Nozha Boujemaa is a Research Director at Inria and Director of DATAIA Institute, leading projects in Data Sciences, Algorithmic Transparency, and Societal Impact. She co-founded the Digital Society Institute (ISN) and serves as a Senior Scientific Advisor for AI initiatives. Expert in Large Scale Multimedia Content Search, Pattern Recognition, and Machine Learning Developed methods for visual content enrichment, interactive retrieval, and satellite image analysis Scientific leader of Pl@ntNet (plant identification), CHORUS (multimedia search engines), VITALAS, MUSCLE, and TRENDS projects Her research spans Multimedia Retrieval , Big Data Applications , and Algorithmic Transparency , with over 150 publications. She has supervised 25+ PhD/Master's students and organized conferences like ACM Multimedia 2013 and European Big Data Value Forum 2017. Key scientific awards include: Knight of the National Order of Merit (France) She has contributed to international projects (NSF, European Commission), served on editorial boards for Multimedia Tools and Applications , and co-chaired workshops on visual digital libraries and AI ethics. Her work impacts web search, cybersecurity, biodiversity, and earth observation.
Arnaud Giacometti is a Professor at the University of Tours, affiliated with the Faculty of Science and Technology and the Computer Science Department. He is also the President of the University and leads research at the Tours Fundamental and Applied Computer Science Laboratory (LIFAT). Research Interests: His work focuses on data mining, pattern discovery, and knowledge graph analysis. He has developed innovative methods for frequent pattern mining, sequential pattern sampling, and constraint-based clustering, emphasizing efficiency and scalability in large datasets. Recent Publications: His research trends include trie-based sampling, reservoir pattern sampling in data streams, and semantic web-driven comparison tables. Key subfields span distributed databases, knowledge graph representativeness, and memory-aware parallelization for big data. Scientific Roles: As President of the University of Tours, he oversees institutional leadership while maintaining active research contributions. His laboratory (LIFAT) serves as a hub for fundamental and applied computer science research.
Dominique LI is a Professor in the Department of Computer Science at the University of Tours, affiliated with the Fundamental and Applied Computer Science Laboratory (LIFAT) and the Polytechnic School of the University of Tours (EPU). His research focuses on pattern mining, skyline computation, and data mining applications in domains like lithium-ion battery analysis and fluid mechanics. His recent publications highlight advancements in sequential pattern sampling, dynamic texture classification, and skyline maintenance for data streams. Keywords span Computer Science Machine Learning Database Systems Fluid Dynamics Text Classification Algorithm Design .
Veronika Peralta Costabel is an Associate Professor at the University of Tours and a key member of the LIFAT Computer Science Laboratory . She serves as Head of the Master 2 SIAD program and has held academic positions at institutions in France, Uruguay, and Argentina since 1996. PhD in Computer Science (2006) from University of Versailles and University of the Republic of Uruguay Master in Computer Science (2001) and Engineer degree (1998) from University of the Republic of Uruguay Her research focuses on Data Quality , Exploratory Data Analysis , and Query Personalization in heterogeneous and distributed systems. She has led projects on data narratives in journalism, public health, and urban mobility. Recent article trends emphasize context-aware data quality frameworks , interestingness measures for OLAP queries , and automated data storytelling models , particularly in big data and cloud environments . Scientific Awards : Young Researcher Prize (2005), CLEI-UNESCO 3rd Prize (2003), LIFAT Council Member (2018-2019) She supervises PhD and Master students in topics like SQL workload analysis, data narrative generation, and semantic trajectory clustering, and contributes to journal editorial work and conference organization .
Professor Vassilis Christophides is a Full Professor at the École Nationale Supérieure d'Électronique et de ses Applications (ENSEA), part of the University of Cergy-Pontoise. His research focuses on Machine Learning Systems, Data Science & Big Data Computing, Databases and Semantic Web Systems, and Digital Libraries. He has published over 155 articles in top-tier journals/conferences like ACM SIGMOD and IEEE ICDE. Teaching includes courses such as Machine Learning, Big Data Computing, and Transparency in AI at the undergraduate and master's levels. He actively serves in conference roles (e.g., Tutorial Chair at WISE 2022), steering committees (EDBT Association), and as a reviewer for ERC grants. His work emphasizes ethical AI, explainable systems, and scalable data management across domains like Cultural Heritage and Environmental Sciences. Notable contributions include the BDA 2021 Best Paper Award and leadership in projects like the NOON research group. His work bridges theoretical advancements with real-world applications in data-driven decision-making and system optimization.
