Romain Raveaux is an Associate Professor at the LIFAT Computer Science Laboratory, University of Tours, affiliated with Polytech Tours. His research focuses on Image Analysis, Machine Learning, Structural Pattern Recognition, Graph Matching, Graph Neural Networks, Discrete Optimization, Reinforcement Learning, and Transfer Learning . Email: romain.raveaux@gmail.com , romain.raveaux@laposte.net Address: 64 av. Jean Portalis, Tours, France, 37200 Phone: +33 (0)2 47 36 14 27 Research Interests Graph Matching and Neural Networks Discrete Optimization for Pattern Recognition Transfer Learning in Graph-Based Models Historical Document Analysis Scientific Trends His recent work bridges Graph Neural Networks with Mixed-Integer Programming , focusing on Image Semantic Segmentation and Graph Cycle Detection . Earlier studies emphasize Genetic Algorithms for graph classification and Graph Edit Distance optimization in pattern recognition.
Quentin Stiévenart is a researcher at Université du Québec à Montréal, focusing on abstract interpretation, concurrency, and static analysis. His work spans programming language design, software verification, and tool development for WebAssembly and functional languages like Racket and Scheme. Active in organizing and reviewing for conferences including SPLASH, ICFP, ECOOP, and SAS Developed tools such as Wassail for WebAssembly static analysis and RacketLogger for educational purposes Contributions include theoretical work on effect-driven flow analysis and practical advancements in concolic execution abstraction His research addresses challenges in concurrency verification, cyclic reinforcement in incremental analysis, and security-focused taint tracking across multiple language paradigms.
Mostafa BAMHA is an Associate Professor (Maître de Conférences) at the University of Orleans, affiliated with the LIFO Laboratory (Laboratoire d'Informatique Fondamentale d'Orléans). He leads research in parallel and distributed computing, focusing on MapReduce optimization , data skew handling , and scalable graph processing . Member of the PRV team (Parallelism, Virtual Reality, System Verification) Active in projects: HPIAF (High Performance computing for AI in Finance) INEx (Cloud Computing experiments) Girafon (Graph & BigData processing) Research Trends His publications from 2018–2024 show focus on: MapReduce optimizations for join operations and LSH similarity joins Graph processing challenges in Pregel with high-degree vertices Skew-insensitive algorithms across distributed architectures Academic Contributions Co-author in 15+ peer-reviewed publications (2000–2024) including International Journal of Parallel Programming , DEXA , and HLPP conferences. Key collaborators: Sébastien Rivault , Mohamad Al Hajj Hassan , Sophie Robert .
Tony Lelièvre is a Professor of Applied Mathematics at the Ecole Nationale des Ponts et Chaussées, part of the Institut Polytechnique de Paris. He holds a PhD (2004) and Habilitation (2009), specializing in multiscale modeling, molecular simulation, and stochastic processes. His research focuses on free energy computations, numerical analysis of complex fluids, and computational statistical physics. He co-authored two books and over 100 papers, receiving significant awards like the ERC Consolidator Grant (2013-2019) and the Grand prix Alcan. Lelièvre has organized numerous international workshops and serves on editorial boards of journals like ESAIM:M2AN and SIAM/ASA Journal of Uncertainty Quantification. His work bridges applied mathematics, numerical analysis, and computational science with applications in materials science and industrial fluid dynamics. Education: PhD in Applied Mathematics, 2004 Habilitation à Diriger des Recherches, 2009 Research Interests: Multiscale modeling of complex fluids Molecular dynamics and free energy calculations Stochastic methods for metastable systems Numerical analysis of PDEs and SDEs Computational statistical physics Recent Contributions: His work on adaptive biasing force methods and hybrid Monte Carlo techniques has advanced the simulation of rare events and free energy landscapes. He contributed to variance reduction techniques in molecular simulations and mathematical analysis of parallel replica algorithms. Awards: ERC Consolidator Grant (2013-2019), Prix CS 2002, Grand prix Alcan (2010), Ordway Visiting Professorship (2012-2013), and several teaching awards. Professional Activities: Organized major conferences like CEMRACS 2013 and IPAM Long Program on Energy Landscapes (2017). Co-edits journals and authored influential textbooks on magnetohydrodynamics and free energy computations.
