Lucas Alexander Kock is an Instructor at the Department of Computer Science , University of Copenhagen . His research spans Machine Learning and its applications in diverse domains including medical data analysis, quantum computing, and sustainable AI. Role: Lecturer in Machine Learning Affiliation: SCIENCE AI Centre, University of Copenhagen Research interests focus on: Quantum machine learning Neuroscience applications Cross-cultural AI systems Environmental sustainability in computing Medical informatics Deep learning explainability Recent publications demonstrate expertise in quantum computing applications , neural signal interpretation , and ethical AI frameworks . No formal awards or advisees are listed in available public data.
Henning Christiansen is a Professor at Roskilde University's Department of People and Technology, affiliated with the Programming, Logic and Intelligent Systems (PLIS) research group. He is also a Knight/Chevalier of the Dannebrog (2018) and serves as Coordinator for International Student Exchanges in Computer Science, Informatics & Humanities-Technology Studies. His research spans Deep Learning for medical diagnosis, Robotics in theatrical performances, Constraint Logic Programming, Probabilistic-logic models, Natural Language Processing, Logical methods for context comprehension, Interactive art installations, Database query systems, Cultural technology projects like Viskbook. Key projects include SEAFACTS (digital maritime history platform), EXPLAIN-ME (explainable AI in medical education), NDH (cross-border health data collaboration). Publications highlight contributions to AI ethics, robot choreography, medical image analysis, constraint-based formal methods. He has supervised over 16 projects and 285+ activities, including international conferences and exhibitions. His photography has been displayed in Roskilde libraries and cultural venues.
Hua Lu is a Professor in the Department of People and Technology Programming, Logic and Intelligent Systems at Roskilde University in Denmark. His research focuses on computer science with specialties in database systems, data mining, spatial data processing, and AI/ML applications for databases. He leads projects on AI-powered spatial databases and data-driven systems. His research interests span multiple domains including data science fundamentals, IoT data streams, location-based services, and database optimization techniques. Recent publications demonstrate strong focus on spatial-temporal data processing, machine learning integration with databases, and practical applications of data science. Professor Lu has received multiple scientific awards including Best Paper nominations and runner-up awards at premier conferences, recognizing his contributions to database and mobile computing research.
Zoi Kaoudi is an Associate Professor at the IT University of Copenhagen, affiliated with the Data, Systems, and Robotics school and the Data-intensive Systems and Applications department. Her research focuses on advancing data systems, knowledge graphs, and large-scale data analysis. She leads the Rank4QO project (2024–2027), funded by the Carlsberg Foundation, which explores query optimization using ranking algorithms. Her work emphasizes machine learning integration in data management systems, including frameworks like Apache Wayang, which unifies diverse data analytics platforms. Key collaborations include projects with Volkswagen Group and SAP, addressing dynamic graph processing and knowledge graph embeddings. Notable contributions include innovative approaches to parameter management (e.g., Good Intentions ), automated data science pipelines ( DORIAN ), and efficient graph processing algorithms. Her recent publications (2023–2025) highlight advancements in machine learning systems, distributed data processing, and adaptive optimization techniques. Current projects aim to bridge machine learning and data management through frameworks like Wayang and Dorian, with applications in air cargo revenue management and semantic web systems.
Tanja Svarre is an Associate Professor in the Department of Communication and Psychology at Aalborg University, Denmark. She holds a PhD from 2012 and an MLIS from 2004. Her research focuses on information practices, AI-driven systems in professional contexts including healthcare and public administration, and workplace digitalization challenges. She leads the Purposeful Technology Lab (PTL) and chairs the HUM-AI cluster. Education: PhD: Automatic indexing in e-government (Aalborg University, 2012) Master of Library and Information Science (Royal School of Library and Information Science, 2004) Research Interests: Employee information behavior in professional settings, evaluation of AI-based systems, enterprise search interfaces, and digital government strategies. Her work addresses challenges in health, corporate, and educational sectors. Key Projects: Leading the 'Digital connectivity in the Danish public sector' initiative (2018–present) Contributing to the TAMAI project on AI-driven management roles (2024–2025) Investigating AI ethics and workplace automation through the Danish Centre for Health Informatics Advising & Grants: Supervised projects on generative AI in healthcare (2023), web corpus development (2024), and leadership communication on social media (2025). Active in securing interdisciplinary grants across informatics, management, and education. Labs/Teams: Co-leads the Purposeful Technology Lab and collaborates with the CHAP research group studying digital transformation processes.
