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Ruben Verborgh is a Professor of Decentralized Web Technology at the Ghent University – imec and a Visiting Fellow at the Oxford Martin School (University of Oxford). He leads the Internet Technology and Data Science Lab (IDLab) and co-founded the Solid platform with Tim Berners-Lee to re-decentralize the Web. His research focuses on Linked Data Fragments , a paradigm for Web-scale query execution, and explores decentralized data governance , user-controlled data ownership , and rule-based Web agents for policy enforcement. He has co-authored two books on Linked Data and contributed to over 250 publications. Recent articles highlight trends in decentralized data ecosystems , including ODRL policy interoperability , event notification systems , and personal data vaults . His work bridges Linked Data , hypermedia APIs , and privacy-preserving technologies . Verborgh collaborates with institutions like MIT, Oxford, and the European Commission, and advises companies through Inrupt . His labs ( IDLab , Solid Ecosystem ) focus on sustainable data-driven societies.

• Decentralization
• Linked Data
• Querying
• +7 more

Bertrand Verlaine is a Professor at the University of Namur, affiliated with the School of Social Sciences, Politics and Communication and the department of Economics Management Communication Politics. His expertise spans Service Engineering, IT Project Management, and Requirements Engineering for service-oriented systems. He holds a Doctorate in Economics and Business Management, along with advanced qualifications in education and business administration from the University of Namur. Education: PhD in Economics and Business Management (2016) Engineer in Business Administration (specialization: information management) - UNamur (2009, 1st in class) Bachelor in Business Administration - UNamur (2007, cum laude) Research focuses on integrating Agile methodologies with ITIL frameworks, service-oriented design, and value co-creation principles. His work emphasizes aligning service systems with management frameworks and enhancing requirements engineering processes. Key themes include IT service management frameworks, agile-ITIL synergy, and conceptual modeling of service components. Prominent contributions include advancing Agile IT Service Management frameworks and adapting ITIL lifecycle models to incorporate SCRUM. His research outputs consistently address the theoretical and practical challenges of service-oriented systems and their alignment with organizational processes. He has supervised 19 academic works but no named students are listed. No specific grants or labs are mentioned, though his involvement in international conferences like IESS and workshops like WeRE 2010 highlights active engagement in the academic community.

• Service Engineering and Management
• IT Projects Management
• Requirements Engineering
• +5 more

Chris Cornelis is a full-time Professor in fuzziness and uncertainty modelling at Ghent University's Department of Applied Mathematics, Computer Science and Statistics. His research integrates fuzzy logic and rough set theory to advance machine learning methodologies for complex data analysis. Education: M.Sc. in Computer Science, Ghent University (2000) Ph.D. in Computer Science, Ghent University (2004) Research Focus: Cornelis pioneers fuzzy-rough hybrid systems for uncertainty handling in machine learning. His work spans theoretical foundations (e.g., implication operators, granular approximations) and practical applications including emotion detection, medical diagnosis, and imbalanced data classification. Key innovations include FRNN-OWA classifiers and polar encoding for missing values, demonstrating exceptional versatility in bridging abstract mathematics with real-world AI challenges. Publication Trends: Recent work (2023-2025) reveals intensified exploration of topological data analysis (Mapper-based rough sets), advanced granular computing (disjoint/adjacent fuzzy granules), and ethical AI ("No Imputation Without Representation"). His research shows consistent progression from foundational fuzzy-rough theory toward multi-disciplinary applications while maintaining mathematical rigor, particularly in Choquet integration and quantifier-based frameworks. Scientific Awards: No specific awards were documented in the provided sources. Research Support: Cornelis has secured competitive funding including FWO postdoctoral mandates, a Ramón y Cajal contract at the University of Granada, and an FWO Odysseus Type II project at Ghent University. These grants enabled foundational work in fuzzy-rough set theory and its applications to complex data problems. Research Unit: He leads research within Ghent University's Computational Web Intelligence (CWI) unit, focusing on intelligent data analysis systems that leverage fuzzy-rough methodologies for web-scale information processing.