Laure Gonnord is a Full Professor in Computer Science at Grenoble INP , affiliated with the Esisar Engineer School in Valence, France, since September 2021. She is a member of the CTSYS research team at the LCIS laboratory and an external member of the CASH team at the University of Lyon / CNRS / LIP / Inria. Her research focuses on compilation , static analysis , and applications to safety , security in high-performance and embedded systems . Fields of Interest : Compiler Design Static Analysis for Safety & Security Abstract Interpretation Embedded Systems High-Performance Programming Hardware Security Engineering Research Trends (from recent publications): Her work explores modular verification through monadic abstract interpreters, complexity bounds in term rewriting , and educational tools for theorem proving . Notable contributions include compiler hardening schemes for hardware security and memory layout optimizations for algebraic data types. Academic Leadership : Scientific Director of the Summer School EJCP (École Jeune Compilation et Programmation) Board Member of the French national research group GDR GPL Teaching Responsibilities at Grenoble INP include courses in architecture , compilation , programming languages , algorithms , and databases . She has also taught at University of Lyon, ENS Lyon, Polytech'Lille, and INSA.
Stéphane MAAG is a Professor at Telecom SudParis, associated with the SAMOVAR research laboratory. His work focuses on formal methods for protocol validation, network security, and distributed systems monitoring. He has contributed to advancing testing methodologies for mobile ad-hoc networks (MANETs), IoT protocols, and wireless communication systems. His research integrates machine learning techniques for automation and has been applied to web testing frameworks and ERP systems. Key research areas include protocol performance evaluation, trust management in networks, and curriculum design for computer science ethics. His work spans both academic surveys (e.g., European ethics education studies) and technical innovations (e.g., proactive interoperability solutions for wireless routing). Publications emphasize practical applications of formal methods, with over 50 peer-reviewed articles in journals like International Journal of Ethics Education , Computer Communications , and IEEE Transactions . His work bridges theoretical foundations with real-world systems like ODOO ERP and SIP protocols.
Jerry Lacmou Zeutouo is a Lecturer in the field of Networks and Data at Université de Picardie Jules Verne (UPJV), contributing to computer science research through advanced algorithm development and optimization. His work primarily focuses on parallel computing, dynamic programming, and computational biology. Academic Rank: Lecturer at UPJV Institution: Université de Picardie Jules Verne Research Interests: Dr. Zeutouo specializes in designing and optimizing parallel algorithms for dynamic programming and database security. His research spans: Coarse-Grained Multicomputer (CGM) architectures K-anonymity in distributed databases RNA folding and bioinformatics applications Longest Common Subsequence (LCS) constraints Publication Trends: His work emphasizes solving computational bottlenecks via parallelism, particularly in cloud environments, bioinformatics, and database security. Key themes include: Transformer-based models for cold start mitigation in FaaS Multicomputer algorithms for dynamic programming Four-splitting and Four-Russians techniques for optimization
Ningning Xie is a researcher affiliated with the University of Toronto, specializing in functional programming, type systems, and logics. Their work spans applications in compilers, code generation, and machine learning, with a focus on compositional programming and effect handling. Research interests include: Functional programming Type systems Logics Compiler design Multi-stage programming Effect handlers Recent publications demonstrate expertise in type-level programming, staged compilation, and effect systems. Key areas include distributive disjoint polymorphism, parallel algebraic effect handlers, and macro systems for OCaml and Haskell. Their work bridges theoretical formalisms with practical language implementations. Contributions to academic service include organizing and reviewing for conferences like POPL, PLDI, ICFP, and Haskell workshops. Notable roles include Publicity Chair for POPL 2026 and Co-chair for PLMW and Artifact Evaluation committees.