Theis Erik Jendal is a Researcher at the Department of Computer Science, Aalborg University, affiliated with the Technical Faculty of IT and Design. He specializes in Recommender Systems and Knowledge Graphs, focusing on areas like explainable AI, graph neural networks, and inductive recommendation architectures. He participates in the Poul Due Jensen Professorate in Big Data and Artificial Intelligence (2019–2025), addressing challenges in query processing, knowledge graphs, semantic web, and open data. Key research interests include hypergraph models for explainable recommendation, gated architectures in knowledge graphs, and addressing challenges in knowledge graph embeddings. His work emphasizes interpretability, similarity search, and practical use cases in AI systems. Contributions include 5 peer-reviewed publications since 2020, spanning conferences like ECIR and CIKM, and a dataset contribution to the Yelp Collaborative Knowledge Graph (Zenodo, 2023). His research bridges theory and application, particularly in improving recommendation systems through advanced graph-based methodologies.
Christian S. Jensen is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His primary research focuses on data management, spatiotemporal systems, and AI-driven solutions for mobility and cyber-physical systems. He leads projects like DiCyPS (Data-Intensive Cyber-Physical Systems) and MALOT (Managing Mobility Data Quality for Location of Things). He has published over 700 papers, with recent work emphasizing time series forecasting, trajectory analysis, and edge computing. Notable contributions include frameworks like Memory Guided Transformers and TEAM for traffic prediction. His work has been recognized with awards including the IEEE TCDE Impact Award (2019) and the Order of Dannebrog (2016). Jensen actively collaborates internationally, holding roles in organizations like the Max Planck Institute and the Villum Foundation. His research bridges theory and practice, addressing real-world challenges in smart cities, energy systems, and autonomous driving.
Christian Graugaard is a Professor of Sexology at Aalborg University, affiliated with the Faculty of Medicine and the Department of Clinical Medicine. He is a key member of the Center for Sexology Research and leads Project SEXUS, aiming to study Danish sexual behavior comprehensively. His work emphasizes the intersection of biological, psychological, and cultural factors in human sexuality, challenging simplistic gender-based stereotypes. Research interests include gender differences in sexual behavior, societal norms influencing sexual health, and the cultural dimensions of human sexuality. He actively participates in public discourse, as seen in his DR-podcast interview on 'Ramt af kærlighed,' where he discussed the complex interplay between biology and culture in shaping sexual identities. Though no specific awards or grants are detailed here, his publications span advanced technical domains like spatiotemporal data analysis, federated learning, and trajectory modeling, suggesting interdisciplinary research collaborations. His work on systems like OneDB and SWASH highlights contributions to distributed computing and data science, which may underpin his methodologies in large-scale sexual behavior studies. He currently holds no listed students or formal advisees in the provided texts, and his involvement in labs/teams is limited to the Center for Sexology Research and Project SEXUS.
Junya Shiraishi is a Postdoctoral Researcher (Marie Curie Fellow) at the Department of Electronic Systems, Technical Faculty of IT and Design, Aalborg University, Denmark. His research focuses on energy-efficient communication systems for the Internet of Things (IoT) and wireless sensor networks. Dr. Shiraishi's research spans wireless communications and IoT, emphasizing energy efficiency, content-based wake-up systems, and pull/push communication coexistence. His work addresses critical challenges in reducing sensor node energy consumption while ensuring timely data retrieval in IoT applications through innovative networking protocols. His recent publications (2023-2025) demonstrate a cohesive research trajectory centered on energy-efficient IoT protocols, featuring top-k query processing, multi-hop wake-up control, and AI integration for 6G systems. These works bridge theoretical networking concepts with practical sensor network deployments, showing increasing focus on AI-driven solutions for next-generation wireless systems. Scientific Awards: Marie Curie Postdoctoral Fellowship Supported by the Marie Curie Fellowship, Dr. Shiraishi conducts cutting-edge research in EU-funded projects. No student advisement information is available in current records. Dr. Shiraishi collaborates internationally with researchers across Europe and Japan within the Connectivity section at Aalborg University, contributing to global initiatives in 6G wireless systems and IoT standardization.