Louis Mandel is a Researcher at Inria, specializing in reactive programming languages and probabilistic systems. He co-developed ReactiveML and Q*cert (a verified query compiler), with applications in chatbots and cloud log analysis. His work bridges formal methods and practical tools for embedded systems.
Danai Symeonidou is a Researcher (CR) at INRAE in Montpellier since 2015, working in the GAMMA team. She holds a PhD from University Paris Sud (2014) and a joint Greek-French Master's degree from the University of Crete and University Paris Sud. Her research focuses on Semantic Web technologies, key discovery in knowledge bases, rule mining, and descriptive analytics. She has conducted postdoctoral research at Telecom ParisTech and was a visiting researcher at the Insight Centre for Data Analytics in University College Cork, Ireland. Education includes a Bachelor's degree from the University of Macedonia (2009), followed by advanced studies in France. She has extensive teaching experience across multiple institutions, including courses on Discrete Mathematics, Relational Databases, UML, Algorithms, and Semantic Web topics. Her work emphasizes scalable solutions for data linking and key discovery, with contributions to RDF data analysis, knowledge base optimization, and agrifood chain modeling. She has published widely in top venues such as ISWC, K-Cap, and EKAW, focusing on theoretical foundations and practical applications of semantic web technologies.
Nicole Schweikardt is a full Professor at the Department of Computer Science, Humboldt-Universität zu Berlin, since 2014. She previously held positions at Goethe-Universität Frankfurt (W2/W3 Professor for Theory of Complex Systems, 2007-2014) and served as Junior-Professor for Logic and Database Theory at HU Berlin (2005-2007). Her research focuses on logic in computer science, particularly database theory and complexity theory. Key contributions include algorithmic meta-theorems for bounded degree structures, efficient query evaluation techniques, and analysis of first-order logic extensions with counting quantifiers. She explores query languages' expressivity, document spanners, and locality properties in logic. Recent work (2025) covers event stream query discovery, color refinement for relational structures, and learning aggregate queries via first-order logic. Earlier studies (2018-2022) address FO+MOD queries under updates, Hanf normal forms, and enumeration algorithms over sparse graphs. GI-Dissertationspreis (2002) Emmy-Noether Fellowship (2005) Heinz Maier-Leibnitz-Prize (2007) Teaching Award (2015) She has supervised 6 PhD theses and contributed to database conferences (PODS, ICDT, LICS) as PC member and workshop organizer. Her affiliations include DFG Fachkollegium Informatik (since 2024) and editorial boards of Acta Informatica and ACM SIGLOG Education Committee.
Benjamin Monmege is an Associate Professor at Aix-Marseille Université , affiliated with the Laboratoire d'Informatique et Systèmes (LIS) and the MOdelisation and VErification team. He previously held a Post-doc position at Université libre de Bruxelles (ULB) until August 2015. His research focuses on formal methods for software verification, synthesis, and quantitative analysis using automata theory , game theory , and grammatical inference . He has developed tools like MightyL for MITL-to-timed-automata conversion and QuantiS for quantitative specification verification. Monmege has mentored PhD students including Julie Parreaux (Université Paris-Est Créteil), Théodore Lopez (Université Rennes), and Julie Parreaux . His recent work includes algorithmic advancements in weighted timed games, stochastic strategy emulation, and robust controller synthesis.
Ladjel Bellatreche is a Full Professor of Data Engineering at the National Engineering School for Mechanics and Aerotechnics (ENSMA) in Poitiers, France, where he has been a faculty member since September 2010. He leads the Data and Model Engineering Team at the Laboratory of Computer Science and Automatic Control for Systems (LIAS) . His educational background includes an Engineering degree in Computer Science obtained in 1992 from the Department of Computer Science at Sidi Bel Abbès, Algeria. He later held positions as Assistant and Associate Professor at the University of Poitiers, France, and served as a Visiting Professor at the University of Québec in Outaouais, Canada, and a Visiting Researcher at Purdue University and Hong Kong University of Science and Technology. His research interests span a wide range of topics in data engineering, including semantic data integration , ontology-based database design , big data and cloud computing , green computing , and database deployment . He has made significant contributions to the design and optimization of data warehouses, particularly in the context of large-scale and distributed systems. Bellatreche has an extensive publication record, including over 60 journal articles and numerous conference proceedings. His recent work focuses on scalable RDF query processing, green query optimization, and leveraging linked open data for enhancing traditional data warehouses. He has also co-authored several books and book chapters on data warehousing and big data analytics. He actively participates in the research community by serving as a reviewer for top-tier journals such as IEEE TKDE and DKE , and as an editorial board member for various international journals. He has organized and co-organized numerous conferences and workshops, including DAWAK , DASFAA , and MEDI , and has served on the program committees of over 40 international conferences. Bellatreche is deeply involved in promoting research in Africa and Asia, where he co-supervises several PhD and Master's students and organizes conferences and workshops to foster collaboration and knowledge exchange.