Frank Pfenning is a Professor in the Department of Computer Science at Carnegie Mellon University's School of Computer Science. With decades of active research and service in programming languages and logic communities, he maintains a significant presence across major conferences including POPL, ICFP, and ESOP. His research spans foundational work in Programming Languages , Logic and Type Theory , Logical Frameworks , Automated Deduction , and Trustworthy Computing . Recent publications reveal a strong focus on session types, substructural logics, and their applications to concurrency and distributed systems. His work bridges theoretical foundations with practical implementations for reliable communication protocols. Analysis of his publication trends shows a consistent evolution from foundational type theory toward practical applications in concurrent and distributed systems. The integration of logical frameworks with session types represents a signature research trajectory, increasingly addressing real-world challenges in protocol verification and deadlock freedom. As an active community member, Pfenning has served on numerous program committees including POPL (2016-2025), ICFP (2015-2022), and ESOP. His mentoring activities include PLMW@POPL presentations, demonstrating commitment to training next-generation researchers. His technical contributions are primarily disseminated through premier venues in programming languages research. The absence of explicit grant information in available sources suggests focus on theoretical contributions rather than large-scale funded projects, though his sustained conference participation indicates stable institutional support.
Kinan Abbas is a researcher at the University of Strasbourg's College of Science and Engineering, Department of Computer Science, specializing in hyperspectral imaging and machine learning. His work focuses on spectral image processing techniques including unmixing, demosaicing, and low-rank matrix approximation. His research interests center on hyperspectral imaging and machine learning applications, particularly developing novel methods for snapshot spectral image processing. Key contributions include locally-rank-one-based joint unmixing frameworks, diffusion models for texture synthesis, and entropy-weighted spectral deconvolution techniques. His work bridges theoretical signal processing with practical applications in remote sensing and computational photography. Analysis of his publication trend (2021-2025) shows evolution from foundational spectral unmixing techniques toward advanced generative models, with increasing focus on diffusion-based synthesis and multifractal analysis. His research consistently addresses computational challenges in spectral image reconstruction. Abbas actively collaborates with researchers from ICube laboratory (Strasbourg), including Matthieu Puigt, Gilles Delmaire, and Gilles Roussel, evidenced by consistent co-authorship across 14 publications. His work appears in IEEE Transactions, ICASSP, and French GRETSI conferences, indicating strong institutional support for his research program.
Jonathan Sarton is a Lecturer in Computer Science at the University of Strasbourg and a Researcher at the ICube laboratory. He holds an affiliation with the Geometric and Graphics Computing (IGG) team within the UFR of Mathematics and Computer Science. His primary research focuses on scientific visualization, volume rendering, and GPU programming. Education includes a PhD (2018) from the University of Reims Champagne-Ardenne titled 'High-performance interactive visualizations of massive volumetric data: an out-of-core multiresolution approach based on GPUs' , and a Master's in Visualization, Imaging, and Performance (2014) from the University of Orléans. He has held roles including Temporary Teaching and Research Associate (ATER) at the University of Reims (2018-2019) and a doctoral researcher at CReSTIC (2015-2018). Current research emphasizes interactive visualization of large unstructured meshes from numerical simulations, supported by the ANR LUM-Vis project. His technical work includes GPU-based out-of-core architectures for handling AMR time series data and distributed visualization systems. Professional activities include teaching computer science courses at undergraduate and graduate levels, focusing on 3D graphics, parallel programming, and algorithms. Professional contact: Office C118 at ICube, sarton@unistra.fr .
Dario Colazzo is a Professor at Université Paris-Dauphine, affiliated with the LAMSADE laboratory. His research focuses on database systems and programming languages, particularly in cloud databases, type systems for semi-structured data, and query processing optimizations. He has contributed extensively to XML and XQuery research, including parallel query execution and static analysis techniques. His teaching includes courses on programming (Java), UML, database systems, and semantic web technologies at both undergraduate and graduate levels (in French). Key research interests span XML/XQuery optimization, JSON processing, distributed systems, and type systems for safe and efficient data handling. His work emphasizes scalable cloud-based solutions and efficient query execution models. Publications highlight advancements in parallel query processing (e.g., PAXQuery), type-based optimizations for XML updates, and semantic graph analytics. He has collaborated with institutions like EDBT, WWW, and VLDB conferences.
