Anna Rogers is an Associate Professor of Data Science at the IT-University of Copenhagen , affiliated with the NLPnorth research group. Her work focuses on Natural Language Processing (NLP) , Artificial Intelligence , and Large Language Models (LLMs) , with a particular emphasis on ethical data use, peer review innovation, and transformer model analysis. She leads projects addressing AI transparency, medical QA hallucinations, and generative AI applications. Her research explores topics including: LLM behavior and evaluation Data governance in NLP Peer review systems optimization Transformer model robustness Medical AI applications Key Projects : PlagAIrism : Tracking LLM training data origins Pioneer Centre for AI : Pre-registered replication studies TinyGPT : Efficient NLP models AIInterviewer : Large-scale qualitative data collection Publications span ACL , EMNLP , and specialized NLP workshops, addressing topics from BERT analysis to AI content farms.
Sarah Frances Homewood is an Assistant Professor (Tenure Track) in the Department of Computer Science at the University of Copenhagen, affiliated with the Human-Centred Computing research section. Her research focuses on the intersection of human-computer interaction and artificial intelligence, with applications in healthcare, natural language processing, and interpretable machine learning. Her diverse research interests span Human-Computer Interaction, Machine Learning, Natural Language Processing, and Artificial Intelligence. Recent investigations include interpretability of large language models, clinical NLP applications, fairness in recommender systems, and quantum natural language processing. Analysis of her recent publications reveals strong emphasis on NLP interpretability techniques, healthcare applications of AI, and theoretical foundations of machine learning. Her work frequently bridges fundamental computer science with practical applications in medicine and human-centered systems. Emerging research directions include quantum NLP and protein sequence modeling. Dr. Homewood's research contributes to the Machine Learning Section's focus on both theoretical foundations and applied domains including medical data analysis and information retrieval.
Julius Koschnick serves as an Assistant Professor (tenure track) in the Department of Economics at the University of Southern Denmark, where he is affiliated with the Historical Economics & Development Group (HEDG). His research examines historical knowledge economies, focusing on how Scientific Revolution ideas were adopted and influenced useful knowledge stocks during industrialization periods. Education: Ph.D. in Economics, London School of Economics and Political Science (2023) Koschnick's work bridges economic history and history of science through quantitative analysis of knowledge transmission. He investigates agglomeration effects in late 18th-19th century Germany, vocational school impacts, and how scientific institutions like economic societies facilitated technological progress. His methodology uniquely combines applied microeconomics with natural language processing to analyze historical texts, revealing patterns in knowledge spillovers across industrializing regions. Recent publications demonstrate consistent focus on knowledge flow mechanisms during pivotal industrial transitions. His 2025 Economic Journal article quantifies how economic societies accelerated useful knowledge diffusion, while his working paper on teacher-directed scientific change employs difference-in-differences analysis of English educational reforms. Collectively, his work establishes causal links between scientific institutionalization and industrial innovation. Koschnick teaches 'Trends in Applied Economics' (2024) and actively disseminates research through international conferences, with no public information on grants or student advising. As a core member of HEDG, he contributes to collaborative projects analyzing historical economic development through computational methods, maintaining strong engagement with the European Historical Economics Society.
Jens Myrup Pedersen is a Professor at Aalborg University's Department of Electronic Systems within The Technical Faculty of IT and Design. He is affiliated with the Cyber Security Group and focuses on improving digital wellbeing through cybersecurity research. His primary research interests include botnets, network security, machine learning applications in cybersecurity, and cybersecurity education. He leads or participates in projects such as Cyber Safe Robotics , AI:SECURITY , and GAMESS , addressing topics like AI-driven security, gamification in education, and secure software development. Pedersen has contributed to over 235 publications since 2003, emphasizing cybersecurity threats, network analysis, and educational methodologies. His work extends to cybersecurity training platforms like Haaukins and The Privacy Universe , designed to enhance user awareness through gamification. Pedersen collaborates internationally, engaging in initiatives like the European Cyber Security Challenge and cybersecurity hackathons. He holds roles in professional organizations such as the Danish Cybersecurity Board and the IDA association. Recent research highlights include NLP security ethics, OT cyber resilience, and cryptocurrency forecasting tools. His projects often bridge academia and industry, focusing on real-world impact through student-driven projects and cross-disciplinary collaborations.