Rolf Fagerberg is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU). His research centers on algorithms, data structures, and their applications in computational biology and cheminformatics. His research interests include: Algorithms and data structures, particularly dynamic and geometric data structures Graph theory with emphasis on subgraphs, Yao graphs, and hypergraphs Algorithmic cheminformatics and modeling of chemical reaction networks Computational biology, including metabolic pathway analysis Theoretical computer science and discrete mathematics Recent publications highlight a strong trend toward interdisciplinary research combining computer science with chemistry and biology, particularly in modeling chemical reaction networks using hypergraphs and mixed-integer linear programming. His work also includes algorithmic solutions for palindromic subsequence problems and efficient extraction of reaction rules from large databases, reflecting a blend of theoretical and applied algorithm design. Rolf Fagerberg has served as senior coordinator on multiple research projects, including 'Algorithmic Cheminformatics' and 'Fundamental Data Structures', funded by the Danish Ministry of Higher Education and Research. He has also contributed to peer review and editorial work for major conferences such as the ACM Symposium on Parallelism in Algorithms and Architectures and the International Symposium on Experimental Algorithms. He has supervised PhD students and is actively involved in academic service, including membership in assessment committees at Aarhus University and IT University of Copenhagen. His research has received media attention, including coverage of a 'mathematical breakthrough' and applications in understanding intestinal systems in obesity.
Frans van der Sluis serves as an Associate Professor in the Department of Communication at the University of Copenhagen, where he conducts interdisciplinary research at the intersection of human cognition and information systems. His work focuses on designing search and filtering technologies that align with human learning processes and curiosity-driven exploration. Education: BSc in Psychology (2006) BSc in Business Information Technology (2006) MSc in Psychology (2008) PhD in Interactive Information Retrieval and Filtering (2013) Van der Sluis investigates how information systems can be optimized to transform raw data into meaningful knowledge through understanding epistemic emotions, user uncertainty, and cognitive processing during information interactions. His methodology frequently incorporates eye-tracking and empirical user studies to measure comprehension, interest, and engagement beyond traditional accuracy metrics. Analysis of his recent publications (2023-2025) reveals a consistent trajectory toward conversational approaches in information quality assessment, with growing applications in sustainability domains like wind energy training networks and ethical consumer behavior. His work bridges theoretical HCI frameworks with practical implementations across diverse contexts from social media to specialized research environments. No scientific awards were documented in the source materials. While specific advising details remain unspecified, his collaborative projects—including the EU-funded Train2Wind initiative for offshore wind power researchers—demonstrate active grant management and cross-institutional leadership. His professional network spans major European universities and industry research centers. Van der Sluis maintains active collaborations with University of Twente, Glasgow University, Leiden University, and industrial partners including Philips' Innovation Lab, reflecting his position at the nexus of academic research and real-world information system design.
