Damien Stehlé is a Professor at the École Normale Supérieure de Lyon (ENS Lyon), affiliated with the Laboratoire LIP (CNRS, ENSL, INRIA, UCBL, U. Lyon) and a member of the AriC team. He is also a member of the Institut Universitaire de France. His research focuses on cryptography, computational number theory, and computer algebra, particularly lattice-based cryptography and its applications in post-quantum security. He has held editorial roles at the Journal of Cryptology and Designs, Codes and Cryptography, and served on committees for major conferences like ASIACRYPT and CRYPTO. His work has led to advancements in lattice reduction algorithms, cryptographic protocols (e.g., signatures, encryption schemes), and security proofs in the quantum random oracle model. Stehlé has supervised numerous PhD students, including Alice Pellet-Mary and Miruna Rosca, and has contributed to open-source lattice reduction software like fplll. His awards include Best Paper Awards at ASIACRYPT 2021 and 2015. He teaches advanced courses on post-quantum cryptography and cryptanalysis at the Master’s level.
Ronja Sczepanski is an Assistant Professor at the Centre for European Studies and Comparative Politics (CEE) at Sciences Po. Her research explores the intersection of national and European identities, focusing on how political perceptions, social sorting, and behavioral processes drive identity change. Specializes in identity formation through political events like referenda Analyzes neighborhood effects and ethnic diversity on identity dynamics Investigates political party framing of social groups Her recent work examines how differentiated integration impacts EU policy compliance and validates open-source machine translation for text analysis. She teaches courses on European integration at institutions including ETH Zurich and the University of Cologne. Recipient of the PolMeth Europe Best Poster Award (2023) Published in journals like Comparative Political Studies and European Union Politics
Joseph Salmon is a Senior Researcher at Inria (Team Iroko) in Montpellier, working with the Pl@nNet team. He previously served as a Full Professor at Université de Montpellier from 2018 to 2024 and was a Junior member of the Institut Universitaire de France (IUF) from 2021 to 2024. His research focuses on machine learning, optimization, and data science, with applications in citizen science and crowd-sourcing. He leads the doctoral program 'Statistics and Data Science' at Université de Montpellier. Education: Ph.D. in Statistics and Image Processing (2010) from Université Paris Diderot, under supervision of Dominique Picard and Erwan Le Pennec. Earlier roles include Assistant Professor at Telecom Paris (2012-2018), postdoctoral work at Duke University (2011-2012), and visiting positions at UW Statistics (2018) and the Simons Institute (2022). Research interests span isotonic regression, convex optimization algorithms (e.g., PAVA), statistical learning theory, and applications in environmental AI (Pl@ntNet). His work bridges theory and practice, emphasizing scalable algorithms for high-dimensional problems. Key contributions include advancements in PAVA convergence analysis, Slope penalty optimization, and peer-reviewed frameworks for crowdsourced data. Grants include ANR VITE (variable importance/explainability) and CaMeLOt (Cooperative Machine Learning Optimization). Labs/Teams: Active in Inria's Iroko team and collaborates with Pl@ntNet's AI development. Maintains the STATLEARN conference and ML-MTP initiative in Montpellier.
Prof. Maryline Laurent is a Professor at Telecom SudParis, affiliated with the SAMOVAR research laboratory. Her work focuses on cybersecurity, privacy-preserving technologies, blockchain applications, and IoT security. She has contributed to numerous high-impact publications and conferences, addressing challenges in secure healthcare systems, decentralized identity management, and privacy in distributed systems. Her research spans cryptographic protocols, access control mechanisms, and compliance with EU data protection regulations. Key areas of expertise include secure communication protocols for IoT, blockchain-based solutions for healthcare, and privacy-enhancing technologies. She has collaborated on projects such as self-sovereign identity frameworks, anonymized data aggregation, and privacy-preserving smart grid systems. Her work emphasizes practical methodologies for assessing re-identification risks in anonymized datasets and designing secure systems compliant with evolving regulations. Prof. Laurent’s contributions extend to book chapters and edited volumes on digital identity management and wireless network security. She actively participates in international conferences and initiatives, advocating for privacy-by-design principles in intelligent infrastructures.
Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.