Dominique Geniet serves as an Associate Professor at the University of Poitiers, France, working within the College of Engineering. He is affiliated with the LIAS Laboratory (Laboratoire d'Ingénierie des Applications de la Signalétique), which maintains dual locations at ENSIP (École Nationale Supérieure d'Ingénieurs de Poitiers) and ISAE-ENSMA (École Nationale Supérieure de Mécanique et d'Aérotechnique). His research spans theoretical computer science and practical applications in real-time systems, with significant contributions to scheduling theory, system validation, and more recently, data management. Professor Geniet's research focuses on hard real-time systems with strict temporal constraints, employing formal methods including regular languages, generating functions, and discrete geometry. His early work concentrated on theoretical foundations of scheduling algorithms for uniprocessor and multiprocessor systems, evolving toward distributed real-time systems validation, and more recently expanding into data warehousing optimization and query scheduling. His interdisciplinary approach bridges theoretical computer science with practical applications in critical systems where timing constraints are paramount. Analysis of his publication trajectory reveals a consistent focus on real-time systems with evolving applications. The earliest works (1995-2005) emphasize theoretical foundations using formal language theory and mathematical models for scheduling and validation. The middle period (2005-2012) shows expansion into distributed systems and geometric approaches to validation. The most recent publications (2012-2018) demonstrate a strategic pivot toward data management problems while maintaining the core focus on timing constraints and optimization. This evolution reflects both theoretical depth and practical adaptation to emerging computational challenges. Professor Geniet has been actively involved with the Real Time Team and Data Engineering Team within LIAS Laboratory. His work demonstrates strong collaboration with researchers including Gaëlle Largeteau-Skapin, Annie Choquet-Geniet, and Ladjel Bellatreche, suggesting participation in both theoretical research groups and applied projects with potential industrial relevance. The dual affiliation with ENSIP and ISAE-ENSMA indicates work spanning broader engineering contexts beyond pure computer science.
Ladjel Bellatreche is a Full Professor of Data Engineering at ISAE-ENSMA (National Engineering School for Mechanics and Aerotechnics) in Poitiers, France, where he has served as faculty since September 2010. He leads the Data and Model Engineering Team within the Laboratory of Computer Science and Automatic Control for Systems (LIAS). Prior to his current position, he spent eight years as Assistant and then Associate Professor at Poitiers University. His academic journey includes visiting positions at the University of Québec en Outaouais (Canada), Purdue University (USA), and Hong Kong University of Science and Technology (China). Professor Bellatreche's research focuses on Semantic Data Integration, Ontology-based Database Design, Life Cycle of Extremely Large Database Design, Big Data & Cloud Computing, Green Computing, and Database Deployment. His work bridges theoretical foundations with practical applications in data management systems, particularly addressing challenges in scalability, efficiency, and semantic enrichment of data repositories. His research has evolved from traditional data warehousing to encompass modern big data analytics and energy-efficient database systems. Analysis of his recent publications reveals a strong trend toward addressing the challenges of big data management through innovative approaches in semantic integration, graph-based query optimization, and green computing. His research shows consistent focus on data warehousing evolution, with increasing emphasis on semantic technologies, RDF data processing, and energy efficiency in query processing. The work demonstrates progression from traditional database design to contemporary challenges in data science and advanced analytics. Professor Bellatreche actively contributes to the academic community through editorial roles, including serving as an Editorial Board Member for the International Journal of Reasoning-based Intelligent Systems and Subject Area Editor for the Scalable Computing Journal. He has organized numerous international conferences and workshops including DAWAK, DOLAP, and MEDI, and has served on program committees for over forty international conferences. He is deeply involved in research mentorship and international collaboration, particularly in Africa and Asia, where he co-supervises students and organizes academic events such as ICT-EurAsia and CIIA. His work extends to promoting research capacity building in developing regions through academic partnerships and collaborative projects. As leader of the Data and Model Engineering Team at LIAS laboratory, Professor Bellatreche oversees research initiatives focused on advanced data management systems. His team works on cutting-edge problems in database design, optimization, and integration, with particular expertise in semantic data warehousing, big data analytics, and green computing approaches for database systems.
