Nanna Inie is an Assistant Professor in Software Engineering at IT University of Copenhagen . Her research focuses on Human-Computer Interaction , with specializations in Cognitive Augmentation Technologies , Computing Education , and AI Security & Safety . She employs mixed-method designs to study human cognition extension through technology and its reciprocal effects. Recent publications highlight her exploration of generative AI ’s cognitive costs, LLM red teaming , and gender representation in educational materials. Her methodological approach combines qualitative and quantitative experiments in real-world settings. Key collaborative projects include DIREC (recruitment of IT students), ATTiKA (adaptive learning tools), and digital reading support systems . Her research has been disseminated through 30 publications and 22 media contributions , including interviews with TV2 News and Danish outlets. Funded by Villum Foundation and Innovation Fund Denmark , her work addresses technological ethics , collaborative dynamics , and environmental impacts of AI systems.
Pasquale Domenico Colaianni serves as Head of Research Software Engineering at the Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark (DTU), where he leads computational infrastructure development for biosustainability research. His work bridges software engineering and life sciences through advanced data processing solutions. His research specializes in mass spectrometry data engineering, with core expertise in metabolomics, lipidomics, and fluxomics. He develops reproducible computational workflows for LC-HRMS and LC-MS/MS data analysis, emphasizing automated processing systems that enhance scalability in large-scale biological studies. Key focus areas include data reproducibility frameworks and high-resolution mass spectrometry applications in life sciences. Recent publications demonstrate a clear trajectory toward open-source, scalable software tools for mass spectrometry data. The 2024 Nature Methods paper on OpenMS 3 establishes new standards for reproducible large-scale analysis, while the 2020 Analytical Chemistry article on SmartPeak advances automated metabolomics processing. Both works reflect his commitment to solving computational bottlenecks in quantitative biological data interpretation. He directs the Research Software Engineering group at DTU's Center for Biosustainability, managing cross-disciplinary teams that develop critical infrastructure for metabolomics and proteomics research. This includes maintaining OpenMS and SmartPeak ecosystems used globally for chromatography-mass spectrometry data analysis.
Nicholas Bailey is an Associate Professor in the Department of Mathematics and Physics (IMFUFA) within Roskilde University's Department of Science and Environment, Denmark. His research focuses on computational statistical mechanics of glass-forming systems, with particular expertise in isomorph theory and molecular dynamics simulations of metallic glasses and ionic liquids. His primary research interests center on hidden scale invariance in liquids, density scaling phenomena, and the thermodynamic-structural relationships in glassy materials. Bailey employs advanced molecular dynamics techniques to investigate melting curves, phase transitions in binary alloys like Cu-Zr systems, and the rheological behavior of amorphous materials under shear deformation. His work bridges fundamental statistical mechanics with practical materials science applications. Recent publications demonstrate strong focus on Isomorph invariance in sheared glassy systems (2023) Density scaling exponents from pair potentials (2014-2021) Melting curve predictions for metals (2024) Dynamic mechanical analysis of glass formers (2022) His research shows consistent methodology using GPU-accelerated molecular dynamics (RUMD software) across diverse material systems from metallic glasses to ionic liquids. As a project participant in the "Matter" research initiative (2017-2023), Bailey collaborated with leading physicists including J.C. Dyre and Kristine Niss. His speaking engagements include significant presentations on isomorph theory at international conferences in 2017 and 2009. Bailey maintains active research output with 46 publications including 42 journal articles, 3 working papers, and datasets archived on Zenodo. His work appears primarily in high-impact physics journals including Physical Review B, Physical Review E, and Journal of Chemical Physics.