Dr. Yi-Ping Fang is an Assistant Professor at the EDF Chair SSEC with a joint appointment at the Industrial Engineering Laboratory, CentraleSupélec, Université Paris-Saclay, France. His research focuses on computational methods for risk, vulnerability, and resilience analysis of critical infrastructures including smart grids, electrified transportation, and interdependent lifeline systems. Risk Analysis Resilience Engineering Optimization Under Uncertainty Game Theory Applications His work applies advanced techniques like distributionally robust optimization, POMDP modeling, and interdependency analysis to enhance infrastructure resilience against climate change, natural hazards, and intentional attacks. Publications demonstrate expertise in hybrid optimization algorithms, stochastic modeling, and network vulnerability assessment. Recent trends include: Smart grid resilience enhancement Uncertainty quantification in infrastructure systems Multi-stage decision modeling Game-theoretic approaches for interdependent networks Integration of deep learning for dynamic system prediction
Thomas Le Goff is an Assistant Professor of Law & Technology at Télécom Paris – Institut Polytechnique de Paris, affiliated with the Interdisciplinary Institute of Innovation (i3) and the Digital, Organization and Society (DTOS) research team. His work bridges legal frameworks and technological advancements, focusing on AI regulation, environmental sustainability, data protection, and cybersecurity. Education: PhD in Private Law, Université Paris Cité Master’s in Law, Université Paris Cité LLM, University of Exeter (UK) Licence and Magistère, Université de Rennes 1 His research explores the intersection of AI and sustainability, emphasizing how legal principles can guide environmentally responsible technology. Key themes include the AI Act’s implications, data center energy demands, and regulatory strategies for balancing digital growth with net-zero targets. His work has been presented at international forums like BILETA and contributes to EU think tanks such as CERRE. Recent publications address AI’s environmental footprint, nuclear energy’s role in powering AI, and legal frameworks for sustainable digital infrastructure. Collaborations with institutions like the Center on Regulation in Europe (CERRE) highlight his focus on comparative analyses of global regulatory practices.
Loris D'Antoni is an Associate Professor in the Department of Computer Science and Engineering at the University of California at San Diego (UCSD) . He is also a Visiting Academic at Amazon Web Services (AWS) . His research focuses on helping people write trustworthy software through techniques in program synthesis, formal verification, and machine learning robustness. Bachelor and Master in Computer Science from University of Torino (2008, 2010) PhD in Computer Science from University of Pennsylvania (2015) His research integrates programming languages , automata theory , and formal methods to ensure software reliability. Recent work explores semantics-guided synthesis and specification-aligned LLMs , with applications in network security, machine learning fairness, and automated code repair. Key trends in his publications include program synthesis , formal verification , and trustworthy AI systems . He has contributed to tools like AutomataTutor and SemGuS , a framework for customizable synthesis problems using constrained Horn clauses. Phillip R. Certain-Gary D. Sandefur Distinguished Faculty Award NSF CAREER Award Microsoft Research Faculty Fellowship Google and Facebook Faculty Awards Best Paper Award at ICDCN 2023 Distinguished Paper Award at SBES 2021 D'Antoni actively contributes to academic community service as a committee member in PLDI , OOPSLA , POPL , and CAV . He leads the Programming Systems Group at UCSD and collaborates with SemGuS research team on synthesis frameworks.
Abderrahim Benslimane is a Full Professor of Computer Science at the University of Avignon, France, where he serves as Vice Dean of International Relations at the UFR STS (Unité de Formation et de Recherche en Sciences et Technologies). He is also Head of the master degree SICOM (Systèmes Informatiques Communicants: réseaux, services et sécurité) program at the university. His extensive academic career spans several decades with significant contributions to computer science, particularly in networking and security domains. Professor Benslimane holds a HDR (Title to supervise researches) from the University of Cergy-Pontoise, a Ph.D. from the University of Franche-Comté, along with M.S. and B.S. degrees in Computer Science from the same institution and the University of Nancy respectively. His research interests primarily focus on distributed computing, networking and communication protocols, with particular emphasis on modeling, describing and implementing secure communication protocols and multimedia applications in heterogeneous network architectures. He combines engineering and theoretical approaches using graphs, distributed algorithms, transition systems, and performance evaluation models. Benslimane's scholarly work demonstrates a strong trend toward addressing security and privacy challenges in emerging technologies. His recent publications focus on cybersecurity applications for wireless sensor networks, Internet of Things, blockchain implementations, UAV communications, and vehicular networks. He has pioneered research in energy attack mitigation, trust management systems, and secure group communications, often employing game theory and novel cryptographic approaches. His work bridges theoretical foundations with practical implementations in next-generation networking technologies. IEEE VTS Distinguished Lecturer (2020-2022) Best Paper award at IEEE ICC 2019 Multiple Prime d'Encadrement et de Recherche Doctorale awards (1998-2021) Prime d'Excellence Scientifique (2011-2015) IEEE Senior Member As an academic leader, Benslimane has served as Editor in Chief of Multimedia Intelligence and Security Inderscience Journal, Area Editor of IEEE Internet of Things Journal, and Associate Editor for multiple prestigious publications including IEEE Transactions on Multimedia and IEEE Wireless Communication Magazine. He has founded and led research centers including the Informatics Research center (CRI) at the French University in Egypt and the Multimedia and networking team (RAM) at the Laboratoire d'Informatique d'Avignon (LIA). His laboratory research focuses on security, communication protocols, graphs and distributed algorithms, with applications in ad hoc networks, sensor networks, vehicular networks, and IoT.