Pascal Richard is a Full Professor specializing in Real-Time Systems at the Institute of Technology, University of Poitiers. He is affiliated with the LIAS (Laboratoire d'Ingénierie des Applications de la Résolution des Systèmes) laboratory, which has locations at both ENSIP (École Nationale Supérieure d'Ingénieurs de Poitiers) in Poitiers and ISAE-ENSMA (Institut Supérieur de l'Aéronautique et de l'Espace - École Nationale Supérieure de Mécanique et d'Aérotechnique) in Chasseneuil. His research spans over two decades with continuous publication from 1999 through 2023. Professor Richard's primary research interests focus on real-time scheduling theory, embedded systems, and avionics. His work particularly emphasizes AFDX networks , cache-related preemption delays , self-suspending tasks , and worst-case response time analysis . His research bridges theoretical foundations with practical applications in safety-critical systems, particularly in the aerospace domain. He has made significant contributions to the understanding of scheduling anomalies, feasibility analysis, and the development of approximation schemes for complex real-time problems. His publication record demonstrates strong collaboration with researchers across France and internationally, with consistent contributions to major real-time systems conferences including RTSS, ECRTS, and RTNS. His work shows an evolution from foundational scheduling theory toward increasingly complex systems including multiprocessor platforms, mixed criticality systems, and integrated modular avionics architectures. Professor Richard has contributed to the real-time community through numerous journal publications in Real-Time Systems , IEEE Transactions on Computers , and IEEE Transactions on Industrial Informatics , among others. His most recent work continues to address cutting-edge challenges in real-time scheduling for modern computing platforms.
Jorge Galicia Auyon serves as a Lecturer in the Data Engineering team at ISAE-ENSMA (Poitiers, France), affiliated with the LIAS Laboratory of Automatic Systems. He holds a PhD in Computer Science from ISAE-ENSMA completed in 2021. Education: PhD in Computer Science, ISAE-ENSMA, 2021. Thesis: "Revisiting Data Partitioning for Scalable RDF Graph Processing". His research centers on Semantic Web infrastructure and Big Data systems, with specialized expertise in RDF query optimization, graph database partitioning, and distributed data processing. He develops novel techniques for scalable RDF graph traversal and fragmentation strategies applicable to large-scale clustered environments. Analysis of his 2019-2021 publications reveals consistent innovation in bridging physical/logical data partitioning for RDF systems, advancing query performance through graph exploration frameworks. These contributions strengthen foundations for industrial-scale semantic technologies. Research Environment: He actively contributes to the Data Engineering team at LIAS laboratory, which operates across ISAE-ENSMA's Chasseneuil campus and ENSIP's Poitiers facility, focusing on automatic control, real-time systems, and data engineering solutions.
Houssameddine Yousfi serves as an ATER (temporary academic researcher) in Data Engineering at the University Institute of Technology (IUT) of the University of Poitiers, France. He maintains dual institutional affiliation through the LIAS laboratory (Laboratoire d'Ingénierie des Systèmes Automatisés), operating jointly at ENSIP (University of Poitiers) and ISAE-ENSMA engineering school. His doctoral research culminated in a 2023 PhD thesis titled Efficient Query Processing when Spatial Data Meets RDF Graph , co-supervised by ISAE-ENSMA and Université Aboubekr Belkaid de Tlemcen (Algeria). This work established foundational integration methods between spatial databases and semantic web technologies. Yousfi's research centers on overcoming scalability barriers in heterogeneous data systems, specifically developing novel query optimization techniques for RDF graphs and spatial databases. His methodology emphasizes hybrid indexing structures and distributed processing frameworks to handle industrial-scale datasets, with applications spanning geospatial analytics and knowledge graph querying. Current projects focus on optimizing R-tree exploration for spatial RDF datasets through parallel computation strategies. His publication pattern reveals consistent specialization in big data infrastructure, with both 2020-2021 conference papers addressing computational bottlenecks in spatial and semantic data domains. The 2021 WISE conference paper introduced RDF_QDAG for distributed RDF querying, while the 2020 AI2SD paper pioneered R-tree enhancements for spatial big data. As a core member of LIAS' Data Engineering research team, he collaborates with international researchers including Boumediene Saidi, Amin Mesmoudi, and Allel Hadjali on EU-funded data infrastructure projects. His laboratory work integrates automated control systems with advanced data processing pipelines at the Poitiers and Chasseneuil research sites.