• Machine Learning
• Fuzzy Sets
• Rough Sets
• +3 more

Bart Bogaerts is an Associate Professor in the Department of Computer Science at KU Leuven's Faculty of Engineering Science. He is affiliated with the Declarative Languages and Artificial Intelligence (DTAI) research unit and is a member of Leuven.AI - KU Leuven Institute for Artificial Intelligence. Bogaerts serves on the Council of the Faculty of Engineering Science as senior academic staff and participates in the Programme Committee for Artificial Intelligence curriculum development. His research focuses on foundational aspects of logic programming and knowledge representation, with particular expertise in approximation fixpoint theory, higher-order logic programming, and non-monotonic reasoning. Bogaerts investigates the theoretical underpinnings of stable model semantics, justification frameworks, and executable query languages. His work bridges theoretical computer science with practical applications in artificial intelligence and knowledge-based systems. Bogaerts' publication record demonstrates consistent contributions to top venues in logic programming and artificial intelligence. His recent work shows increasing focus on category-theoretic approaches to approximation theory, distributed web traversal specifications, and certified model expansion techniques. The publications reveal a strong emphasis on formal methods with applications spanning from theoretical mathematics to practical AI systems. As a promotor for multiple significant research projects, Bogaerts leads investigations into certified answer set programming (CertifASP), first-order model expansion (CertiFOX), proof generation for combinatorial optimization, distributed configuration problems, and knowledge integration paradigms. These projects, funded through 2028-2029, demonstrate his leadership in advancing the theoretical foundations of AI and logic programming. Bogaerts is actively involved in teaching courses on knowledge representation and reasoning, contributing to the development of next-generation AI researchers. His work within the DTAI research unit positions him at the forefront of declarative AI approaches in Belgium's leading research university.

• Logic Programming
• Knowledge Representation
• Artificial Intelligence
• +6 more

Femke De Backere is a part-time Associate Professor at Ghent University's IDLab research group (Faculty of Engineering and Architecture, Department of Information Technology) and a full-time Senior Scientist at imec. She holds a Bachelor's in Informatics and Master's in Computer Science Engineering from Ghent University, with doctoral research focused on semantic technologies and personalized healthcare systems through collaborations with intensive care units and industry partners. Her research integrates computer science, health psychology, and movement science across three primary domains: (1) developing explainable knowledge models, (2) creating personalized context-aware systems, and (3) designing engagement mechanisms like serious games and gamification. Current projects include digital interventions for cancer survivors, VR-based ecological validity platforms, and physical activity promotion in older adults. Recent publications (2020-2025) demonstrate strong focus areas: 60% address personalized health interventions using machine learning and ontologies; 30% explore virtual reality applications in clinical and behavioral contexts; and 10% examine academic systems development . Longitudinal trends show increasing emphasis on sensor-driven behavioral analytics and adaptive eHealth platforms. She leads significant research initiatives including: GRAY – Ghent University Research for Aging Young (2020-2030) FWO projects on eHealth self-management for cancer survivors and sedentary behavior interruption Interdisciplinary grants for VR-enabled consumer preference studies and physical activity interventions Her lab coordinates doctoral training for 9+ students and collaborates with the eBehaviourChange research unit, focusing on technology-mediated behavior modification across clinical and wellness domains.

• Knowledge Representation
• Machine Learning
• Ubiquitous Computing
• +7 more

Mark Depauw is a Professor at the Faculty of Arts , KU Leuven. He leads the Research Group Antiquity, Leuven and is deeply involved in digital humanities initiatives, particularly through the Trismegistos project and its extensions like Trismegistos+ and STUDIUM.AI . His work bridges classical studies with computational methods. Research Focus: Quantitative analysis of classical text reception in early modern Europe Network analysis of ancient literary and social connections Development of digital infrastructures (GLAUx Treebank, Trismegistos) for ancient textual studies Application of transformer models and named entity recognition to Greek and Latin texts Membership & Collaborations: Member of the Faculty Council of Arts Part of KU Leuven Institute for Digital Society (DigiSoc) Core contributor to LECTIO for textual transmission studies Collaborator on international projects like CLARIAH-VL+ and NIKAW Teaching: He contributes to courses in historical data management, Greek papyrology, and digital research methods. His pedagogical approach integrates computational tools with classical scholarship.