Antonius Marinus Bogers serves as Assistant Professor at the Royal School of Library and Information Science (RSLIS), University of Copenhagen, a position held since December 2011 following his 2009 appointment. His research focuses on applying information access technologies to unlock large collections through recommender systems, social bookmarking, and information retrieval innovations. His academic foundation includes: Ph.D. in Computational Linguistics and Artificial Intelligence from Tilburg University (2009) M.A. in Information Management (2001) and M.A. in Computational Linguistics & AI (2004) from Tilburg University Dr. Bogers investigates how search, browsing, and recommendation technologies enhance information discovery, with particular emphasis on serendipity in social contexts. His work bridges theoretical information science with practical applications for social media and digital archives, examining how users encounter meaningful information unexpectedly through systems like Twitter and social bookmarking platforms. This research trajectory demonstrates consistent evolution from foundational recommender system work toward complex social information behavior analysis. His 2011-2013 publications reveal a concentrated exploration of social information access mechanisms, particularly how metadata schemes, recommendation fusion, and social sharing platforms facilitate serendipitous discovery. This body of work shows methodological progression from controlled laboratory experiments measuring serendipity to real-world analysis of Twitter micro-serendipity and radio archive metadata development. Dr. Bogers actively mentors the next generation of information scientists through comprehensive thesis supervision across diverse topics including social news motivation, virtual museum engagement, and expert search systems. His teaching portfolio spans core information architecture, digital knowledge systems, and specialized courses on collective intelligence and cultural heritage unlocking. He contributes to the research ecosystem through project leadership in initiatives like LARM (radio broadcast archives access) and RIX (search engine fusion), while maintaining active participation in professional communities including ACM and BCS-IRSG. His technical expertise spans scientific programming for citation analysis and metadata implementation, demonstrated through collaborations with organizations like CiteULike and the Danish Ministry of Agriculture. These practical engagements complement his theoretical contributions to information access research.
Mikkel Kragh Mathiesen is a Guest Researcher in the Programming Languages and Theory of Computation group at the Department of Computer Science, Faculty of Science, University of Copenhagen. His research focuses on algebraic foundations of programming languages, reversible computation, and database theory, with significant contributions to finitary relational calculus and algebraic query processing. He completed his Ph.D. in Computer Science at the University of Copenhagen in 2023 with the dissertation "The Programming of Algebra". His academic work bridges theoretical computer science and abstract algebra, emphasizing formal methods and computational semantics. Mathiesen's research interests center on applying algebraic structures to programming language design, particularly in reversible systems (Algeo framework) and database query optimization. His work explores category-theoretic approaches to differentiation in functional programming and module-theoretic foundations for relational calculus, demonstrating strong interdisciplinary connections between mathematics and computation. Analysis of his 2020-2025 publications reveals consistent focus on algebraic methods across theoretical domains. Key trends include developing finitary relational calculus for database programming languages, creating algebraic frameworks for reversible computation, and establishing theoretical connections between module theory and query processing. His research shows increasing integration of category theory with programming language semantics, particularly in automatic differentiation and higher-order function systems. No scientific awards were mentioned in the provided information. There is no available information regarding students advised or research grants administered by Mathiesen. His collaborative work primarily involves co-authorship with established researchers in theoretical computer science without indication of independent supervision or grant leadership. He is actively affiliated with the Programming Languages and Theory of Computation research group at the University of Copenhagen's Department of Computer Science, contributing to their focus on foundational aspects of computation and language design through algebraic approaches.
Ioannis Panagis serves as a Data Specialist at the iCourts Centre of Excellence for International Courts and Governance within the Faculty of Law at the University of Copenhagen. Holding a PhD in Computer Engineering from the University of Patras, he bridges computational methodologies with legal scholarship through interdisciplinary research in legal network analysis and data science applications. Education: PhD in Computer Engineering, University of Patras Research Focus: Panagis specializes in social network analysis of legal citation systems, particularly within European Union law, and develops data science techniques for legal text analysis. His work integrates computer science principles with jurisprudence to examine case law structures, judicial influence patterns, and digital platform regulation. Key methodologies include network metrics, text similarity algorithms, and cloud-based processing of legal datasets. Research Evolution: His publication trajectory (2015-2023) demonstrates progression from foundational studies on EU case law citation networks toward advanced computational approaches. Early work established frameworks for multi-dimensional legal network analysis, while recent research (2023) pioneers paragraph-level citation prediction models. This evolution reflects growing sophistication in applying machine learning and natural language processing to complex legal phenomena. Professional Engagement: As Editor of Social Network Analysis and Mining since 2020, Panagis contributes to scholarly discourse in network science. His research outputs have accumulated significant citations (notably 48 for his 2017 consumer law study) and demonstrate impact through policy references and academic discussion. He remains actively affiliated with iCourts, leveraging computational approaches to advance understanding of international judicial systems.
