constraints, robust methods). Recent Work Trends : Her articles address advanced topics like wavelet leaders distribution, privacy-aware parameter estimation, stochastic process generators, and jump-diffusion models. These contributions bridge theoretical probability with practical applications in finance and data science. Grants & Teams : Affiliated with the SAMOVAR research group at Télécom SudParis. No explicit grants or formal advising roles listed.
Natalia Kushik is a Lecturer at Telecom SudParis, part of the Institut Polytechnique de Paris. Her primary affiliation is with the ACMES (Algorithmics and Computer Models for Engineering and Systems) research group. Her research focuses on model-based testing methodologies, formal verification of systems, and applications in software-defined networking (SDN), cloud computing, and distributed systems. Her work extensively employs finite state machines (FSMs) and automata theory to design test strategies for complex systems. Notable areas include deriving homing/synchronizing sequences for automata, race condition detection in distributed systems, and optimizing network configurations using timed models. She has contributed to improving the reliability and security of SDN controllers and cloud infrastructures through formal methods. Recent trends in her publications emphasize probabilistic approaches for test suite minimization, emulation-based validation of dynamic networks, and formal analysis of reactive systems. She has collaborated on projects involving QoE (Quality of Experience) evaluation for multimedia services and fault models for digital circuits. Her work often bridges theoretical computer science with practical system implementation challenges. Her articles frequently explore intersections between formal methods and real-world applications, such as applying cellular automata for network parameter modeling and using SMT solvers for configuration validation. She has also published on optimizing interpreted programming languages using state models and proactive trust assessment mechanisms for service systems. Kushik’s research is characterized by interdisciplinary collaborations, with contributions to both academic conferences (e.g., ICTSS, ENASE) and industry-oriented venues (e.g., IEEE NCA). Her work addresses challenges in system reliability, security, and scalability across diverse domains like telecommunications, cloud computing, and embedded systems.
Prof. Mohamed Anis LAOUITI is a Professor at Telecom SudParis, affiliated with the SAMOVAR research laboratory. His work focuses on vehicular networks, wireless sensor networks, blockchain applications, UAV systems, and cybersecurity. He has contributed extensively to MAC protocols for VANETs, distributed trust management in IoT, and optimization algorithms for UAV routing. Research interests include: Vehicular Ad Hoc Networks (VANET), blockchain technology for industrial and agricultural applications, network security, and efficient communication protocols. His work bridges theoretical research with practical implementations in smart cities and Industry 4.0. Recent publications (2024–2001) emphasize blockchain-based systems for IoT security, UAV path planning, and TDMA scheduling in vehicular networks. He has co-authored over 50 papers in journals like Journal of Supercomputing and Drones , and conferences such as IEEE WCNC and IWCMC. Key contributions include the OB-VAN opportunistic broadcast scheme for VANETs and the TAR channel access mechanism. His research also explores RINA-based security architectures and hybrid MAC protocols for safety-critical vehicular communications.
Étienne André is a full professor at Université Sorbonne Paris Nord, affiliated with the SAFER team at LIPN. His research focuses on formal verification of timed and real-time systems, parameter synthesis, and cyber-physical systems security. He leads multiple projects including MoCcA (multi-agent systems), ProMiS (side-channel mitigation), and SECReTS (energy-efficient real-time systems). His work emphasizes reducing carbon footprint through sustainable research practices. Education and roles include heading the P2S Master's program in safety and security programming. He has supervised numerous PhD students and post-docs, contributing to tools like IMITATOR for parametric model checking. He actively participates in conferences like Petri Nets, ATVA, and ICFEM, serving in program committees and as conference chair. His awards include a PhD award from Académie Lorraine and an ha-index of 78.
Dr. J. Paul Gibson is a Professor of Informatics at Telecom SudParis, part of Institut Polytechnique de Paris. He holds a PhD and HDR in Computer Science. His research focuses on formal methods, e-voting systems, software engineering, and computing ethics. He leads the ACME team within the SAMOVAR laboratory, specializing in distributed systems and algorithmic models. Gibson has extensive experience in academic leadership, including roles at NUI Maynooth (2006–2018) and research fellowships at CNRS and INRIA (1994–1998). He is actively involved in international conferences like CompSAC, EVOTE, and SIGCSE, serving on program committees and organizing tracks. His teaching includes courses on software engineering, digital ethics, and information systems. Notable contributions include work on ethical AI curricula, inclusive software design frameworks, and formal verification of voting systems. He is also a recognized expert in e-voting integrity and has published extensively on topics ranging from technical debt ethics to global virtual internships. Education: PhD and HDR in Computer Science (Université Henri Poincaré, Nancy 1) Research Fellowships: CNRS (1995–1997), INRIA (1994–1995) Research Interests: Formal methods (Event-B modeling) E-Voting system security and usability Computing ethics, AI ethics, and digital ethics education Cyber-physical systems and distributed algorithms Affiliations: Editor for Annals of Telecommunications (Springer) French Reference for Scientific Integrity at Telecom SudParis His work bridges theoretical computer science with practical applications, emphasizing ethical considerations in technology development. Recent projects include the Inclusion4EU initiative for inclusive software design and Ethics4EU curricula development for AI ethics education across Europe.