Pierre Marion is a Researcher at INRIA Paris, working within the Sierra research team since September 2025. His work focuses on the theoretical foundations of deep learning and he is beginning to explore applications of AI in mathematics. Marion has established collaborations across multiple institutions including EPFL, Sorbonne Université, and Google DeepMind. His educational background includes: Engineering degree from École polytechnique (2015-2018) with specialization in Applied Mathematics Master's degree from Sorbonne Université (2019-2020) PhD from Sorbonne Université (2020-2023) under the supervision of Gérard Biau and Jean-Philippe Vert Postdoctoral research at EPFL (2024) supervised by Lénaïc Chizat Marion's research interests primarily focus on the theory of deep learning, where he investigates the optimization and statistical properties of various neural network architectures. His work spans from shallow networks to complex generative models, with a particular emphasis on understanding the mathematical foundations that govern deep learning performance. Recently, he has begun exploring applications of AI in mathematical research, aiming to bridge the gap between theoretical machine learning and mathematical discovery. His research often combines rigorous theoretical analysis with practical implications for training deep neural networks. Analysis of Marion's recent publications reveals several key trends in his research. He has made significant contributions to understanding the role of large learning rates in optimization dynamics, demonstrating how they can accelerate convergence in logistic regression and prevent memorization in score-based generative models. His work on attention mechanisms has provided theoretical guarantees for their effectiveness in specific tasks like single-location regression and clustering. Additionally, Marion has extensively studied the connections between residual networks and neural ordinary differential equations , establishing generalization bounds and exploring scaling properties in the large-depth regime. His earlier work included contributions to natural language processing and quasi-Monte Carlo methods, reflecting a broad mathematical foundation that informs his current deep learning research. Marion has received several notable scientific awards: Runner-up PhD Award of AFIA (French Association for Artificial Intelligence) in 2024 Google PhD Fellowship in 2022 Ecole polytechnique Grand Prize of Research Internships in 2018 As an advisor, Marion currently supervises PhD student Yu-Han Wu (since 2024), with whom he has co-authored multiple publications on large learning rates and denoising score matching. Previously, he co-supervised several Master's students including Seorim Park, Yerkin Yesbay, and Nathan Doumèche. Marion has been actively involved in the machine learning community through conference organization (NeurIPS@Paris meetups), session chairing (ICSDS 2022), and extensive reviewing activities. He has served as a reviewer for top journals including JASA and Annals of Statistics, and conferences including NeurIPS and ICLR, where he was recognized as a top reviewer at NeurIPS 2023. Marion is a member of the Sierra research team at INRIA Paris, which focuses on machine learning theory and applications. He has also collaborated with researchers at CREST (Center for Research in Economics and Statistics), as evidenced by his participation in seminars organized by Anna Korba and Karim Lounici. His work often bridges theoretical computer science, statistics, and applied mathematics, reflecting the interdisciplinary nature of modern machine learning research.
Overview Prof. Harris Kyriakou is an Associate Professor and Chair Holder of the Media & Digital Chair at ESSEC Business School. His research focuses on leveraging artificial and collective intelligence to enhance organizational value creation, digital strategy, and data-driven decision-making. He has advised multinational firms like Airbnb, Facebook, and Yelp, and his work is supported by grants from NSF and the Spanish government. Education Ph.D. in Management Sciences (Stevens Institute of Technology, 2016) M.S. in Engineering & Technology Innovation Management (Carnegie Mellon University, 2010) B.Sc. in Digital Systems (University of Piraeus, 2007) Research Focus His research explores intersections between AI/collective intelligence, blockchain, sharing economy regulations, and platform governance. Key themes include data network effects, algorithmic regulation, and digital transformation. Recent work on ChatGPT vs. Google examines AI-driven competitive dynamics in search markets. Recognition Awarded the 2024 Case Centre Triple Award, 2022 Early Career Award (AIS), and multiple best paper awards (AoM, INFORMS). Recognized as a 40-Under-40 MBA Professor by Poets & Quants. Teaching & Leadership Co-leads the 'Algorithmic Governance in Platform Economy' thesis Teaches courses on AI, digital strategy, and IT management at ESSEC and IESE Former Assistant Professor at IESE Business School (2016–2021) Professional Contributions Serves as a European Commission advisor on digitalization, reviewer for top journals (MIS Quarterly, Academy of Management Review), and mentor for doctoral candidates.
