Françoise Nau is a Professor of Food Science at the Agro Institute Rennes-Angers , affiliated with the Department of Animal Production, Agrifood, Nutrition (P3AN) and the UMR STLO (Science & Technology of Milk & Eggs) research unit. Her career spans academic, research, and industrial development roles, with a focus on egg and milk protein functionality, bioactive properties, and nutritional quality. Doctor of Engineering in Agronomic Sciences from ENSAR (1993), specializing in 'Animal Production Science and Technology' Accreditation to Direct Research (HDR) prior to becoming a Professor in 2003 Former R&D engineer at Sopharga (Roussel-Uclaf), Nutripharm-Gallia (Danone), and Ovonor Her teaching emphasizes ingredient functionality , physical chemistry of food , and statistical process control , across M1-M2 and L2 levels. Research themes include: Characterization of egg white proteins using proteomics and molecular biochemistry Valorization of egg proteins via chromatographic techniques Exploration of egg white’s antimicrobial activity under thermal treatments Impact of food processing on nutrient digestibility and bioavailability (via in vitro and in vivo models) Statistical analysis of pepsin action on peptidomic datasets Collaborations with UMR STLO’s Microbiology and Statistics teams, and leadership of the ANR-funded OvoNutriAl project (2008–2011) on egg processing and allergenicity. Contact: francoise.nau@institut-agro.fr
Emmanuelle LERAY is a Professor at EHESP (Ecole des Hautes Etudes en Santé Publique) specializing in epidemiology and public health. She serves as Director of U1309 Research on Health Services and Management (RSMS), an Inserm-certified team within the UMR Arènes. Her institutional affiliations include the Research Collective on Disability, Autonomy, Inclusive Society (CoRHASI), the Department of Quantitative Methods in Public Health (METIS), and UMR 6051 ARENES Course. Dr. LERAY completed her thesis in epidemiology at the University of Rennes 1 in 2005 and obtained her HDR in 2017. After training at ISPED in Bordeaux, she worked in Burma as an epidemiologist for Aide Médicale Internationale before serving as University Hospital Assistant in the Epidemiology and Public Health Department of the Rennes University Hospital. She joined EHESP at the end of 2010 where she has established herself as a leading researcher in multiple sclerosis epidemiology. Her research focuses on epidemiology of chronic diseases, particularly multiple sclerosis , examining care pathways, health services, and the impact of social determinants on health outcomes. She investigates developments in the health system, fights against social and territorial inequalities in health, and studies the organization, management, regulation and control of health services. Her work spans social and health policies with particular attention to disability, chronic illness, and health determinants. Dr. LERAY coordinates the EU "Introduction to the methodology of epidemiological studies - part 1" for Master 1 public health at Rennes 1-EHESP and the Joint Public Health Seminar. She teaches epidemiology, biostatistics, and health observation, contributing significantly to public health education through her methodological expertise. Her extensive publication record shows consistent focus on multiple sclerosis epidemiology, with particular attention to socioeconomic determinants of health, treatment access, disability progression, pregnancy outcomes, and healthcare utilization in MS patients. Her research connects clinical aspects of multiple sclerosis with broader health system organization and social determinants, often using large-scale data linkage approaches including the French multiple sclerosis cohort (OFSEP) and national health insurance database. Dr. LERAY is actively involved in developing clinical guidelines, particularly regarding vaccinations and cancer management in multiple sclerosis patients. Her work bridges clinical practice and health services research, aiming to improve care pathways and reduce health inequalities for patients with chronic neurological conditions.
Jeremy L'HOUR is a quantitative research specialist at Capital Fund Management (CFM) in Paris, France, and an affiliated researcher in the Economics department of CREST (Center for Research in Economics and Statistics, Université Paris-Saclay). His work focuses on econometric methods and the application of machine learning to causal inference.
Kevin MICHENEAU is a Teacher-Researcher at CESI School of Engineering, affiliated with the LINEACT research laboratory in Guipavas, France. His work bridges building energy systems and experimental particle physics, focusing on data-driven optimization of smart buildings and dark matter detection. Education: PhD in Subatomic Physics, University of Nantes (2018): "Study of residual electrons in the XENON100 experiment" Master's degree in Research in Subatomic Physics, University of Nantes (2014) Research Focus: Dr. MICHENEAU develops advanced models for building energy performance with emphasis on occupancy behavior impact and smart control systems . His methodology combines sensor fusion and multi-objective optimization to balance energy efficiency with occupant comfort. Previously, he contributed to XENON dark matter experiments through signal reconstruction and background modeling. Publication Evolution: His research trajectory shows a strategic pivot from particle physics (2017-2019) to building energy systems (2024), applying rigorous data analysis techniques across domains. The 2024 MPC optimization study demonstrates transferable methodology from high-precision physics to sustainable engineering. Mentorship: Currently supervising PhD candidate BOURGOIN on "Towards modeling the impact of occupancy on the energy behavior of smart buildings" (2023-2026). Research Ecosystem: Member of the "Engineering and Digital Tools" team within LINEACT, teaching Computer Science, Mechanics, and Physics across preparatory and engineering cycles while contributing to PhD training at University of Nantes.