Abdulkadir Celikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design and the Data, Knowledge and Web Engineering research group. His research focuses on graph representation learning, network analysis, bioinformatics, and machine learning applications in dynamic systems. Key projects include the Villum Foundation-funded 'DarkScience: Illuminating microbial dark matter through data science,' which explores metagenomic binning and microbial ecology using advanced data science techniques. He has been recognized with the Best Paper Award (2023) for contributions to temporal graph analysis and modeling. His work spans continuous-time dynamic node representations, scalable genome profiling, and polarization detection in social networks. Celikkanat collaborates widely, contributing to interdisciplinary research at the intersection of computer science, biology, and environmental science. Recent publications highlight innovations in graph embeddings, citation network modeling, and hybrid membership latent distance models. His research addresses challenges in low-dimensional graph representations, efficient kernel methods, and integrating biological networks for protein analysis.
Dimitris Chrysostomou is an Associate Professor in the Department of Materials and Production at Aalborg University, Denmark. He leads the Robotics & Automation Group and directs the AI:Cybernetics Lab. His work focuses on developing safe, intuitive robotic systems for industrial and social contexts, emphasizing human-robot interaction (HRI), AI ethics, and collaborative robotics. Chrysostomou has over 15 years of research experience, funded by EU frameworks and national grants, with over 70 peer-reviewed publications. Education: PhD in Robot Vision (2013) and Diploma in Production Engineering (2006) from Democritus University of Thrace. He coordinates courses in robotics and manufacturing technology, emphasizing problem-based learning models. Administrative roles include heading the Robotics and Automation Group and the Aalborg Robotics Challenge steering committee. Research interests span AI-driven robotics, ethical implications of robot behavior, and HRI evaluation methodologies. Key projects include SAPIENT (2025-2027) for robotic intelligence and RIACT (2024-2025) on collaborative robot technology. Editor-in-Chief of *Industrial Robot* journal, IEEE Senior Member, and leader in euRobotics standardization initiatives. Notable contributions include virtual assistants for industrial robots, trust evaluation frameworks in HRI, and energy-based approaches for collaborative robotics. Active in conference organization, editorial roles, and industry collaborations, including co-founding AI startups.
Markus Strohmaier is Professor and Chair of Data Science in the Economic and Social Sciences at the University of Mannheim, with affiliations as Scientific Coordinator at GESIS – Leibniz Institute for the Social Sciences and External Faculty Member at the Complexity Science Hub Vienna. His interdisciplinary work bridges computer science, economics, and the social sciences. University of Mannheim – Chair for Data Science in the Economic and Social Sciences GESIS – Scientific Coordinator for Digital Behavioral Data Complexity Science Hub Vienna – External Faculty Former Professor at RWTH Aachen University and University of Koblenz-Landau Previous Post-Doc and Visiting Roles at Stanford University, Xerox PARC, University of Toronto, and Graz University of Technology His research focuses on computational social science , algorithmic fairness , network science , and the modeling of human behavior using machine learning and large-scale data. He develops methods to analyze textual, relational, and emerging data types to understand socioeconomic systems and digital societies. The recent articles reflect a strong trend in studying inequality in algorithmic systems , governance in decentralized organizations (DAOs) , and psychological profiling of AI . His work spans high-impact journals like Nature and Scientific Reports , emphasizing fairness, transparency, and societal impact of data-driven technologies. Notable scientific contributions include: Editor-in-Chief of EPJ Data Science (2018–2022) Founding co-chair of the Computational Social Science section of the German Informatics Society He advises students and leads research projects on algorithmic fairness, digital governance, and behavioral modeling. His team engages in both fundamental methodological development and applied studies in real-world digital platforms. He has been involved in significant grants and collaborative initiatives around digital behavioral data and computational social science infrastructure. His lab and projects include the Algorithmic Fairness initiative and the interactive visualization tool Planets of Disparity , which explores how algorithms behave on different network structures. These efforts aim to enhance public understanding and technical scrutiny of algorithmic systems.
