David Janin is an Associate Professor in Computer Science at Bordeaux INP , specifically within the ENSEIRB-MATMECA school. He leads the PoSET research project, exploring algebraic models for heterogeneous interactive temporal media systems. Research Affiliations : CNRS INS2I , INRIA Bordeaux Sud-Ouest , Idex Bordeaux , LaBRI (CNRS UMR 5800). Research Focus : His work bridges inverse semigroup theory and functional programming to develop algebraic frameworks for synchronizing diverse temporal media (sound, animation, video). This includes T-calculus in Haskell and Octopus for 3D animation. Technical Contributions : Key developments include models for overlapping tiles , birooted tree languages , and causal function semantics in real-time systems. Contact : janin@labri.fr
Christophe Hurter is a Researcher at the National School of Civil Aviation , specializing in Artificial Intelligence , Data Visualization , and Human-Computer Interaction . His work bridges Aviation Technology , Neuroscience , and Extended Reality (XR) , focusing on enhancing human performance through AI-driven systems. Affiliation: National School of Civil Aviation (Faculty Member, AI axis) Research Interests include: Explainable AI for aviation systems Neurophysiological monitoring using thermal imaging Eye tracking and cognitive processes in memory retrieval Graph and trajectory visualization for air traffic control Augmented/Virtual Reality interfaces for remote collaboration Event-based vision systems for spiking neural networks Recent article trends highlight his expertise in machine learning for medical diagnostics , XR applications in aviation , and visual analytics for human factors research . His work often addresses real-world challenges in air traffic control, piloting training, and biomedical data interpretation.
Juan-Manuel Torres Moreno is an Associate Professor (Maître de Conférences HDR HC) at the University of Avignon (UAPV), where he conducts research in Natural Language Processing at the Laboratoire Informatique d'Avignon (LIA). His academic position includes the HDR (Habilitation à Diriger des Recherches), a post-doctoral qualification in France that enables supervision of PhD students. His primary research interests focus on Natural Language Processing, with particular emphasis on automatic text summarization, sentence generation, and phrase compression algorithms. His work spans both theoretical and applied aspects of NLP, incorporating machine learning techniques and artificial intelligence approaches. His research has significant applications in multilingual processing, text mining, and information extraction systems. Torres Moreno's publication record demonstrates a consistent trajectory in advancing text summarization techniques, with recent work exploring cross-lingual approaches, multimedia content processing, and deep learning applications. His research often bridges the gap between theoretical linguistic concepts and practical implementation, with publications spanning from fundamental NLP algorithms to applied systems for video summarization, speech processing, and multilingual document analysis. He actively collaborates with researchers across multiple institutions including École Polytechnique de Montréal (with 50 joint publications), Laboratoire Informatique d'Avignon (83 publications), and Universidad Nacional Autónoma de México. His work appears in reputable journals such as Computer Speech and Language, Data and Knowledge Engineering, and Pattern Recognition Letters. Within the Laboratoire Informatique d'Avignon, Torres Moreno contributes to the Language Processing research theme, working with colleagues on projects related to multilingual information access, opinion mining, and text analysis. His research group has participated in several evaluation campaigns including DEFT (Défi Fouille de Textes) challenges, focusing on information retrieval and sentiment analysis tasks.
Laure Soulier is a HDR (Habilitation à Diriger des Recherches) Lecturer at Sorbonne University within the MLIA team at the ISIR (Institute for Intelligent Systems and Robotics) laboratory. Her research focuses on the design of language models for Information Retrieval (IR) and Natural Language Processing (NLP) applications, including data-to-text generation , search-oriented conversational systems , language models for robotics , and continual learning with domain adaptation . Recent publications highlight her work on cross-encoders, co-speech gesture generation, and latent space metrics. She supervises an ANR-funded postdoctoral researcher position (SCAI/BnF program) and collaborates on projects involving multimodal techniques, user interaction analysis, and document vectorization. Her scientific contributions include best paper awards at CORIA 2021, SCAI@EMNLP 2019, CORIA 2015, and AIRS 2013. Laure Soulier’s work spans collaborative information retrieval models, entity ranking in heterogeneous networks, and neural approaches for knowledge-based IR. She has contributed to evaluation frameworks for LLMs in IR and co-speech gesture generation, with applications in e-commerce search, medical information retrieval, and social media-based collaboration. Her research integrates user roles, document representations, and reinforcement learning techniques.