Louis Duvivier is a Senior Lecturer at the University of Grenoble Alpes since October 2024, affiliated with the Science Department Drôme Ardèche. He is associated with the Coordination, Cooperation & Control of Complex Systems (CO4SYS) team within the Systems Design and Integration Laboratory (LCIS). Former ATER at École Centrale de Lyon (2023–2024) Postdoctoral Fellow at ENS Lyon (2022–2023) ATER at Institute of Financial and Insurance Science (2021–2022) His research focuses on network modeling , graph analysis , and statistical inference , particularly in stochastic block models , community detection , and temporal graph analysis . His recent work explores probabilistic validation techniques and geometric interpretations of network structures. Publications demonstrate expertise in Bayesian inference , model selection , and sensor network estimation . Earlier contributions to distributed estimation and link prediction date back to 2019. He teaches courses in complementary computing , algorithms , numerical analysis , and statistics at both bachelor's and master's levels.
Jean-Luc Danger is a Professor at TELECOM Paris where he currently heads the Digital Electronic Systems Research Group. He is affiliated with the Secure and Safe Hardware (SSH) Research Team within the Information Processing and Communication Laboratory (LTCI). With a career spanning over three decades in academia after 12 years in industrial research at PHILIPS and NOKIA, Professor Danger has established himself as a leading expert in hardware security and cryptographic implementations. Professor Danger received his degree in electrical engineering from SUPELEC in 1981 before embarking on his industrial career. His academic journey began in 1993 when he joined TELECOM Paris, where he has since made significant contributions to the field of hardware security. His educational background in electrical engineering provided the foundation for his later specialization in secure hardware design and analysis. Professor Danger's research primarily focuses on embedded systems security , physically unclonable functions (PUFs) , side-channel attacks and countermeasures , and fault injection techniques . His work bridges the gap between theoretical cryptography and practical hardware implementations, addressing critical security challenges in modern computing systems. His research has evolved from foundational work on cryptographic algorithms to more recent investigations into hardware Trojans, aging effects on security primitives, and automotive security systems. Professor Danger has been particularly influential in developing methodologies for analyzing and protecting against electromagnetic fault injection attacks and side-channel information leakage. His extensive publication record demonstrates a consistent focus on hardware security challenges, with recent work showing increased attention to automotive security systems, machine learning applications for intrusion detection, and reliability issues in security primitives affected by aging and process variations. The trajectory of his research shows a natural progression from pure cryptographic implementations to more holistic security approaches that consider the entire hardware stack and its vulnerabilities. Through his leadership of the Secure and Safe Hardware research team, Professor Danger has fostered a collaborative environment that bridges theoretical security research with practical hardware implementation challenges. His work has contributed significantly to the development of standardized methodologies for evaluating hardware security and has influenced both academic research and industry practices in secure hardware design.