Patrizia Paggio serves as an Associate Professor and Senior Researcher within the Department of Nordic Studies and Linguistics at the University of Copenhagen's Faculty of Humanities, concurrently holding a full professorship at the University of Malta's Institute of Linguistics and Language Technology since September 2011. Her scholarly work centers on the intricate relationship between verbal and nonverbal communication modalities, with international recognition for advancing methodologies in multimodal analysis. Academic Background: PhD in Computational Linguistics from the University of Copenhagen (1997), dissertation: "The Treatment of Information Structure in Machine Translation" Professor Paggio's research program investigates how gestures, head movements, and other nonverbal cues interact with spoken language to construct meaning in natural communication. She has pioneered methodologies for constructing and analyzing multimodal corpora, while maintaining technical expertise in machine translation systems, grammar engineering, and content-based querying frameworks. Her theoretical work spans formal syntactic structures, discourse phenomena, information packaging, and ontological representations for linguistic data, consistently bridging computational methods with linguistic theory. Analysis of her recent publications (2020-2025) reveals a sustained focus on computational approaches to nonverbal communication, particularly the automatic detection and annotation of head movements and gestures in both physical and digital environments. Key contributions include the GEHM Zoom corpus for online interaction analysis, eye-tracking studies of emoji processing, and diachronic modeling of historical language change. Her work strategically integrates eye-tracking, corpus linguistics, and machine learning techniques, establishing her at the convergence of linguistic theory, cognitive science, and artificial intelligence applications. Professional Leadership: Coordinator of the international GEHM (Gestures and Head Movements in Language) research network Organizer of MULTIMODAL CORPORA 2018, 4th European/Nordic Symposium on Multimodal Communication, and LREC2022 Workshop on People in Vision, Language and the Mind
Mohammad Naser Sabet Jahromi is an Assistant Professor at the Department of Architecture, Design and Media Technology, Aalborg University, Denmark. He is affiliated with the Visual Analysis and Perception Centre for AI Ethics, Law and Policy. His research focuses on explainable AI (XAI), biometrics, machine learning, and ethical AI applications in legal and medical domains. He actively participates in interdisciplinary projects like REPAI: Responsible AI for Value Creation (2023-2027), which explores AI ethics, computational discourse analysis, and value-driven AI systems. His educational background is not explicitly detailed in the provided text, but his research trajectory indicates strong expertise in computer science and AI systems. Key research interests include interpretable machine learning models, privacy-preserving biometric systems, and AI applications in asylum adjudication and educational assessment. Recent work emphasizes developing XAI frameworks like SIDU-TXT for NLP, verifying machine unlearning mechanisms, and automating large-classroom assessments. His projects bridge technical AI advancements with societal implications through collaborations with legal and ethical scholars. Notable contributions include datasets evaluating XAI methods in medicine and methodologies for transparent AI decision-making. He has participated in conferences such as ICPR 2024 and JURISIN 2023, showcasing interdisciplinary research impact.
Arianna Pera is a PhD student in Computational Social Science at the IT University of Copenhagen , affiliated with the NERDS research group . She has completed a visiting research stay at the School of Information, University of Michigan (until December 2024) and is currently collaborating with Central European University in Vienna. Education : MSc in Data Science from the University of Milano-Bicocca , with a thesis on NLP applications to bias analysis in online discourse. Her research explores social media communication in political and social contexts, focusing on collective action dynamics , discourse framing , and swing voter identification . Current projects include analyzing climate delay discourses in UK Parliament speeches and labor movement communication in the US. Recent work includes a 2025 AAAI ICWSM paper on social media-based collective action extraction and a 2024 arXiv preprint on hidden swing voters in Italian elections. Her methodologies integrate NLP , social network analysis , and propaganda detection . Labs/teams : NERDS research group at ITU Networks and Data Science Department at Central European University Collaborative work with Prof. Luca Maria Aiello (COCOONS project) and Ceren Budak (University of Michigan)
Christina Lioma is a Full Professor at the Department of Computer Science (DIKU), University of Copenhagen . She has held academic positions including Associate Professor (2014-2018) and Freja Fellow/Assistant Professor (2012-2013) at the same institution. M.Hons (University of Glasgow, 2001) M.Sc. (University of Manchester, 2003) Ph.D (University of Glasgow, 2007) Her research focuses on Information Retrieval , Text Analytics , and Recommender Systems within Applied Machine Learning and Natural Language Processing . Recent work examines fairness-relevance tradeoffs in recommendation systems and neural mechanisms for knowledge conflict tracing. Recent publications in Nature Communications and top conference proceedings (WWW, SIGIR) explore hybrid computation architectures, brain-based language generation, and fairness evaluation metrics. She actively participates in academic conferences as organizer and speaker, including the European Conference on Information Retrieval.