Anatole Lefort is a Postdoctoral Researcher at the Systems Research Group of the Technical University of Munich, hosted by Prof. Pramod Bhatotia. His current research focuses on CXL-based disaggregated memory systems for heterogeneous compute architectures. Dr. Lefort's educational background includes: Ph.D. in Computer Science from Institut Polytechnique de Paris (2023), advised by Pierre Sutra and Gaël Thomas. Diplôme d’Ingénieur (M.S. Eng.) from Télécom SudParis (2018), where he graduated first in his class. His research interests encompass Distributed Systems, Distributed Computing, Persistent Memory, Concurrency, Language Runtimes, and Cloud Infrastructures. During his doctoral studies, he investigated persistent memory programming, particularly for managed languages like Java, addressing challenges in memory management and concurrency. His recent publications demonstrate a strong focus on persistent memory systems, with contributions such as J-NVM for off-heap persistent objects in Java and the WB-amcast protocol for fault-tolerant atomic multicast, reflecting expertise in both systems and distributed algorithms. Dr. Lefort has been recognized with several awards: Laureate of Engineers of the Future Awards, Engineers for Research category (December 2022). Best student publication in ICTs at Institut Polytechnique de Paris (September 2022). He has not advised any students to date. His funding includes an NVMW Student Travel Grant for the 2022 workshop and a Fully-Funded Ph.D. Scholarship from Institut Mines-Télécom awarded in 2018 based on academic excellence. Currently, Dr. Lefort is part of the Systems Research Group at TUM. Previously, he was a member of the Parallel and Distributed Systems Group at Télécom SudParis during his Ph.D. studies.
Radu Mateescu is a Research Director at Inria Grenoble - Rhône-Alpes where he heads the CONVECS research team. He has been with Inria since 1998, previously working as a researcher in the VASY project-team. His research focuses on formal methods, particularly model checking and verification of concurrent systems. Mateescu holds a PhD in Computer Science from INPG (Institut National Polytechnique de Grenoble) with a thesis on "Verification of the temporal properties of parallel programs". His educational background includes a graduate engineer diploma from the POLITEHNICA University of Bucharest in Automatic Control and Computers. He has been instrumental in developing several formal verification tools including XTL, CAESAR_SOLVE, EVALUATOR, and BISIMULATOR. His research interests span formal specification and verification of temporal properties of concurrent systems, temporal logics extended with data-handling primitives, on-the-fly model checking, equivalence checking, diagnostic generation, partial order reduction, and massively parallel verification. He served as chairman of the FMICS (Formal Methods for Industrial Critical Systems) Working Group of ERCIM from 2011 to 2014. Mateescu has published extensively in formal methods, with recent work focusing on applications in autonomous vehicles, IoT systems, and hardware verification. His publications show a consistent trend toward applying formal verification techniques to increasingly complex real-world systems, particularly in safety-critical domains. Test-of-Time Tool Award at ETAPS'2023 Inria - Académie des Sciences - Dassault Systèmes Innovation Prize Information Technology Award from Fondation Rhône-Alpes Futur Mateescu has taught courses at ENSIMAG (Grenoble), ESIREM (Dijon), and the University of Savoie. He has contributed to major research projects involving industrial applications of formal methods, particularly through the CADP toolbox which has been used to verify numerous critical systems including the IEEE-1394 FireWire protocol, Bull's cluster file system, and autonomous vehicle systems. His work bridges theoretical formal methods with practical industrial applications.
Beatrice Markhoff is a Professor of Computer Science (CNU 27) at the University of Tours , affiliated with the Faculty of Science and Technology (Blois site) and the Computer Science Department . She is a member of the UMR CNRS 7324 - CITERES - Archaeology and Territories Laboratory (CITERES-LAT) and the Maison des Sciences de l'Homme Val de Loire (MSH VdL) . Her research focuses on semantic interoperability , Web of data , knowledge representation , and knowledge extraction , with significant collaborations in cultural heritage disciplines . Doctorate in Computer Science, University of Franche-Comté (1995) HDR (Habilitation to Direct Research), François Rabelais University of Tours (2013) Her research themes include semantic interoperability, knowledge engineering, and data mining, with applications in cultural heritage. She co-created the International Workshop on Semantic Web for Cultural Heritage and co-edited special issues for the Semantic Web Journal and JOCCH . She leads projects like ANR SESAMES (2018-2023), H2020 ARIADNEplus (2019-2022), and H2020 4CH (2021-2023). Her academic contributions span XML data management, semantic web technologies, and functional programming. Key projects include carto4CH for cultural heritage mapping and OpenArchaeo for knowledge graph profiling. She has co-supervised PhD theses on topics like LOD querying and knowledge extraction from Wikidata .