Christine Largouet is an Associate Professor in Computer Science at Institut Agro Rennes-Angers and a member of the IRISA Laboratory's LACODAM team. Her research focuses on Explainable AI, Complex Systems Modelling, and applications in Ecosystem Management and Agroecology. She leads the Computer Science Teaching Unit at Institut Agro Rennes-Angers since 2006 and has held academic roles at the University of New Caledonia (2003-2006) and IRD Nouméa. She holds a HDR (2019) and PhD (2000) from Université de Rennes 1. Education: Habilitation Defense (HDR): "Artificial Intelligence for Decision Support of Dynamical Complex Systems" (Université de Rennes 1, 2019) PhD: "Interpretation of a sequence of remote-sensing images" (Université de Rennes 1, 2000) Research Interests: Her work bridges AI and environmental/agricultural systems, with emphasis on: Explainable AI for decision-making Machine learning in precision agriculture Timed behavioral models & discrete event systems Data-driven approaches for ecosystem management Publications Trends: Recent work emphasizes: AI explainability in user-centric applications Livestock management using sensor-based machine learning Formal methods for system modeling (timed automata) Interdisciplinary applications in agroecology Teaching & Supervision: Supervised 17+ PhD/Master students since 2002 Teaches programming, AI, and data science at multiple academic levels Labs & Teams: Active contributor to: IRISA Laboratory's LACODAM team Collaborations with INRAE and INRIA
François Bachoc is an Assistant Professor at the Toulouse Mathematics Institute and the University Paul Sabatier, where he has held a tenured position since 2015. He is a Junior Member of the Institut universitaire de France (IUF) (2024–2029). His research focuses on Statistics, Machine Learning, and Gaussian Processes , with applications in industrial and interdisciplinary domains. He earned his Ph.D. in Statistics from the CEA and Université Paris VII (2013), followed by a postdoctoral position at the University of Vienna (2013–2015). He completed a Habilitation (HDR) at University Paul Sabatier in 2018. His work emphasizes uncertainty quantification, Bayesian methods, and optimal design of experiments. Bachoc leads several funded projects, including the ANR Project GAP (€205k) and the AI Chair UQPhysAI (€350k). He teaches courses on asymptotic statistics, machine learning, and Gaussian processes at the graduate level. His contributions span theoretical advancements and practical applications in coastal flood modeling, sensitivity analysis, and computational statistics.
Christophe Rosenberger is a full professor of Cybersecurity at ENSICAEN School of Engineering (University of Caen Normandy) and director of the GREYC research lab. He holds a PhD in Computer Science from the University of Rennes 1 (1999) and an HDR (2006). His research focuses on biometrics (keystroke dynamics, soft biometrics), digital forensics, and cybersecurity, with 250+ publications and 28 supervised PhD students. He leads projects like the PEPR Cybersecurity COMPROMIS initiative and chairs international conference tracks. Education: PhD (1999, University of Rennes 1), Master's (1996), and Bachelor's (1994) in Computer Science and Mathematics from the University of Rennes 1. Research interests include behavioral biometrics, phishing detection, and explainable AI. He has developed tools like GREYC-Hashing for biometric template protection and GREYC-Keystroke software for keystroke analysis. Awards include the 2023 highest grade promotion to professor and Asgard laureateships for Norway collaborations. Teaching includes courses on cybersecurity, machine learning, and digital identity at ENSICAEN. He co-supervises 3 ongoing PhD projects on biometric explainability, fairness evaluation, and phishing detection. Active in committees like the CNRS Computer Science Institute and the French-Norwegian MyTIGER program. Media engagements include radio/podcast appearances on biometrics and AI, featured in articles by CNRS, Le Figaro, and others. The GREYC lab, under his leadership, hosts over 200 researchers and collaborates globally on cybersecurity and AI initiatives.
Xuefei LU is an Associate Professor at SKEMA Business School (France), affiliated with the SKEMA Center for Analytics and Management Science and the Digitalization Research Center. Her academic journey includes a Ph.D. in Statistics from Bocconi University (Italy) and an MSc in Analytics from the University of Manchester (UK). Prior roles include Assistant Professorships at SKEMA Business School and the University of Edinburgh Business School, along with a postdoctoral position at Politecnico di Milano (Italy). Her research focuses on Statistical Machine Learning , Uncertainty Quantification , and Big Data Problems , with applications in epidemiological modeling, decision analysis, and industrial systems. She has developed frameworks for identifying critical components in complex systems and pioneered methods like the Cohort Shapley value for fairness in SME financing. Her work has been recognized with prestigious awards, including the 2024 INFORMS Data Mining Best Paper Award (Runner-Up) and multiple Excellence in Reviewing Awards from the European Journal of Operational Research. She actively supervises doctoral students in areas like anomaly detection and pandemic modeling. Xuefei LU collaborates across disciplines, contributing to labs focused on Reliability Engineering , Molten Salt Reactors , and Transportation Systems . Her research bridges theoretical advancements with practical applications in public health, infrastructure resilience, and sustainable energy systems.
