Jonas Vinther is a Research Fellow at the Department of Computer Science , University of Copenhagen, specializing in Machine Learning and its intersections with quantum computing, medical data analysis, and sustainability. He is also an external PhD student in the Quantum Information Science & Technology program at the Niels Bohr Institute. Email: jonas.vinther@nbi.ku.dk , jonas.vinther@di.ku.dk Location: Universitetsparken 1, 2100 København Ø His research spans quantum machine learning , AI ethics , medical imaging , and environmentally sustainable AI , with recent publications on topics ranging from quantum neural networks to fairness in recommender systems . He contributes to the SCIENCE AI Centre and collaborates on initiatives like TreeSense for global tree resource monitoring.
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.
Ivan Adriyanov Nikolov is an Assistant Professor at the Department of Architecture, Design and Media Technology within Aalborg University's Technical Faculty of IT and Design. He specializes in Computer Graphics, Computer Vision, and Augmented Reality, with a focus on 3D reconstruction techniques like Structure-from-Motion (SfM). His work bridges academic research and industrial applications, particularly in wind turbine blade inspection and educational technology. His educational background includes contributions to computer science education through innovative teaching methods. He has led projects like 'Drone Application for Pioneering Reporting in Wind Turbine Blade Inspection' (2017–2019) and 'Leading Edge Roughness - Wind Turbine Blades' (2015–2019), advancing drone-based inspection and 3D modeling for wind energy sectors. Research interests include synthetic data generation, environmental monitoring datasets (e.g., BrackishMOT, DigiWeather), and improving VR/AR user experiences. He has developed tools for dynamic lighting in pixel art games and multimodal guardian systems in VR. His datasets, such as Sewer Defect Point Clouds and Wind Turbine Blade SfM Reconstructions, are publicly available for academic use. He actively contributes to educational innovation, such as flipped classroom strategies to boost programming class engagement. His interdisciplinary approach spans computer graphics, AI-driven NPC interactions, and collaborative mixed-reality games for trust-building. Labs/Teams: Member of the Computer Graphics Group and Visual Analysis and Perception team at Aalborg University. Collaborates with industry partners on drone technology and environmental surveillance systems.
Barbara Plank is a Professor and Chair for AI and Computational Linguistics at LMU Munich , where she leads the Munich AI and NLP (MaiNLP) Lab within the Center for Information and Language Processing (CIS) . She also serves as a Visiting Full Professor at the IT University of Copenhagen . Her research focuses on natural language processing under real-world constraints, including domain adaptation, continual learning, and multimodal learning. Current Projects: ERC Consolidator DIALECT Project KLIMA-MEMES Project Her recent work investigates annotation bias , self-consistency in language models , and trustworthy model evaluation . She has delivered keynotes at major conferences like ACL, EMNLP, and CLEF, emphasizing human-centric approaches to NLP. Scientific Awards: ACL 2024 Area Chair Award Barbara actively contributes to the academic community as VP-Elect for the Association for Computational Linguistics (ACL) and through teaching roles in MSc/BSc Computational Linguistics programs.
Stella Grasshof is an Assistant Professor in Data Science at the IT University of Copenhagen , specializing in machine learning and computer vision applications. Her work spans 3D reconstruction, facial expression analysis, mental health diagnostics, and sports analytics. Research Areas : 3D trajectory estimation, diffusion models, underwater image segmentation, and technical drawing digitization. Key Collaborations : European Commission (REMARO), Danish National Research Foundation (Pioneer Centre for AI), Lundbeck Foundation (Automatic Analysis of Mental Disorders). Research Trends : Stella's recent publications focus on advancing generative models for interpretable latent space analysis, improving sim-to-real underwater segmentation, and applying synthetic data to 3D motion tracking. Her work bridges computer vision, machine learning, and real-world applications in sports and mental health. Scientific Awards : Best Student Paper Award at the 11th International Conference on Pattern Recognition Applications and Methods (2022). Projects & Grants : Active in multidisciplinary projects like REMARO (Trustworthy AI for marine robotics), Pioneer Centre for AI (Danish National Research Foundation), and TeamSPORTek (sports technology research). She has also developed datasets like MarinaPipe for marine robotics.
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.
Patricia Wolf is a Professor in the Department of Business & Management at the University of Southern Denmark (SDU), Faculty of Business and Social Sciences. She is affiliated with multiple interdisciplinary research centers, including the Centre for Integrative Innovation Management (C*I2M), the SDU SCC Elite Center PACA, and the SDU Climate Cluster, reflecting her broad impact across innovation, climate futures, and societal transformation. Her research focuses on innovation management, foresight methodologies, artificial intelligence in social contexts, knowledge management, and climate futures. She explores how organizations and societies can envision and enact sustainable futures, particularly through participatory and creative methods such as scenario development, flash fiction, and citizen science. The 15 most recent publications reveal a strong trend toward integrating AI, climate imagination, and participatory futures, with increasing emphasis on emotional dimensions like climate anxiety and hopeful envisioning. Her work bridges theory and practice, often involving collaborations with SMEs, educational institutions, and environmental organizations to co-create actionable strategies. Innovative Teaching Award 2024 John Bessant Best Paper Award (2023 and 2021) SCC Fast track - uddelingsrunde november 2024 Patricia Wolf has secured significant grant funding for projects such as FUSION, PACA, and CFF, where she serves as Principal Investigator or Co-PI. These projects focus on mobilizing youth, educators, and communities to engage in climate future fiction and responsible innovation. She has supervised PhD students and contributed to curriculum development in innovation and foresight education. Her teaching includes courses on innovation management, knowledge management, and organizational processes. She leads and participates in interdisciplinary teams across SDU, including collaborations with the Department of Sports Science, the Climate Cluster, and external partners in Denmark and Germany. Her labs and research groups emphasize co-creation, narrative methods, and future-oriented design, fostering a culture of integrative and socially responsible innovation.