• Classics
• Ancient History
• Papyrology
• +5 more

Maarten Bassier is an Assistant Professor (tenure track) at KU Leuven, affiliated with the Department of Civil Engineering within the Faculty of Engineering Technology. He is based at the Geomatics unit operating at the Ghent and Aalst Campuses. His academic profile combines research, teaching, and institutional service, with significant contributions to the field of digital construction technologies. As senior academic staff, he serves on both the Council of the Faculty of Engineering Technology and the Civil Engineering Department Council, actively participating in institutional governance while maintaining a robust research program focused on Scan-to-BIM methodologies and geospatial applications in construction. Dr. Bassier's research centers on Scan-to-BIM methodologies, which involve converting 3D scans of existing buildings into Building Information Models. His work bridges geomatics, computer vision, and civil engineering, with applications in construction progress monitoring, infrastructure inspection, and heritage documentation. He applies machine learning techniques to automate aspects of the modeling process, particularly semantic segmentation of point clouds and integration of UAV (drone) data. His research increasingly incorporates deep learning approaches for object detection, segmentation, and completion in complex built environments, with practical applications spanning road construction, bridge inspections, and electrical substation modeling. His interdisciplinary approach connects civil engineering with computer science to solve practical construction challenges through digital innovation. Bassier's recent publication record demonstrates a strong focus on automating the Scan-to-BIM process through advanced computational techniques. His work spans multiple application domains while maintaining a consistent methodological thread of integrating sensing technologies with semantic understanding of construction environments. The trajectory of his research shows increasing sophistication in machine learning applications, moving from basic point cloud processing to complex semantic understanding and automated model generation. His publications appear in high-impact journals across civil engineering, remote sensing, and computer vision domains, reflecting the interdisciplinary nature of his work. SESAME - Semantic Segmentation of Electrical Substations and Derived Models for Engineering (2024-2026) - Promotor UAV-assisted bridge inspections (2022-2027) - Co-promotor XR-empowered dynamic reality modeling for AECO applications (2021-2026) - Co-promotor Digitization in road construction: automating as-built models (2020-2026) - Co-promotor SCAN-to-BIM Automation of as-built BIM production through digitization and machine learning (2020-2025) - Co-promotor As a member of the Division Digital and Sustainable Civil Engineering and the Subdivision Geomatics Ghent, Dr. Bassier contributes to KU Leuven's research ecosystem focused on digital transformation in civil engineering. His teaching portfolio includes courses on BIM, industrial measurements, Scan-to-BIM, 3D modeling, and geomatics, preparing the next generation of civil engineers for the digital construction landscape. His work represents the cutting edge of digital construction technologies, with practical applications that address real-world challenges in infrastructure development and maintenance.

• Scan-to-BIM
• Geomatics
• Building Information Modeling
• +7 more

Erik Mannens is a Professor at Ghent University and Research Valorisation Director at imec, specializing in Semantic Technologies and Artificial Intelligence since 2005. He leads the Data Science team at IDLab, managing over 50 researchers focused on advancing semantic data science and its applications. Education: PhD in Computer Science Engineering (2011), Master’s in Computer Science (1995), and Master’s in Electro-Mechanical Engineering (1992) The team explores the fusion of semantics and AI, aiming to: (1) analyze the Web’s societal impact, (2) study Web-enabled lifestyle changes, and (3) develop future Web innovations. Their work emphasizes Open Source , Open Access , and Open Knowledge principles. His research spans Semantic Intelligence , Big Data Analytics , and Linked Data applications, with notable publications on RDF mappings, IoT platforms, and distributed data querying. Key projects include MapVOWL, RMLEditor, and the MASSIF platform. Current affiliations include Ghent University’s IDLab , where he contributes to cutting-edge research in data science and semantic technologies.