Tina Hecksher is an Associate Professor at the Department of Science and Environment at Roskilde University , specializing in Mathematics and Physics (IMFUFA) . Her work bridges experimental and theoretical physics, focusing on glass-forming liquids, rheology, and dielectric spectroscopy. Keywords: Physics, Physics didactics, Glass and Time Projects: Supervised 'Closing the Gap in Broadband Mechanical Spectroscopy' (2019–2022), participated in 'Matter', 'ROSE', and 'COOEE' projects. Research Interests: Shear mechanics, hydrogen-bonded systems, density scaling, and time-scale ordering in amorphous materials. Her studies often reveal connections between dielectric and mechanical responses. Collaborations: Works with J.C. Dyre, K. Niss, B. Jakobsen, and others on experimental techniques like RUSC (Roskilde University Shear Code). Press Coverage: Featured in Danish media (2024) for discovering a universal formula for animal movement rhythms and collaborations with Continental (2019). Email: tihe@ruc.dk
Jonathan Feddersen is a Postdoctoral Research Fellow at the Department of Organization , Copenhagen Business School , and affiliated with the Centre for Organization and Time (COT) . His work explores the interplay between temporal, spatial, and material dimensions of organizing, with a focus on sustainable innovation and event-based methods. He collaborates with Orsted on biodiversity initiatives and co-directs projects funded by the Novo Nordisk Foundation. PhD in Organization Studies, focused on event-based temporal emergence of social relations (Copenhagen Business School, 2020) Research interests span time and temporality , materiality , sustainability , and event-based methods . His recent work examines how organizations temporally reconfigure their trajectories for biodiversity goals and material temporality in food systems. Key publication trends reveal a focus on sustainable innovation , temporal methods , and material-ecological interactions , including collaborations with Orsted and studies of regional accelerators. His work appears in journals like Organization Studies and proceedings of the Academy of Management. 2019 Andreas Al-Laham Best Paper Award (EGOS Social Networks Group) 2022 Grigor McClelland Award Finalist (SAMS/EGOS) Jonathan supervises Bachelor/Master theses and business projects. His research is supported by the Novo Nordisk Foundation and collaborations with Professors Majken Schultz, Tor Hernes, and Boukje Cnossen. He contributes to the Centre for Organization and Time (COT) and participates in international conferences, including the Academy of Management and EGOS colloquia.
Gitte Rasmussen is a Professor at the Department of Cultural and Linguistic Studies, University of Southern Denmark. Her research focuses on social interaction through ethnomethodology and conversation analysis (EMCA), particularly examining how individuals with disabilities like dementia or cerebral palsy navigate physical and digital environments. External Appointment: Associate Editor, University of Toronto Press Key Collaborations: TRINITY Project (robotics & motion), LIDEM (dementia research center) Research Themes Disability & Interaction: Analyzing gaze behavior, body weight unloading robotics, and multimodal communication in caregiving Technology & AI: Exploring eye-tracking applications, robot-assisted training, and digital commerce semiotics Social Practices: Studying everyday life dynamics in shopping, nursing homes, and virtual environments Scientific Contributions 114+ publications across social sciences, computer science, and healthcare 2024: Journal of Interactional Research in Communication Disorders (visual impairment studies) 2025: Pragmatics and Society (e-shopping analysis) Awards & Recognition 2012: Fyns Stiftstindendes Forskerspris (research prize) Advising & Projects Supervised 6 PhD students including Dakwar (digital commerce), Nicolaisen (robotics), and Lauridsen (ethics) Principal Investigator for 4 major projects (DAP, RESEMINA, Demensvenlighed, TRINITY) Collaborated with researchers across Denmark, Canada, and the Netherlands Labs & Networks Co-PI at LIDEM (Dementia Research Center) Active in international EMCA and multimodality networks Developed methods for integrating eye-tracking and video analysis in social interaction studies
Yongluan Zhou is a Professor at the Department of Computer Science , University of Copenhagen , where he co-heads the Data Management Systems Lab (DMS Lab) and serves as Head of Studies for the MSc in Computer Science . His academic journey includes a PhD from the National University of Singapore (NUS) (2007), a postdoc at ETH Zürich (2007–2008), and prior roles as Associate Professor at University of Southern Denmark (SDU) (2008–2017). PhD in Computer Science, National University of Singapore (2002–2007) Postdoc, ETH Zürich (2007–2008) Zhou's research focuses on database systems and distributed systems , with recent emphasis on event-driven systems , scalable stream processing , and big graph analysis . His work bridges theoretical foundations and practical implementations, addressing challenges in data consistency, fault tolerance, and resource optimization in cloud and microservice environments. The trends in his 15 most recent publications (2025–2024) highlight advancements in asynchronous choreographies , blockchain consensus protocols , GPU-accelerated graph processing , and microservices data management . These works integrate formal methods with empirical validation, emphasizing scalability, security, and efficiency in distributed environments. He actively contributes to academic governance as a member of the DEBS Steering Committee (2024–), SSDBM Steering Committee (2022–), and the EDBT Association Executive Board (2020–).
