Axel Prütz Kanne is an Instructor at the Department of Computer Science, University of Copenhagen, affiliated with the Natural Language Processing (NLP) section. His work aligns with the section’s focus on methods for automatic text processing, understanding, and generation using statistical models and machine learning, with applications in fact-checking, machine translation, and multimodal language processing. Department: Computer Science Location: Universitetsparken 1, DK-2100 Copenhagen Ø The NLP section explores core tasks like cross-lingual processing, parsing, and transfer learning, as well as multi-modal machine learning intersecting with computer vision. Teaching activities include courses in Computer Science, Machine Learning, and the Language Technologies track for MSc programs.
Ernst á Heygum Kass is an Instructor at the Department of Computer Science (DIKU), University of Copenhagen. He is affiliated with the Natural Language Processing (NLP) section, which focuses on methods for automated text processing, understanding, and generation using statistical models and machine learning. Core research areas include natural language understanding, misinformation detection, explainable AI, and multi-modal machine learning intersecting with computer vision. Applications of his affiliated section's work span automatic fact-checking, machine translation, question answering, and visually-grounded language learning.
Marcus Friis Klausen is a Lecturer at the Department of Computer Science , University of Copenhagen, affiliated with the Machine Learning section. His work intersects theoretical and applied machine learning across interdisciplinary domains. Research interests include: Quantum machine learning and hardware acceleration Explainable AI and model interpretability Clinical and healthcare applications of NLP Neuroscience-informed language modeling Environmentally sustainable AI practices Geometric and non-Euclidean deep learning Recent publications show trends in: Quantum computing applications for molecular simulations Medical imaging and clinical decision support Algorithmic fairness and ethical information retrieval Neural network optimization for energy efficiency Biological data modeling and remote sensing
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.
Jeppe Fræhr Linderød works as a Lecturer at the Department of Computer Science , University of Copenhagen. His research aligns with the department's Machine Learning section, focusing on theoretical foundations and applications in information retrieval, medical data analysis, remote sensing, and sustainability. He is part of the interdisciplinary SCIENCE AI Centre . His recent publications span diverse subfields including: Quantum machine learning and optical computing Explainable AI and feature attribution Large language models for emotion recognition Medical informatics applications Fairness in recommender systems Green/sustainable AI practices He contributes to the department's computational infrastructure, including access to a powerful compute cluster. His work often intersects with environmental and healthcare domains, particularly through projects like the TreeSense center for remote sensing applications.
Tobias Nordholm-Højskov is an Instructor at the Department of Computer Science , University of Copenhagen (DIKU). His research intersects machine learning with healthcare, sustainability, and quantum computing, focusing on theoretical foundations and applications in medical data analysis, climate-aware AI, and quantum systems. He is affiliated with the SCIENCE AI Centre and contributes to projects like QDarts (quantum dot array simulation) and TreeSense (remote sensing for environmental monitoring). His work spans diverse subfields, including Explainable AI for healthcare records Federated Learning in rare disease research Quantum-inspired neural networks Retrieval-Augmented Generation frameworks Environmental impact mitigation in AI
Benjamin Daniel Nyberg Shultz is an Instructor at the Department of Computer Science (DIKU), University of Copenhagen. His work aligns with the Natural Language Processing (NLP) section , focusing on methods for automated text processing, understanding, and generation using statistical models and machine learning. Research Focus : Core NLP tasks like parsing, cross-lingual processing, and transfer learning; applications in automatic fact-checking, machine translation, and visually grounded language learning. Teaching : Contributes to courses in Computer Science, Machine Learning, and Data Science, including advanced topics in NLP and deep learning. Collaboration : Part of the SCIENCE AI Centre, exploring intersections between NLP and computer vision in multi-modal learning.