Lasse Bjørn Kristensen is a Research Fellow at the Department of Computer Science, University of Copenhagen, specializing in Machine Learning with a focus on quantum computing applications. Research Interests His work bridges quantum computing, machine learning, and computational biology, with contributions to: Quantum neural networks and spiking neurons Quantum error correction and circuit robustness Quantum chemistry simulations Information flow in parametrized quantum systems Notable Research Trends Kristensen's publications reveal a strong emphasis on quantum-classical hybrid models, entanglement-enhanced devices, and computational methods for chemistry and physics. His recent work explores error-driven learning paradigms and quantum eigensolvers. Contact Email: lakr@di.ku.dk Address: Universitetsparken 1, 2100 Copenhagen Ø
Nico Lang is an Assistant Professor at the University of Copenhagen's Department of Computer Science, associated with the Pioneer Centre for AI and Global Wetland Centre. He holds a PhD from ETH Zurich where he developed methods for global forest structure mapping. His research bridges computer vision, machine learning, and remote sensing to address environmental challenges like deforestation monitoring and climate change mitigation. Lang's research focuses on: Developing probabilistic deep learning models for global canopy height estimation Advancing open-set recognition under adversarial conditions Creating multi-modal representation learning frameworks for geospatial data Applying computer vision to biodiversity monitoring and conservation His work frequently appears in top venues like Nature, CVPR, and ECCV. His publications show strong emphasis on: environmental applications of AI, uncertainty-aware deep learning, and global-scale geospatial analysis. Recent work explores vision-language models and fine-grained open-set recognition. Awards & Honors: Culmann Prize for outstanding doctoral thesis (2023) Outstanding Reviewer for CVPR 2023 U.V. Helava Award for best paper in ISPRS Journal (2019) Associate PhD Fellow at Max Planck ETH Center (2018) Collaborations & Labs: Directs research at the intersection of computer vision and environmental science. Key affiliations include NASA GEDI mission, Swiss Federal Institute for Forest, Snow and Landscape Research, and the Pioneer Centre for AI. Organizes workshops like FGVC at CVPR and SSL4EO summer schools.
William Henrich Due serves as a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Machine Learning section. His work intersects with the SCIENCE AI Centre and leverages the department's high-performance compute cluster for research in quantum computing, sustainable AI, and medical applications. Research focuses span quantum machine learning (biomolecular simulations, photonic processors), sustainable AI systems (energy efficiency, climate impact), and clinical applications (EEG analysis, medical imaging). His recent publications reveal strong activity in quantum-classical hybrid systems, with 8/15 recent papers addressing quantum computing challenges. The work emphasizes practical implementations in medical imaging and resource-constrained environments. His research aligns with DIKU's Machine Learning section priorities including medical imaging biomarkers and sustainable computing. Key infrastructure includes TreeSense for remote sensing and the department's dedicated compute cluster. No scientific awards were explicitly documented in the provided materials. Due contributes to DIKU's teaching mission as a Lecturer while engaging with the SCIENCE AI Centre's interdisciplinary initiatives. His work connects with medical imaging applications and quantum computing infrastructure development. Active in the Machine Learning section's research ecosystem, his work intersects with medical imaging analysis and quantum computing applications, utilizing specialized resources like TreeSense for environmental monitoring.
Kasper Hornbæk is a Professor at the Department of Computer Science, University of Copenhagen, specializing in Human-Centred Computing. He conducts research in human-computer interaction (HCI), usability, eye tracking, visualization, and software engineering. Primary Fields: Human-Computer Interaction, Usability Research, Eye Tracking, Visualization, Software Engineering Recent Collaborations: International (country/territory-level) partnerships in HCI and AI Research Outputs: 228 publications focusing on multimodal interaction, VR, and causal modeling His work explores audio-tactile integration, theoretical frameworks in HCI, and principles for user interface design through empirical studies and meta-analyses. Recent projects include heartbeat resonance interfaces and critiques of experimental methodology. Contact: kash@di.ku.dk | Research Website