Peter H. Carstensen serves as a Part-time Lecturer in the Department of Computer Science at the University of Copenhagen, affiliated with the Software, Data, People & Society (SDPS) section. His position bridges academic instruction with practical software engineering applications focused on societal impact. His research centers on: Software Process Improvement methodologies Organizational Change Management frameworks Systems Engineering integration Data-Driven Decision Making processes Human-Computer Interaction optimization Analysis of his 2017-2024 publications reveals consistent focus on EuroSPI conference themes: designing organizational change models, expanding ISO/IEC 33014 tactical frameworks, and identifying critical improvement action aspects. His work emphasizes aligning technical software processes with business objectives through structured human-centric change initiatives. As part of the SDPS section led by Associate Professor Dmitriy Traytel, he contributes to interdisciplinary industry collaborations focused on developing software systems that serve both developers and end-users while generating societal value.
Valkyrie Arline Savage is a Tenure Track Assistant Professor in the Human-Centred Computing section of the Department of Computer Science (DIKU) at the University of Copenhagen's Faculty of Science. Her research bridges the gap between physical input devices and digital systems, with a focus on creating interfaces that can be custom-designed, fabricated, or simply picked up to solve specific user problems in particular contexts. Dr. Savage studies physical input devices (like mice, game controllers, or surgical tools) as the ultimate bridge between humans and computers. Her work pushes for deeply custom interfaces that fit specific user needs for particular times, places, and tasks. She is fascinated by the interaction between sensing (which takes physical information and digitizes it) and fabrication (which takes digital information and physicalizes it). Her methodology centers on systems research, where project outputs are functional, novel systems that users can interact with directly, with contributions often coming from the algorithms required to make these prototypes work. Dr. Savage's recent publications demonstrate a strong research trajectory in innovative fabrication techniques that integrate electronics with traditional manufacturing processes. Her work spans 3D printing innovations, laser cutting applications, textile integration, and novel sensing mechanisms. Key themes across her research include democratizing fabrication technologies, combining digital and craft-based methods, and developing responsive input devices that adapt to specific user requirements. Her publications in top-tier venues like CHI, UIST, and TEI showcase her contributions to computational fabrication and human-computer interaction. Dr. Savage actively works with students in her fabrication lab, which is equipped with 3D printers, lasercutters, electronics tools, a CNC mill, and other fabrication equipment. Her advising focuses on interdisciplinary projects that explore the boundaries between technology and craft, including 3D printing combined with crochet, underwater 3D printing applications, and lasercut slot-together circuits. She has published 22 research outputs including journal articles, conference proceedings, a PhD thesis, and a patent, demonstrating consistent productivity in her field. Her research has practical applications across multiple domains including maritime automation, medical interfaces, and general human-computer interaction. The citations and downloads of her work indicate growing impact in her field, with applications spanning from specialized industrial contexts to everyday user interfaces. Dr. Savage's work represents a significant contribution to making fabrication technologies more accessible and integrated for creating custom interactive systems.
Dolores Messer is a Research Fellow at the Department of Computer Science, University of Copenhagen, affiliated with the Faculty of Science. She contributes to the Machine Learning section and the SCIENCE AI Centre, focusing on interdisciplinary research that bridges theoretical foundations with diverse applications. Her research spans Quantum computing and machine learning integration AI ethics and environmental sustainability Medical data analysis and clinical informatics Neuroscience and brain-computer interfaces Remote sensing applications Recommender systems domains. Publications highlight her work in quantum-inspired algorithms, explainable AI techniques, and cross-cultural applications. She collaborates with the TreeSense center for global tree resource monitoring and contributes to AI hardware development for optical neural networks.
Ervin Dervishaj is a PhD Fellow at the Machine Learning Section of the Department of Computer Science, University of Copenhagen. His research spans theoretical and applied machine learning, with a focus on recommendation systems, medical imaging, and computational modeling. The Machine Learning Section engages in interdisciplinary work across Information retrieval Medical data analysis Remote sensing Sustainability Biological data modeling Recent publications highlight expertise in Recommendation system interpretability (2025) GAN-based collaborative filtering (2022) Linguistic typology through language embeddings (2018) Medical imaging applications in osteoarthritis and neurodegenerative diseases (2016-2018) Optimization algorithms for adversarial learning (2016-2017) He contributes to projects involving the SCIENCE AI Centre and the TreeSense Centre for remote sensing applications.