Thor Alexander Bøje Simonsen is an Instructor at the Department of Computer Science , University of Copenhagen , focusing on interdisciplinary research at the intersection of machine learning, quantum computing, and real-world applications. He is affiliated with the SCIENCE AI Centre and contributes to projects in sustainability, medical data analysis, and quantum-enhanced algorithms. His research spans theoretical and applied domains, including: Quantum computing for biomolecular simulations Environmentally sustainable AI systems Medical data analysis and clinical decision support Image reconstruction and remote sensing Explainable AI for large language models Thor's work engages with cutting-edge challenges in machine learning, from hardware acceleration to ethical considerations in clinical contexts. He is part of the university's Machine Learning Section , which has access to a dedicated compute cluster and collaborates with initiatives like TreeSense for global tree resource monitoring.
Luca Pezzarossa is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on real-time systems, embedded computer systems, digital microfluidics, compiler optimizations, and hardware accelerators. He leads projects such as the Edu4Chip: Joint Education for Advanced Chip Design in Europe initiative and supervises PhD students in areas like compiler optimizations for neural networks and speech enhancement algorithms. His academic journey includes contributions to interdisciplinary fields, combining computer engineering with biomedical applications such as biochip design and PCR optimization. He actively engages in open-source tool development, particularly using the Chisel framework for hardware design education and research. Key research themes include: Real-time systems and time-predictable architectures Compiler-driven optimizations for constrained devices Digital microfluidics for lab-on-a-chip systems Edge computing and TinyML applications Recent publications highlight innovations in microplastic detection on edge devices, dynamic channel pruning for speech enhancement, and parallel execution engines for digital microfluidics. His work aligns with sustainable development goals through environmental applications and energy-efficient technologies. Current projects involve: PhD Supervision: Andrea Cerioli (Compiler Optimizations), Riccardo Miccini (AI-to-Neural Network Mapping), Ehsan Khodadad (Time-predictable Systems) Research Grants: EU-funded Edu4Chip (2023–2025), multiple industry-academia collaborations Labs and teams: Leads the Embedded Systems Engineering group at DTU, focusing on interdisciplinary hardware-software co-design for real-world applications. Active in developing open-source frameworks for education and research.
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.
Dr. Sepideh Valiollahi Bisheh is a Postdoctoral Researcher at the Department of Architecture, Design and Media Technology, Technical Faculty of IT and Design, Aalborg University, Denmark. Her research addresses critical challenges in robotics, wireless communication, and human-robot interaction for industrial and domestic applications, with publications spanning 2023-2025. Her research portfolio includes: Robotics (Home and Industrial Automation) Wireless Communication Technologies (5G, Wi-Fi, Ultra-Wideband) Human-Robot Interaction Systems Industrial Internet of Things (IIoT) Infrastructure Edge Computing Implementations Computer Vision and Natural Language Processing Integration Recent publications demonstrate a clear trajectory toward vision-language frameworks for assistive robotics in home environments and industrial safety applications. Her work systematically evaluates real-time locating systems and 5G technologies for industrial automation, emphasizing practical implementation, experimental validation, and performance metrics in real-world settings. No information is available regarding student advising responsibilities, grant funding history, or specific laboratory team affiliations in the provided materials.
Xiufeng Liu is a Senior Researcher at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His research focuses on smart meter data analytics, big data, and energy systems, contributing to UN Sustainable Development Goals related to affordable and clean energy. He holds a PhD from Aalborg University (2012) and has held positions at IBM Canada, the University of Waterloo, and Åbo Akademi University. Dr. Liu’s expertise spans energy economics, climate policy modeling, and data-driven methodologies. His work integrates machine learning with energy systems, addressing challenges such as wind power forecasting, solar cell optimization, and heat load prediction. Key projects include OPTIX (optimizing positive-energy districts) and ANSWER (wind-solar energy prediction models). His research outputs emphasize sustainable energy solutions, including federated learning frameworks for privacy-preserving data sharing and anomaly detection in energy grids. Collaborations span global institutions, reflecting his commitment to interdisciplinary energy solutions. Dr. Liu has supervised numerous projects and contributed to 148 publications. His work bridges technical innovation with policy implications, aiming to advance climate-compatible energy strategies globally.