Charles Bertucci is a CNRS researcher in Mathematics at the Applied Mathematics department of École polytechnique in Palaiseau, France. He also serves as a part-time teacher at École polytechnique since 2020. Bertucci defended his thesis on December 11, 2018, and his Habilitation à Diriger les Recherches (HDR) in June 2022, granting him accreditation to supervise research. His educational background includes being a former student of École Polytechnique (class of 2012) and Paris-Sorbonne University. His doctoral studies were conducted at Paris-Dauphine University under the supervision of Pierre-Louis Lions. Bertucci's research focuses primarily on mean-field game theory , optimization , and the analysis of partial differential equations . He is particularly interested in identifying stability principles for equations posed in infinite dimensions. His work spans theoretical mathematics with applications in economics, finance, and real-world phenomena such as oil markets, cryptocurrency markets, and telecommunications. His interdisciplinary approach bridges pure mathematics with practical applications in various economic and technological domains. Analysis of his recent publications reveals a strong focus on mean field games theory, with extensions to applications in finance (particularly cryptocurrency markets), optimal transport theory, and connections to PDEs. His work often involves collaborations with leading mathematicians including Pierre-Louis Lions, Jean-Michel Lasry, and others. The research demonstrates both theoretical depth in mathematical analysis and practical relevance to economic modeling. His notable scientific achievements include: Recipient of the prestigious Peccot Course for 2022-2023 at Collège de France Bertucci has been actively involved in academic service, including organizing the workshop "Mean Field Games and Applications" in 2022 with Yves Achdou, Jean-Michel Lasry and Pierre-Louis Lions. His teaching activities include delivering the Peccot Course on "Mean-field games and stochastic control in Wasserstein space" in 2023 at Collège de France, consisting of four lectures between March 31 and April 21, 2023. His research is conducted within the vibrant mathematical community at École polytechnique and through collaborations with researchers at CNRS and other institutions, focusing on advancing the theoretical foundations of mean field games while exploring novel applications across various domains.
Pauline Lafitte is a Professor at CentraleSupélec since 2011 and a member of the Laboratoire de Mathématiques d’Orsay UMR CNRS 8628 since 2025. She leads the Mathematics Department (3rd year curriculum) and co-heads the Mathematics and Data Sciences program at CentraleSupélec. She has held leadership roles including Director of the CentraleSupélec Mathematics Federation (FR3487) since 2017 and Editor-in-Chief of the Société Mathématique de France’s Gazette since 2023. Education: Habilitation à Diriger des Recherches (HDR) from Université Lille 1 (2010), PhD from ENS Lyon (2001), Agrégation de Mathématiques (1998), and DEA in Numerical Analysis (1997). Research focuses on hyperbolic/parabolic PDEs, kinetic theory, numerical analysis, and applications in physics, biology, and engineering. Key areas include asymptotic-preserving schemes, shock profile analysis, and fluid-particle interactions. Over 27 peer-reviewed articles and contributions to ANR projects like TOP-PRED (2021–2024) and STAB (2013–2016). Teaching includes large undergraduate classes (500–800 students) and specialized M2 courses in applied mathematics, covering PDEs, numerical methods, and functional analysis. Service roles include leadership in academic committees (e.g., CNRS, SMAI), editorial boards, and conference organization (e.g., Franco-Hellenic schools, CEMRACS 2019). Extensive jury participation in PhD defenses and academic recruitment committees.
Olivier Cappé is a CNRS Research Director at the Department of Computer Science of École normale supérieure (DI ENS - CNRS/ENS/Inria) and an Adjunct Professor at Université PSL. He is affiliated with the Centre Sciences des Données (CSD) at ENS and serves as a Chair holder and member of the Executive committee of the Pr[AI]rie-PSAI (Paris School of AI) project. Previously, he served as deputy scientific director at INS2I (2017-2023) and headed the Information Processing and Communication Laboratory (LTCI) from 2013 to 2016. Dr. Cappé's research focuses on statistical signal processing and machine learning. His work spans several areas including Bayesian methods, Markov Chain Monte Carlo, online learning, multi-armed bandit models, and differential privacy for machine learning. Starting in speech and audio processing in the 1990s, he contributed to natural language processing applications in the 2000s, and has focused extensively on online learning and bandit algorithms since 2010. His recent work also addresses privacy issues in machine learning systems. Cappé teaches Reinforcement Learning and Differential Privacy for Machine Learning courses in the IASD master program at Université PSL. His publication record shows consistent output across multiple research areas, with recent work focusing on bandit algorithms, online learning, and privacy-preserving machine learning. His research bridges theoretical foundations with practical applications in digital advertising, recommendation systems, and pandemic response analysis. Grand Prize of the EADS Corporate Foundation (Information Sciences) from the French Academy of Sciences (2013) Co-author of 'Tout comprendre (ou presque) sur l'intelligence artificielle' with Claire Marc Dr. Cappé holds a Supélec engineering degree (1990) and a doctorate from ENST (currently Télécom ParisTech, 1993). He joined CNRS as a researcher in 1996 and has maintained a productive research career spanning over 30 years, with significant contributions to both theoretical and applied aspects of statistical signal processing and machine learning.