Professor Michel MAROT is affiliated with Telecom SudParis, where he holds the position of Professor in the NeSS department. His research focuses on networking, wireless communication systems, performance evaluation, smart grids, and machine learning applications in telecommunications. MAROT has contributed to advancements in vehicular networks (VANETs/V2V), IoT architectures (LoRaWAN), and energy-efficient protocols for wireless sensor networks (WSNs). He has led studies on coalition formation in smart grids, reinforcement learning for policy optimization, and network resource management in 6G systems. Key research areas include optimizing network performance through cross-layer design, improving QoS in mobile and vehicular environments, and deploying intelligent reflecting surfaces (IRS) for 6G. His work frequently addresses challenges in mobility management, collision avoidance, and energy efficiency in distributed systems. MAROT has co-authored influential papers in journals like Neurocomputing, IEEE Transactions on Smart Grid, and IEEE Open Journal of the Communications Society. His research group (SAMOVAR/NeSS) develops practical solutions for real-world networks, including cold chain monitoring systems using sensor networks and DNS-based optimizations for SCHC protocols. MAROT’s recent work explores machine learning embedded in LPWAN sensors and mobility-aware resource allocation in LoRaWAN.
Derya Malak is an Assistant Professor at EURECOM in the Communication Systems Department, with an adjunct position at Rensselaer Polytechnic Institute (RPI) in the Department of Electrical, Computer, and Systems Engineering. She leads the ERC-funded SENSIBILITÉ project, focusing on distributed computing of nonlinear functions over communication networks. Education: Ph.D. in Electrical and Computer Engineering, University of Texas at Austin, 2017 M.S. in Telecommunication Engineering, Koç University, 2013 B.S. in Electrical and Electronics Engineering, Middle East Technical University (METU), 2010 Minor in Physics, METU Her research bridges information theory, coding, and distributed computing, with emphasis on reducing communication costs through structured data encoding. She explores distributed matrix multiplication, function computation over networks, caching optimization, and secure computation, leveraging tools from graph theory and stochastic modeling. Her work has significant implications for content delivery networks, distributed machine learning, and efficient data transmission. Her recent publications focus on distributed computation using structured coding schemes and multi-server frameworks, demonstrating substantial gains over classical approaches. These works are unified by a theme of optimizing computation under limited bandwidth and correlated data structures. Scientific Awards: ERC Starting Grant (2022) Best Paper Award, WiOpt 2023 Best Paper Award, WiOpt 2022 Harold N. Trevett Award (student advisee) She advises PhD students, including Aiden Chen, and has secured major grants from the European Research Council (ERC), Agence Nationale de la Recherche (ANR), National Science Foundation (NSF), Rensselaer-IBM AI Research Collaboration, and DARPA Dispersive Computing. She teaches Information Theory and Advanced Wireless Communications at EURECOM, and previously taught Digital Communications and Signal Processing at RPI. She leads the FASS student seminar series and organizes workshops such as the Workshop on Distributed Computing, Optimization & Learning. Her lab focuses on theoretical and practical aspects of distributed computation, with strong ties to computer science and information theory.
Paul Boniol is a researcher at Inria, affiliated with the VALDA project-team—a collaboration between Inria Paris, École Normale Supérieure, and CNRS. His work focuses on time series analytics, anomaly detection, and machine learning applications. Ph.D. in Computer Science and Applied Mathematics (University of Paris, EDF R&D) Visiting Ph.D. at University of Chicago Education: Grenoble INP ENSIMAG Engineering School Research interests span: Unsupervised anomaly detection in large time series Time series management systems Machine learning for predictive maintenance Graph-based time series analysis Explainable AI for temporal data Recent publications emphasize advancements in: Weakly supervised anomaly localization Graph embedding techniques Model selection frameworks Interactive visualization tools Smart meter data analysis Scientific recognition: Paul Caseau Thesis Prize 2022 Lambdamu Congress Research-Industry Prize 2022 BDA & INFORSID Ph.D. Prizes 2022
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.
Guillaume DUCOURNAU is a **Professor** at the **Institute of Electronics, Microelectronics and Nanotechnologies (IEMN)**, part of the **University of Lille**, France. His research focuses on **Terahertz (THz) wireless communications**, including the development of THz sources, detectors (e.g., photomixers, Schottky diodes), and instrumentation for millimeter-wave and THz characterization. He leads projects such as **GRAPH-X**, **TIMES (6G SNS)**, and coordinates ANR-funded programs like **FUNTERA** (THz converters) and **SYSTERA** (systems beyond 90 GHz for future networks). His work spans European collaborations (e.g., **ThoR H2020**) and national initiatives, including the COM'TONIQ project for 300 GHz THz communications. **Research Interests**: THz communication systems, photonic integrated circuits, semiconductor device characterization (e.g., GaN Schottky diodes), on-wafer measurement techniques up to 1.1 THz, and hybrid packaging strategies for silicon ICs. His contributions include advancements in frequency stability, high-data-rate links (e.g., 327 Gbps), and topological photonics for 6G applications. **Key Projects**: STREP ROOTHz (2010-2013), TERASONIC (ANR/DFG), France 2030 PEPR programs. **Grants**: ANR INFRA 2013, ANR PEPR, Marie-Curie TERAOPTICS network. **Labs & Groups**: Active in the **PHOTONIQUE THz** and **EPIPHY** groups at IEMN, focusing on device characterization, silicon technologies, and collaborative research across 12+ labs in SYSTERA.