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.
Andreas Møgelmose is an Associate Professor at Aalborg University's Department of Architecture, Design and Media Technology under the Technical Faculty of IT and Design. His research focuses on computer vision, artificial intelligence, and their applications in autonomous systems, driver assistance, and industrial vision. He leads projects like AI Color Fashion and Real-world adaption of generative AI for architecture. Møgelmose teaches introductory programming, computer vision, and advanced master's courses, emphasizing practical project-based learning. His work includes developing datasets such as the Multi-view Traffic Intersection Dataset (MTID) and exploring vision-language models for autonomous vehicle safety. He actively engages in media discussions on AI ethics and societal impacts. Research interests span dynamic gesture interpretation for cooperative autonomous vehicles, surgical skill assessment via automated metrics, and multimodal classification of environmental data. He has contributed to over 50 publications, including work on 3D object detection frameworks and generative AI education. Møgelmose collaborates on projects funded by industry partners like COWI and Danish government initiatives. His teaching philosophy centers on blended learning and practical application, fostering innovation in AI and computer vision education. Notable projects include AI:Xpertise Lab (2025–present), which explores AI-driven expertise systems, and collaborations on forest biodiversity analysis using LiDAR and orthophotos. His recent media engagements highlight societal AI challenges, emphasizing responsible implementation in public sectors.
Ashutosh Dhar Dwivedi is an Assistant Professor in the Cybersecurity Group at Aalborg University, Copenhagen, Denmark. He specializes in blockchain security, applied cryptography, post-quantum cryptography, and advanced cybersecurity. His interdisciplinary research spans cryptography, IoT security, and AI-driven security analytics. Education: PhD in Cryptography, with postdoctoral research at institutions including the University of Waterloo, Technical University of Denmark, and the Polish Academy of Sciences. His pedagogical focus includes professional upskilling in cyber defense and post-quantum resilience. Research interests include post-quantum cryptographic protocols, privacy-preserving blockchain systems, and machine learning for security. His work has yielded over 50 peer-reviewed papers, including contributions to high-impact journals and conferences. Notable achievements: 2023 and 2024 Stanford University Top 2% Scientist ranking. Contributions: Editorial roles in international journals, program committees for premier conferences, and leadership in academic-industry collaborations like the Quantum Communication Infrastructure (QCI) consortium. Active in developing quantum-secure systems for national and industrial infrastructure.
Tomer Sagi is an Associate Professor in the Department of Computer Science at Aalborg University (AAU), Denmark. He is affiliated with The Technical Faculty of IT and Design and leads projects in the AI for the People and BLUE – Marine & Maritime Research groups. His research focuses on data integration, ontology engineering, artificial intelligence applications in healthcare and environmental science, and knowledge graph development. PhD in Information Systems from Technion-Israel Institute of Technology (2015) Former Lecturer at University of Haifa (2017–2022) Principal Investigator/Co-PI in projects like ODINI (AI-based Data Integration), MEHDIE (Middle Eastern Heritage Knowledge Graph), and DarkScience (Microbial Data Science) Research Interests: Data Integration, AI for Ocean Science, Medical Informatics, Ontology Evaluation, Multilingual Knowledge Systems, and Explainable AI. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure. Key Projects (2022–2025): DarkScience: Metagenomic data analysis funded by Villum Foundation ODINI: AI-driven ocean data fusion and 3D reconstruction MEHDIE: Multilingual historical knowledge graphs for Middle Eastern heritage Awards: Received NLP4KGC Best Paper Award (2023) and AIME 2020 Best Paper Nomination. His contributions span 46+ publications, 8 datasets, and media coverage on AI applications in healthcare and environmental science. Labs/Teams: Active in AI for the People (applied AI solutions) and BLUE (marine data science). Collaborations include work on virtual twin technology for stroke management and medical data analytics.
Shivam Adarsh is a PhD Fellow in the Machine Learning section at the University of Copenhagen's Department of Computer Science (DIKU), actively contributing to the SCIENCE AI Centre. Based at Universitetsparken 1 in Copenhagen, he engages in interdisciplinary research spanning theoretical foundations and real-world applications of artificial intelligence. His research focuses on Machine Learning, Natural Language Processing, Quantum Computing, Medical Image Analysis, Sustainability Applications, and Remote Sensing. Key projects include cross-cultural recipe adaptation using Retrieval-Augmented Generation, emotion-aware conversational AI, quantum-enhanced biomolecular simulations, and environmentally sustainable AI development. His work bridges computational theory with practical implementations in healthcare, environmental monitoring, and cultural systems. Analysis of his 2025 publications reveals dominant themes in Natural Language Processing (35% of works) and Quantum Computing (27%), with significant emphasis on Explainable AI and Medical Applications. His research consistently integrates multiple disciplines—such as combining quantum algorithms with drug discovery or embedding cultural diversity metrics into recommendation systems—demonstrating a systems-thinking approach to complex problems. As part of DIKU's Machine Learning group led by Professor Yevgeny Seldin, he utilizes the department's powerful compute cluster and participates in the TreeSense Centre for Remote Sensing and Deep Learning of Global Tree Resources. The group's collaborative environment spans medical data analysis, sustainability modeling, and biological data interpretation, with strong ties to Denmark's national AI initiatives.
Johanna Maria Düngler is a Research Fellow at the Department of Computer Science , University of Copenhagen . She is affiliated with the Natural Language Processing (NLP) section, which focuses on advancing methods for text processing, language understanding, and generation using statistical models and machine learning. The section addresses applications such as automatic fact-checking, machine translation, and multi-modal learning involving vision and language.