Kim-Phuc TRAN is an Associate Professor and Research Supervisor at Ecole Nationale Supérieure des Arts et Industries Textiles (ENSAIT), affiliated with the GEMTEX Research Laboratory. He serves as Section CNU 61 and holds leadership roles including Founder member of CybCom (2021-), Member of the Executive Committee of GEMTEX (2020-), and Coordinator of the Cybersecurity axis for GRAISyHM (2020-). His international engagement includes heading the International Chair in Data Science and Explainable Artificial Intelligence at Dong A University & International Research Institute for Artificial Intelligence and Data Science (IAD), Vietnam since 2018. Dr. TRAN's research spans multiple dimensions of Artificial Intelligence with a strong focus on Explainable, Trustworthy, and Transparent AI . His work encompasses Self-Supervised Learning, Anomaly Detection, Federated Learning, and Multimodal Deep Learning. He also investigates Ethical and Human-centered AI through Embedded AI, Wearable AI Devices, and Human-Centered Design to Address Biases. His research extends to Safety and Reliability of AI systems, including Adversarial Machine Learning and Cybersecurity for AI Systems. In Statistical Computing, he focuses on Statistical Process Monitoring and Advanced Control Charts, while his work on Intelligent Decision Support Systems addresses Clinical Decision Support, Supply Chain Optimization, and Predictive Maintenance. His research on Digital Twins spans Healthcare and Smart Manufacturing applications. His publication portfolio shows a strong emphasis on practical AI applications across industries, with particular focus on anomaly detection techniques (appearing in over 40% of his recent publications), industrial applications of AI (35%), and statistical process control methods (25%). His work demonstrates consistent growth in complexity from foundational machine learning approaches to sophisticated multimodal and federated learning systems integrated with domain-specific knowledge. Award for Scientific Excellence (Prime d'Encadrement Doctoral et de Recherche) 2021-2025 from the Ministry of Higher Education, Research and Innovation, France Dr. TRAN has secured substantial research funding as Principal Investigator, including the XAIDS_IChair (500K EUR, 2018-2028), SHSFL (211K EUR, 2020-2024), and EIoTIA (4500 EUR, 2022-2023). He serves as Associate Editor for IEEE Transactions on Intelligent Transportation Systems and Engineering Applications of Artificial Intelligence, demonstrating recognition of his expertise. His leadership extends to coordinating the International semester at ENSAIT and co-creating the International Research Institute for Artificial Intelligence and Data Science. He leads the Human Centered Design Group research team and is actively involved with the GEMTEX research laboratory and Tex-CARE chair. His work bridges academia and industry through multiple collaborative projects with partners like Rosenberger Group, Clear Fashion, and MatchMarket. His current research directions focus on integrating AI with wearable technology, advancing federated learning approaches, and developing trustworthy AI systems for critical applications in healthcare and manufacturing.
Professor Mohammad REIHANEH is an Assistant Professor of Operations Management at IÉSEG School of Management. He holds a Ph.D. in Operations Management from the University of Massachusetts (USA), an MSc in Industrial Engineering from Isfahan University of Technology (Iran), and a BSc in Applied Mathematics from Ferdowsi University of Mashhad (Iran). His research focuses on optimization algorithms, vehicle routing problems, scheduling theory, and logistics systems. He is a member of the LEM research group and has published extensively in top-tier journals such as the European Journal of Operational Research and the Journal of the Operational Research Society. Education: 2018: Ph.D., Operations Management, University of Massachusetts, USA 2012: MSc, Industrial Engineering, Isfahan University of Technology, Iran 2009: BSc, Applied Mathematics, Ferdowsi University of Mashhad, Iran Research Interests: Mohammad’s work emphasizes practical applications of optimization in logistics and healthcare systems. He develops exact algorithms (e.g., branch-and-price) and heuristic methods for complex routing problems, maintenance scheduling, and resource allocation in healthcare settings. His contributions bridge theoretical advancements in operations research with real-world operational challenges in industries like renewable energy (offshore wind farms) and humanitarian logistics. Publications: His recent work addresses cutting-edge challenges such as multi-period offshore wind farm routing, hemodialysis center scheduling, and food bank distribution optimization. He frequently collaborates with international researchers on projects involving vehicle routing, reliability engineering, and control chart design for manufacturing systems. Advising & Grants: No specific student advisees or grant details are listed in the provided materials.