ZGHAL Mourad is a Researcher-Lecturer at CESI LINEACT, holding an HDR (2008) from Sup’Com, Carthage University and a PhD in Electrical Engineering (2000) from University Tunis Manar. He specializes in Optimization, IoT, Sensors, and Smart Healthy Cities , with a strong focus on Photonic Crystal Fibers and Nonlinear Optics . Education: HDR in Engineering (2008), Sup’Com, Carthage University PhD in Electrical Engineering (2000), University Tunis Manar Engineering Degree in Telecommunications (1995), Sup’Com, Carthage University Research Interests: Mourad’s work bridges IoT sensor networks with optical communication systems . He pioneers mid-infrared supercontinuum generation in chalcogenide fibers and explores optical mode multiplexing for high-speed communications. His recent work integrates federated learning for intrusion detection in smart grids and optimizes photovoltaic energy systems for building decarbonization . Publications Trends: His 2023–2025 work emphasizes AI-driven energy management , cybersecurity for IoT , and federated learning frameworks . Earlier contributions (2016–2019) focused on nonlinear optical effects in photonic fibers and high-bit-rate networks . Awards: Elected Vice-Präsident of the International Commission for Optics Fellow Optica (ex OSA) and SPIE Associate scientist at ICTP (UNESCO Category 1 Institute) Advising & Grants: Supervised 9 PhD students (e.g., Z. MONLA’s work on BIM/VR in building maintenance). Active member of the LINEACT Scientific Council and CTI Commission des Titres d’Ingénieurs. Labs & Teams: Leads the Engineering and Numerical Tools research team at CESI LINEACT. Collaborates with IMT Télécom SudParis as an Adjunct Professor.
Aurélien Francillon is a full professor in the Networking and Security Department at EURECOM, France. His research focuses on embedded systems security, software security, and telecom fraud. He holds a PhD from INRIA Grenoble and previously worked as a postdoc at ETH Zurich's System Security Group. Education & Experience: PhD in Computer Science, INRIA Grenoble Postdoctoral Researcher, ETH Zurich (2013-2015) Current Position: Full Professor at EURECOM Research Interests: His work addresses critical challenges in securing embedded systems, firmware analysis, side-channel attacks, and telecom fraud. He pioneers tools like Avatar2 for firmware analysis and explores vulnerabilities in Bluetooth, TLS, and CI/CD systems. His methodologies emphasize reproducibility (BEERR framework) and automation (LibAFL QEMU). Labs & Collaborations: Collaborates with industry partners like Intel Labs, SAP Security Research, and Cisco Talos. His lab leads open-source projects including SymCC, LibAFL, and X-Ray-TLS. Advises over 20 PhD/Master students and postdocs, many now in academia and industry. Awards & Recognition: (No explicit awards listed, but extensive contributions to top-tier security conferences and open-source frameworks.) Grants & Funding: Secures funding through European Research Council grants and industry partnerships for projects on firmware security, IoT vulnerabilities, and CI/CD pipeline integrity.
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.
Patrick Bas serves as Research Director and Thematic Group Facilitator for the Data Intelligence Group (DatInG) at the University of Lille, France, where he leads the Signal and Images team within the CRISTAL laboratory. His office is located at ESPRIT in Lille's Scientific City, and he holds membership on the institution's Scientific Council. Bas earned his Electrical Engineering degree (1997) and Ph.D. in Signal and Image Processing (2000) from the Institut National Polytechnique de Grenoble. His academic journey includes postdoctoral work at Université Catholique de Louvain, CNRS research at Gipsa-Lab (2001-2009), and a visiting position at Helsinki University of Technology (2005-2008). His primary research centers on steganography, steganalysis, and digital watermarking, with significant contributions to image manipulation detection and neural network applications in multimedia security. He has organized major initiatives including the BOSS steganalysis contest (2010) and served as co-coordinator for the Ecrypt European NoE's watermarking virtual lab (2004-2008). While his Google Scholar profile indicates recent publications in graph theory and combinatorics (2021-2025), these appear inconsistent with his documented research focus on multimedia security as evidenced by his thesis supervision and professional activities. Bas has supervised seven doctoral theses including Rony Abecidan's work on robust image manipulation detection (2024) and Solène Bernard's research on neural network steganography (2021). He has held editorial roles for the Eurasip Journal on Information Security (since 2011) and IEEE Transactions on Information Forensics and Security (2013-2016), and co-organized the International Workshop on Information Hiding (IH07).