• Semantic Intelligence
• Big Data Analytics
• Semantic Web
• +4 more

Prof. Dieter De Witte serves as a Professor at Ghent University, dedicating 50% of his time to the Internet Technology and Data Science Lab (IDLab) while simultaneously contributing 50% to the Royal Museums of Fine Arts Belgium (RMFAB) in Brussels through a FED-tWIN mandate from Belspo. At RMFAB, he spearheads the strategic overhaul of digital infrastructure toward FAIR-compliant and data-driven systems, while at Ghent University he collaborates with Prof. Steven Verstockt on applied AI projects across heritage, mental healthcare, and education domains. His academic foundation includes a Master's in Engineering Physics from Ghent University (2008) followed by doctoral research on Big Data technologies and FAIR data for life sciences. Prior to returning to academia in 2021, he gained industry experience as an AI consultant and team lead at Telenet and Ordina. De Witte's research centers on AI-driven transformation of cultural heritage through FAIR data publication , collection enrichment , and intuitive querying interfaces . His technical expertise spans multimodal algorithms, image segmentation, pose estimation, large language models (LLMs), and semantic technologies including SPARQL and IIIF. Current projects focus on human-in-the-loop AI systems that combine diverse AI building blocks for practical heritage applications. Analysis of his 15 most recent publications reveals a clear trajectory from early bioinformatics work (2007-2018) on genomic motif discovery and life sciences data infrastructure toward contemporary cultural heritage applications (2023-2024). Recent outputs demonstrate innovative fusion of pose estimation, linked data frameworks, and multimodal AI for museum contexts, highlighting increasing specialization in AI enrichment of digital collections while maintaining core expertise in FAIR data principles. His FED-tWIN grant enables critical knowledge transfer between academic research and cultural heritage institutions, supporting development of next-generation digital infrastructure at RMFAB. Current projects involve creating AI tools for intuitive collection exploration and systematic enrichment of heritage assets through advanced computational methods. De Witte operates within Ghent University's Internet Technology and Data Science Lab (IDLab), participating in interdisciplinary teams developing applied AI solutions. His work bridges technical innovation with practical implementation in cultural institutions, focusing on sustainable, interoperable systems that enhance public access to digital heritage collections through cutting-edge AI interaction paradigms.

• Artificial Intelligence
• Data Science
• FAIR Data
• +3 more

Femke Ongenae is an Associate Professor at Ghent University's Faculty of Engineering and Architecture within the Department of Information Technology . She leads research at the IMEC postdoctoral level in areas bridging eHealth, predictive healthcare, and knowledge graph technologies . Her work focuses on context-aware systems, stream reasoning, and hybrid AI for healthcare and smart infrastructure applications. Key research domains: Artificial Intelligence , Health Informatics , Knowledge Graphs Leadership roles: Digital Innovation for Man and Society research unit, eBehaviourChange group Her recent publications (2023-2025) highlight advancements in: Semantic rule mining for decision support systems Anomaly detection in healthcare and water networks Context-aware machine learning for COPD and migraine monitoring Knowledge graph embeddings for industrial process monitoring Collaborative projects involve: Developing INK framework for knowledge graph rule mining Building DIVIDE system for adaptive IoT querying Creating MASSIF platform for semantic IoT services Advancing stream reasoning for real-time healthcare applications

• Artificial Intelligence
• Health Informatics
• Data Analytics
• +9 more

Pieter Bonte is a FWO Senior postdoctoral fellow and IMEC Postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology. His research focuses on Semantic Web technologies, stream reasoning, and Internet of Things applications. His research interests span Semantic Web, Internet of Things, Stream Reasoning, Knowledge Graphs, Context-aware Systems, RDF Processing, Linked Data, and Healthcare Informatics. His work bridges theoretical semantic technologies with practical applications, particularly in healthcare and IoT domains. Bonte's publication record shows a strong focus on streaming data processing, with numerous papers on Streaming Linked Data, context-aware query derivation, and semantic reasoning frameworks. His research demonstrates a progression from foundational semantic web technologies toward practical implementations in healthcare and IoT applications, with an increasing emphasis on privacy considerations and efficient processing techniques. His work frequently involves collaborations with Femke Ongenae, Filip De Turck, and other researchers at Ghent University and IMEC, indicating strong institutional research networks. His publications appear in respected venues including the Journal of Web Semantics, Semantic Web Journal, and various conference proceedings in the semantic technologies field.