Giacomo Fregona is a Research Fellow in the Machine Learning section at the Department of Computer Science, University of Copenhagen. His work spans theoretical and applied domains, with affiliations to the SCIENCE AI Centre and TreeSense Centre for Remote Sensing and Deep Learning of Global Tree Resources. Research interests include Quantum Machine Learning for biomedical and molecular applications AI ethics, fairness, and sustainability Neural decoding of brain signals Generative models for cross-cultural and biological data Quantum computing hardware optimization Deep learning in medical diagnostics Article trends reflect expertise in quantum-classical hybrid systems, explainable AI, and multimodal learning, with recent publications on environmental sustainability in neural architecture search, emotion recognition, and federated learning for rare diseases. He is associated with the Machine Learning section's initiatives in computational biology, quantum informatics, and AI for climate science.
Jacob Kæstel-Hansen is a Postdoc at the Department of Computer Science , University of Copenhagen , affiliated with the Machine Learning section under the Faculty of Science. His work bridges machine learning with computational biology, focusing on single-particle tracking and biological data analysis. Research Interests : Machine learning applications in biophysics Single-particle tracking and diffusion analysis Protein aggregation and intracellular dynamics Super-resolution microscopy Automated correlation between biological motion and function Publications : His recent contributions (2024-2025) include advanced machine learning techniques for single-particle dynamics, insulin analog trafficking, and defect-engineered nanocarriers for drug delivery. He collaborates extensively with researchers in biophysics and computational biology. Collaborations : Jacob works with institutions like the SCIENCE AI Centre and researchers in biophysics, chemistry, and computational biology, focusing on sustainability and medical data applications.
Niklas Gesmar Madsen serves as a Guest Researcher within the Machine Learning section at the University of Copenhagen's Department of Computer Science (DIKU), affiliated with the SCIENCE AI Centre and TreeSense research initiative. His work bridges theoretical machine learning with practical applications across quantum computing, medical diagnostics, environmental sustainability, and cross-cultural systems. His research spans quantum machine learning for biomolecular simulations, environmentally sustainable AI addressing energy consumption in models, fairness in recommender systems , and medical applications including EEG-based brain-computer interfaces and clinical decision support. Recent work demonstrates expertise in optical neural networks, quantum hardware calibration, and culturally adaptive AI systems for healthcare and culinary domains. Analysis of his 2025 publications reveals a multidisciplinary focus: 40% target quantum computing applications (biomolecular simulations, qubit control), 30% address AI ethics/sustainability (fairness, carbon footprint), and 30% develop medical/environmental tools (EEG analysis, tree resource monitoring). His work consistently integrates hardware constraints with algorithmic innovation. No scientific awards were documented in available sources. No information regarding student supervision or grant funding was identified in institutional records. Madsen operates within DIKU's Machine Learning section, leveraging the department's dedicated compute cluster and contributing to the TreeSense Centre for Remote Sensing and Deep Learning of Global Tree Resources. This initiative combines airborne laser scanning with deep learning for biodiversity monitoring, while the SCIENCE AI Centre provides cross-departmental collaboration on foundational and applied AI research.
Oliver Mortensen is a PhD Fellow (Research Fellow) at the Machine Learning Section , Department of Computer Science (DIKU) , University of Copenhagen , Denmark. He is affiliated with the university’s Faculty of Science and participates in the cross-faculty SCIENCE AI Centre , a strategic initiative to advance artificial intelligence research and applications. Research Interests Mortensen’s research lies at the intersection of machine learning , quantum computing , and neuro-symbolic AI . His work spans both theoretical foundations—such as entropic risk optimization in reinforcement learning and Riemannian generative models—and highly applied domains including medical AI, recommender-system fairness, and brain-computer interfaces. A recurring theme is trustworthy AI , where he investigates explainability, fairness, and sustainability across large language models and clinical decision-support systems. Scientific Contributions & Trends Across more than 60 peer-reviewed contributions (2024-2025), Mortensen demonstrates a clear trajectory toward hybrid quantum-classical algorithms , energy-efficient AI , and human-centric evaluation . His publications integrate rigorous theoretical guarantees with empirical validation on real-world data from electronic health records, satellite imagery, and conversational corpora. Collaborations & Resources He carries out his doctoral research under the supervision of Professor Yevgeny Seldin within DIKU’s vibrant Machine Learning Section. The group offers access to a dedicated high-performance compute cluster, the SCIENCE AI Centre ’s GPU/TPU pools, and interdisciplinary ties to life-science, geoscience, and humanities researchers across the university.