Houman BOROUCHAKI is a Professor at the University of Technology of Troyes (UTT), France, with over 20 years of academic leadership. He has served as Head of the Automatic Mesh Generation and Advanced Methods (GAMMA3) project team since 2008 and previously led the Laboratory of Mechanical Systems and Concurrent Engineering (LASMIS) (2005-2007). His work bridges academic research and industrial applications through collaborations with INRIA , French Petroleum Institute (IFPEN) , Dassault Aviation , and others. Research Interests: A pioneer in adaptive meshing , he focuses on finite element methods , geometric modeling , and numerical simulations . His innovations underpin mesh generation algorithms , 3D triangulation software , and industrial applications in metal forming, composite simulation, and subterranean modeling. Scientific Trends: His recent work emphasizes metric-based meshing , high-order geometric validity , and parallel processing for mesh generation , with applications in petroleum reservoirs, aviation surfaces, and nanomaterials. His Google Scholar profile reflects 25+ years of contributions to meshing and simulation. Teaching: With 22 years of experience, he teaches courses on meshing , numerical analysis , geometric modeling , and computer graphics at UTT, covering undergraduate to PhD levels. Labs & Teams: He leads the interdisciplinary GAMMA3 team and has contributed to LASMIS (mechanical engineering), L2n (CNRS-UMR 7076) (nanomaterials), and LIST3N (computer science).
Paul-Eric DOSSOU is a Researcher at ICAM’s Grand Paris Sud campus, specializing in Societal and Technological Transitions of Companies. His work focuses on Industry 5.0, decision-aided systems, logistics optimization, and digital twin applications. He leads projects like Plateforme Life, Urban Logistics, and Healthcare 4.0, aiming to enhance SME efficiency through sustainable digital transformation. Expertise includes AI-driven supply chain management, cybersecurity for legacy systems, and robotic solutions for archaeology. He collaborates with industry partners to bridge theoretical research and practical applications, emphasizing human-centric automation and environmental sustainability. Contact: paul-eric.dossou@icam.fr | Mobile: +33 6 17 81 33 43 Research contributions span over 30 peer-reviewed articles since 2003, addressing topics from energy audits in the nautical industry to multi-agent systems in supply chain optimization.
Jaouad Mourtada is an Assistant Professor in the Department of Statistics at ENSAE/CREST, École Nationale de la Statistique et de l'Administration Économique since September 2020. Previously, he was a postdoctoral researcher at the Laboratory for Computational and Statistical Learning at the University of Genoa (2019-2020). He completed his PhD in Statistics at École Polytechnique under the supervision of Stéphane Gaïffas and Erwan Scornet. His educational background includes a Master's degree in Mathematics with specialization in Probability and Random Models (2016), a Master's degree in Fundamental Mathematics (2015), and a Bachelor's degree in Mathematics from Pierre and Marie Curie University and École Normale Supérieure (2013). Dr. Mourtada's research focuses on the intersection of statistics and learning theory, with particular interest in high-dimensional statistics, online learning, and density estimation. His work explores the complexity of prediction and estimation problems through rigorous theoretical analysis. His research spans statistical learning theory, robust statistics, and the theoretical foundations of machine learning algorithms. His publication record since 2017 demonstrates consistent contributions to top-tier venues in statistics and machine learning, with recent work focusing on universal coding, aggregation methods, robust regression, and the theoretical analysis of kernel methods and random forests. His research shows a progression from online learning and expert aggregation to more complex statistical learning problems involving high-dimensional data and model misspecification. He teaches courses including Statistical Learning Theory for Master 2 Data Science students and Probability Theory at ENSAE. His teaching spans theoretical foundations of machine learning and core probability concepts for advanced statistics students.
Charbel Jose Chiappetta Jabbour is a Professeur Eminent and Head of the Systèmes d’Information, Supply Chain Management & aide à la Décision department at NEOMA Business School in France. He holds a Habilitation à diriger un doctorat from the University of São Paulo, Brazil. His research focuses on green and circular supply chains , Industry 4.0 , and sustainability integration in operations management. He has been included in Clarivate/Web of Science Highly Cited Researchers and received the British Academy of Management’s Best Paper Award (2019). Education & Career: Completed Habilitation at University of São Paulo (Brazil) International experience across Brazil, Japan, Scotland, England, and France Former Associate Editor of Journal of Cleaner Production Research Interests: His work bridges theory and practice in sustainable supply chain design, circular economy frameworks, and the human dimension of operations. He emphasizes integrating Industry 4.0 technologies with sustainability goals, and has pioneered studies on blockchain in supply chain digitalization. Grants & Projects: Principal Investigator/Co-Investigator on projects funded by Innovate UK, Midlands Engine UK, FAPESP Focus areas: Sustainable supply chain resilience, circular economy adoption Labs/Teams: Leads the Systèmes d’Information department and collaborates with global networks on sustainability initiatives.