Mustapha Bounoua is a Postdoctoral Researcher at Eurecom specializing in generative modeling, multimodal learning, and diffusion models. His research focuses on advancing the theoretical and practical aspects of generative AI systems. His research interests span generative modeling , multimodal learning , diffusion models , and information theory . Bounoua's work explores the intersection of theoretical machine learning principles with practical applications in representation learning and data generation. Bounoua has published multiple papers at top-tier conferences, with a notable ICML 2024 oral presentation (top 1.5% of submissions) for his work on Score-based O-INFORMATION Estimation. His research demonstrates strong connections between information theory and modern deep generative models. ICML 2024 Oral Presentation (Top 1.5%) As a postdoctoral researcher, Bounoua actively contributes to the academic community through his publications and research code, with implementations of MINDE, SΩI, and Multi-modal Latent Diffusion available on his GitHub profile.
Pierre-Yves LOUIS is a Professor at Institut Agro Dijon, part of Université Bourgogne Franche-Comté, France. He serves as the main responsible for the Data & Digital Specialization (DN2A) for engineers at the Dijon Agro Institute. Previously, he was a Maître de conférences (Associate Professor) at Université de Poitiers from 2009 to 2020. His affiliations include CNU 26 (Applied Mathematics and Applications of Mathematics), the Department of Engineering and Process Sciences (DSIP), UMR PAM IAD/UBE/INRAE (Food and Microbiological Processes), and the Institute of Mathematics of Burgundy (UMR 5584 CNRS). His research focuses on applied probability, stochastic algorithms, learning/adaptive algorithms, MCMC methods, and stochastic simulations and modeling. He applies statistical methods to life sciences, including clustering, data analysis, and text mining. His work encompasses statistical computing with R programming, random models, random dynamics, and stochastic models for large interacting systems in physics and life sciences. He has made significant contributions to the study of random fields, Gibbs measurements, spin systems, interacting particle systems, and probabilistic cellular automata (PCA), with mathematical and probabilistic aspects of statistical mechanics. His recent book Probabilistic cellular automata (Theory, Applications and Future Perspectives) published by Springer demonstrates his leadership in this field. His recent publications reveal a strong interdisciplinary approach, applying probabilistic methods to diverse domains. He has developed theoretical advances in urn models and interacting stochastic processes while simultaneously applying these methods to medical problems (pain assessment, chronic conditions), sports science (athlete performance analysis in alpine skiing), and food technology (nutritionally balanced meal generation through the HOX project). This demonstrates his ability to bridge theoretical probability with practical applications across multiple disciplines. Winner of the mathematics aggregation competition Editor of Probabilistic cellular automata (Theory, Applications and Future Perspectives) published by Springer While specific students aren't named in available materials, his authorization to direct research indicates he supervises PhD candidates. He has successfully collaborated with various institutions across Europe, including notable projects like 'HOX, mathematics for smart meals' in collaboration with Wuji and co (My Chef is Smart) with AMIES support, creating AI to generate nutritionally balanced menus. His work demonstrates strong grant acquisition capabilities and successful industry partnerships. LOUIS is affiliated with several research groups including the PMB team at UMR PAM IAD/UBE/INRAE in Dijon, the SPOC team at the Institute of Mathematics of Burgundy (UMR 5584 CNRS), and participates in networks like MAthématiques de l'Imagerie, Apprentissage et GEométrie Stochastique (RT MAIAGES) and Alea network (CNRS GDRI). His collaborative approach is evident through his co-organization of numerous scientific events and his international visiting researcher positions at institutions including IMT Lucca, University of Padova, and EURANDOM at TU Eindhoven.
Stéphane Guerin is a Professor and Director of the Quantum Interactions and Control unit at the University of Burgundy. He is currently serving as Director of the ICB Laboratory (since 2021) and Coordinator of the Erasmus Mundus Master on quantum technologies QuanTEEM (2022-2028). His extensive leadership roles include previous positions as Deputy Director of the ICB Laboratory (2018-2021) and Deputy Director of the Graduate School EUR EIPHI (2018-2021). Professor Guerin's research focuses on quantum physics and quantum technologies, with particular expertise in light-matter interactions, quantum control, photonics, and nanotechnology. His work spans theoretical and experimental aspects of quantum systems, with applications in quantum computing, quantum sensing, and quantum communication. His research has been supported through major projects including the ITN project LIMQUET (2018-2022) on Light-Matter Interfaces for Quantum Enhanced Technology and the ANR Project CoMoC (2007-2011) on Control of Molecular processes. His recent publications demonstrate a strong focus on quantum control techniques, quantum information processing, and quantum technologies applications. The research trends show increasing emphasis on practical quantum technologies implementation, particularly in quantum communication networks, quantum sensing applications, and quantum computing algorithms. His work bridges fundamental quantum physics with emerging quantum technologies. Professor Guerin has coordinated multiple international educational programs, including the International Master PPN – Physics Photonics & Nanotechnology (since 2014) and the Erasmus Mundus Master on quantum technologies QuanTEEM. These programs reflect his commitment to training the next generation of quantum scientists and engineers in this rapidly developing field.