• Semantic Web
• Internet of Things
• Stream Reasoning
• +5 more

Bart Thijs is an Assistant Professor at the Faculty of Economics and Business of KU Leuven , Belgium. He is affiliated with the Department of Management, Strategy and Innovation and serves as a research expert at ECOOM , the Research and Development Monitoring Research Centre. He is also a member of Leuven.AI , the KU Leuven Institute for Artificial Intelligence, and participates in various faculty councils and program committees. Research Focus His research lies at the intersection of quantitative science studies , social network analysis , and interdisciplinary research evaluation . He employs advanced bibliometric and AI-based techniques to study knowledge networks, citation patterns, and the impact of interdisciplinary collaboration. His work often involves the development of new indicators and methodologies for assessing scientific output and collaboration. Projects & Collaborations NIKAW (2023–2027): Tracing the Impact of Change on Citation Networks in Ancient Literary Texts using Social Network Analysis. NIKAW (2022–2026): Networks of Ideas and Knowledge in the Ancient World. AI & Ancient World Scholarship (2021–2023): Linking traditional scholarship with digital tools via AI. Interdisciplinary Research & Impact (2020–2025): Co-promoting interdisciplinary initiatives. Teaching He teaches courses such as Analysis of Large Scale Social Networks and Advanced Research Methods in Quantitative Science and Technology Studies , contributing to both methodological and domain-specific education in scientometrics and network science.

• Quantitative Science Studies
• Social Network Analysis
• Interdisciplinary Research
• +5 more

Antoon Bronselaer is an Assistant Professor at Ghent University's Department of Telecommunications and Information Processing within the Faculty of Engineering and Architecture. He is an active member of the Database, Document and Content Management (DDCM) research group, focusing on advancing database theory and applications. His research centers on data quality (particularly measurement models and improvement techniques), temporal databases , data fusion , and uncertainty management in databases . Key contributions include measure-theoretic foundations for data quality assessment, dynamical order construction in data fusion, and predicate enrichment techniques for wrapper induction. Analysis of his recent publications reveals consistent innovation in data cleaning algorithms (e.g., Swipe framework), temporal data modeling, and orthographic similarity measures for graph-based text representations. His work increasingly bridges database theory with healthcare applications (e.g., statin-gait relationship studies) and digital humanities (Byzantine epigrams project). No scientific awards are mentioned in the source materials. Dr. Bronselaer's academic guidance includes formal advisees at Ghent University, though specific student names aren't documented in the provided texts. His research is supported through university positions and likely Belgian/Flemish research grants given his publication venues. Current projects involve automated news update analysis, persistent identifier validation, and syntactic interoperability frameworks. He leads research within the DDCM group, specializing in data quality measurement systems and temporal data management solutions. Recent work demonstrates expansion into healthcare data analytics and cultural heritage informatics while maintaining core expertise in database theory.

• Data Quality
• Data Fusion
• Uncertainty Management
• +2 more

Bart Bogaerts is a Professor at the Vrije Universiteit Brussel , affiliated with the Federated Labs AI and Robotics and the Department of Informatics and Applied Informatics . His research centers on Approximation Theory , Logic Programming , Knowledge Representation , and Constraint Satisfaction Problems , with a focus on formalizing reasoning methods and optimizing computational systems. His recent publications emphasize certified algorithms, semantic web traversal, and formal verification, reflecting collaborations with institutions like KU Leuven and Maastricht University. Key themes include: Algorithm Certification : Integrating Coq for verified logic programming and optimization techniques. Web Technologies : Distributed subweb specifications and query processing frameworks. Nonmonotonic Reasoning : Advancing approximation fixpoint theory in knowledge representation. Scientific recognitions include the AAAI 2022 Distinguished Paper Award and the IJCAI 2021 Distinguished Paper Award . Dr. Bogaerts supervises PhD students such as Samuele Pollaci, Robbe Van Den Eede, and Dirk Vandesande in joint programs with VUB and KU Leuven.

• Approximation Theory
• Semantics
• Constraint Satisfaction Problems
• +4 more

Eljas Oksanen is a Postdoc Research Fellow at the University of Reading , focusing on the Medieval Ritual Landscape: Archaeology & Lived Religion project. He holds a PhD in History from the University of Cambridge (2007) and was awarded the title of Adjunct Professor in Archaeology by the University of Helsinki in 2024. Oksanen's research integrates history , archaeology , Digital Humanities , and Citizen Science to explore data-driven approaches to historical and archaeological materials. His recent work examines how digitized cultural heritage is disseminated and consumed in contemporary society, emphasizing collaborative methodologies and intersectoral partnerships. Key publication themes include semantic technologies for democratizing data analysis, public archaeology , and metal detecting as a tool for community-driven heritage research. His projects often bridge technical innovation with sociocultural engagement, leveraging linked open data and digital preservation frameworks. Scientific Awards Adjunct Professor (Dosentti), Archaeology, University of Helsinki (2024)

• History
• Archaeology
• Digital Humanities
• +4 more