Kristoffer Laigaard Nielbo is a Professor at Aarhus University’s School of Culture and Society - Center for Humanities Computing , specializing in computational methods for humanities research. He contributes to AI-driven cultural heritage projects, literary analysis, and mental health diagnostics using electronic health records. His work bridges machine learning with literary theory, social media analysis, and historical data processing. Current Projects : Golden Imprints of Danish Cultural Heritage (2023–2026), CLAI: Center for Language Generation and AI (2023–), SOCIAL MEDIA INFLUENCE (2023–2028). Previous Awards : Best Paper Award (2020), Videnskabsministerens EliteForsk-rejsestipendier (2010). Research Interests : Nielbo focuses on Computational Humanities , Quantitative Text Analysis , and Artificial Intelligence . His work includes modeling literary complexity, analyzing sentiment arcs, and developing multimodal frameworks for cultural data. He also explores AI ethics, fairness in literary criticism, and digital diplomacy. Article Trends : Recent publications emphasize machine learning applications in literary studies (e.g., canonicity, genre asymmetries) and mental health prediction models . He contributes to meme analysis in international relations , multimodal AI for art, and Scandinavian language benchmarks , reflecting interdisciplinary expertise. Scientific Awards : Best Paper Award at ACL (2020) EliteForsk Rejsestipendier (2010) Collaborations : Nielbo co-leads projects like TEXT: Center for Contemporary Cultures of Text (2025–2031) and collaborates with institutions including Carlsberg Foundation and PSYCOP cohort. He actively participates in conferences and AI ethics discussions.
Peter Schneider-Kamp is a Professor of Data Science at the Department of Mathematics and Computer Science, University of Southern Denmark. His research focuses on Artificial Intelligence, Machine Learning, Privacy-Preserving Techniques, and Algorithms. He has led projects such as the Danish Foundation Models initiative and the PREPARE cardiovascular disease project. His work spans synthetic data frameworks (e.g., Syntheval), quantization-aware neural networks (BitNet), and autonomous drone systems (Drones4Safety). He has been honored with awards including the Researcher Award 2014 and the Friedrich-Wilhelm-Preis 2009. Key research interests include optimizing sorting networks, termination analysis, and UAV-based infrastructure inspection. He has contributed to over 112 publications and actively participates in academic activities like organizing the 13th ACM SIGPLAN Symposium on Principles and Practice of Declarative Programming. Schneider-Kamp also engages in educational roles, teaching courses such as DS806 and DM564 on database systems. His projects highlight interdisciplinary impact, including cardiovascular disease risk estimation (PREPARE) and AI-driven health advice analysis. He collaborates internationally, with recent work featured in venues like the International Conference on Agents and Artificial Intelligence.
Tung Kieu is a Tenure Track Assistant Professor in the Department of Computer Science at Aalborg University (Denmark), affiliated with The Technical Faculty of IT and Design and the Daisy Center for Data-intensive Systems. His research focuses on data engineering, time series analysis, anomaly detection, and machine learning applications in traffic forecasting and smart systems. Education: Ph.D. in Computer Science (Awarded May 2021). Research interests include time series forecasting, traffic modeling, robust autoencoder architectures for anomaly detection, and spatio-temporal data analysis. His work contributes to UN Sustainable Development Goals related to smart cities and infrastructure. Recent publications explore bias mitigation in text-video retrieval (BiMa), topology-aware traffic forecasting (TEAM), and stochastic routing in uncertain road networks. His frameworks emphasize lightweight algorithms (LightTS), causal relational learning, and continual calibration for quantized models (QCore). Collaborations involve international teams in data management and AI, with notable work on ensemble methods, explainable AI, and transfer learning in smart building systems.