Giovanna Maria Dimitri is an Assistant Professor Tenure Track in Artificial Intelligence at Universitá degli Studi di Milano (Statale), with additional affiliations at the ICE, University of Cambridge, and the Dipartimento di Ingegneria dell'Informazione e Scienze Matematiche (DIISM) at the University of Siena. She earned her PhD in Artificial Intelligence from the University of Cambridge under Prof. Pietro Liò, focusing on multilayer network methodologies for brain data analysis. She holds an MPhil in Advanced Computer Science from Cambridge with distinction and completed her Master’s and Bachelor’s in Computer and Automation Engineering at the University of Siena, both with top honors. PhD in Artificial Intelligence – University of Cambridge, UK MPhil in Advanced Computer Science – University of Cambridge, UK (Distinction) Master’s & Bachelor’s in Computer and Automation Engineering – University of Siena, Italy (110/110 cum laude) Her research spans a broad spectrum of artificial intelligence, including foundational models, deep learning, brain data modeling, and applications in healthcare, environmental science, and sustainability. She is particularly known for her work on GAN detection, emotional image datasets, climate change impact modeling, and AI for Sustainable Development Goals. Her interdisciplinary approach integrates computer science with neuroscience, public health, and social impact. Her recent publications reflect a strong trend in applying AI to real-world problems such as healthcare diagnostics (e.g., Brugada Syndrome detection), environmental monitoring (air quality, climate change on agriculture), and ethical AI (CO2 emissions of ML models). She also contributes to digital humanities and science communication, indicating a commitment to societal engagement and interdisciplinary collaboration. She has received the competitive Ai-Net Fellows Scholarship from DAAD in 2023, enabling collaboration with Prof. Gemma Roig’s lab. She is an Associate Editor for Neurocomputing (Elsevier) and was elected Associate Editor of IEEE Transactions on Technology and Society in May 2024. Dimitri has extensive teaching experience, lecturing Business Intelligence at the University of Siena and serving as a Guest Lecturer in Data Science at the University of Cambridge’s Institute of Continuing Education. She has supervised numerous students and has a publication record of nearly 60 peer-reviewed papers. She is also active in science communication, having been interviewed by Italian media and appearing on Rai Radio 1. She is a life member of Clare Hall College, University of Cambridge, and continues to contribute to academic and public discourse on AI through seminars, workshops, and editorial leadership.
Marie-Dominique Van Damme is a Lecturer at the French National School of Geographic Sciences (ENSG), IGN's School of Geomatics, and is in charge of research and studies in the MEIG research team within the LASTIG research laboratory. She has been an IGN Teacher-Researcher at ENSG-Géomatique since August 2019, and from March 2025 will return to full research activities at LASTIG while continuing to teach programming with GIS, modeling, databases, and integration of heterogeneous data at ENSG. Dr. Van Damme earned her Computer Engineering degree with an information systems option from the National Conservatory of Arts and Crafts in 2010. Her professional experience includes: August 2019 – present: IGN Teacher-Researcher at ENSG-Géomatique May 2012 – July 2019: Research engineer at LASTIG, MEIG team May 2005 – April 2012: Project Manager at the National Forest Inventory June 2000 – April 2005: Web developer at Risc Group Her research focuses on semantic integration of heterogeneous geographic data, data matching, and the application of these techniques to mountain rescue scenarios. She has led and participated in several significant research projects including: CHOUCAS Project (ANR, 2017-2022): Multi-source semantic integration of landmark objects for mountain rescue LANDSENSE (H2020, 2016-2020): Crowd and community sourcing for land use monitoring UrCLIM (ERA4CS, 2017-2020): Retrieving land cover data for urban climate studies IntForOut (ANR, 2024-2027): Current project on trajectory analysis Dr. Van Damme has developed several software tools to support her research, including Tracklib (a Python library for GPS trajectory manipulation) and MultiCriteriaMatching (a Java library for multi-criteria data matching based on Belief Theory). Her recent publications show a strong focus on spatial data quality, landmark ontology development, mountain rescue applications, and the use of crowdsourced data for geographic information. The research trends indicate a progression from foundational work on data matching algorithms to applied research in emergency response scenarios, particularly mountain rescue operations. She has supervised numerous students including PhD candidate Mathieu Mehdi ZRHAL (defended 2023), research engineers, and multiple internship students working on geospatial projects related to mountain rescue, data integration, and GIS development. She has also led the pedagogical responsibility for the first-year master's degree in geomatics at Gustave Eiffel University from August 2019 to March 2025. Dr. Van Damme's work bridges theoretical research in geographic information science with practical applications in emergency response, demonstrating the real-world impact of geospatial technologies.