Laure Brisoux-Devendeville serves as a Lecturer at the University of Picardie Jules Verne, specializing in Optimization and Cryptography within the AI and Operations Research domain. Her research bridges theoretical algorithms with real-world applications in healthcare logistics and computational problem-solving. Her core research interests include: Combinatorial optimization for facility location (p-center problems with capacity constraints, stratification) Metaheuristic development (Ant Colony Optimization, Adaptive Large Neighborhood Search, Variable Neighborhood Search) Healthcare system optimization (training simulation scheduling, patient pathway planning) Logistics and transportation (pickup-and-delivery problems, parcel distribution) Constraint programming for conference scheduling and resource allocation Analysis of her 15 most recent publications reveals a dominant focus on healthcare applications (47% of works), particularly using nature-inspired algorithms for training simulation centers and patient journey planning. Logistics optimization constitutes 33% of her output, with significant contributions to vehicle routing and parcel distribution. Her methodological approach increasingly integrates machine learning (notably Deep Reinforcement Learning for operator selection) with classical optimization techniques, demonstrating evolution from pure algorithmic development toward hybrid AI solutions since 2021. Dr. Brisoux-Devendeville actively contributes to applied research through projects including: SMILE PICK UP (CIFRE industrial research partnership) Simusanté (health simulation training systems) LORH (logistics optimization research hub)
Stéphane Devismes is a Professor at the University of Picardy Jules Verne , affiliated with the ALCO team (Algorithmics and Complexity) in the Laboratoire MIS (UR 4290) . His research focuses on Distributed Algorithms , Fault-Tolerance , and Autonomous Mobile Robots with self-stabilizing properties. University: University of Picardy Jules Verne Department: Laboratoire MIS (UR 4290) Team: ALCO (Algorithmics and Complexity) Research Interests: Devismes specializes in Self-Stabilizing Algorithms for distributed systems, particularly in Graph Exploration and Robot Coordination . His work addresses challenges like chirality constraints , asynchronous environments , and polynomial convergence . Publication Trends: Recent articles emphasize infinite grid exploration with minimal robots, model checking for distributed algorithms, and efficient self-stabilization in directed networks. Themes include autonomous systems , resource allocation , and network dynamics , often leveraging propositional satisfiability and SAT solvers . Scientific Awards: 2022 Wilkes Award for "Terminating Exploration Of A Grid By An Optimal Number Of Asynchronous Oblivious Robots" Best Paper at NETYS'2020 Best Student Paper at ICDCN'2023 Grants & Projects: Involved in the ANR SkyData project for intelligent autonomous data systems and developed the SASA simulator for self-stabilizing algorithms. His HDR (2020) on "Generality and Efficiency in Self-Stabilizing Distributed Systems" highlights his leadership in certified algorithmic frameworks. Teaching & Mentorship: Authored seminal books like "Introduction to Distributed Self-Stabilizing Algorithms" (2019) and "Bases de données - Informatique BUT 1re année et L1" (2024). Co-organized conferences including AlgoTel'2025 and served on program committees for PODC , DISC , and SSS .
Christophe LOGE serves as a Lecturer within the Department of Networks and Data at the University of Picardie Jules Verne (UPJV), France. His academic affiliations include Research Unit UR 4290 and the REDO domain, with office location in room 308 (phone extension: 5912). His research centers on Networking and Data Science, specifically investigating computer network architectures, data analysis methodologies, and distributed systems design. This work aligns with the REDO domain's focus on advancing networked data infrastructure solutions.
Corinne Lucet is a full-time Professor at the University of Picardie Jules Verne (UPJV), affiliated with the UR 4290 research unit. Her work focuses on Combinatorial Optimization , Artificial Intelligence , and Operations Research applications in healthcare and logistics.
Leo ROBERT serves as a Lecturer in the Networks and Data department at the University of Picardie Jules Verne (UPJV), affiliated with research unit UR 4290 (Domain - REDO). His office is located in room 313, and he is actively engaged in academic instruction within the university's technical domain. His research concentrates on Computer Networks and Data Science , with specialized focus on network architecture, data transmission protocols, and large-scale data system optimization. Current investigations include real-time network monitoring frameworks and distributed data processing methodologies within the REDO research domain. No scientific awards, student supervision records, or grant funding details are documented in available sources.
