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Andreas KugiView profile

Professor

• Control Theory
• Automatic Control
• Complex Dynamical Systems
• +6 more

Andreas Kugi is the Scientific Director at the AIT Austrian Institute of Technology and a full professor of Complex Dynamical Systems at TU Wien (Vienna University of Technology) in the Faculty of Electrical Engineering and Information Technology, Institute of Automation and Control. He has held significant academic and leadership roles across Europe, including professorships at Saarland University and offers from TU Dresden and KIT. His research focuses on the modeling, control, and optimization of complex dynamical systems , with strong applications in mechatronics, robotics, and industrial automation . He has led major research centers such as the Christian Doppler Laboratory for Model-Based Process Control in the Steel Industry and the Center for Vision, Automation & Control at AIT. His work bridges theoretical control design and real-world industrial implementation. The recent publications reflect a consistent focus on nonlinear, hybrid, and distributed parameter systems , with applications in robotics, manufacturing, energy, and process industries. His research integrates advanced control theory with practical engineering challenges, emphasizing real-time optimization, robustness, and system efficiency. Scientific Awards: Mechatronic Systems Outstanding Investigator Award (IFAC, 2022) Goldene Stefan-Ehrenmedaille (OVE, 2023) 16 best paper awards Andreas Kugi has supervised over 50 completed PhD dissertations and has been deeply involved in research leadership, including serving as Editor-in-Chief of Control Engineering Practice (2010–2017) and Vice President of the OVE Austrian Electrotechnical Association (2017–2023). He has secured and led numerous research grants, particularly through industrial collaborations in automation and process control. He leads and contributes to major research initiatives, including the Center for Vision, Automation & Control at AIT and the Christian Doppler Laboratory , fostering interdisciplinary teams focused on industrial digitalization and smart systems.

Xiao ChenView profile

Researcher

• Composite Structures
• Wind Energy Systems
• Structural Integrity
• +7 more

Xiao Chen is a Researcher at the Technical University of Denmark (DTU), specializing in advanced testing and digitalization of composite and offshore steel structures for wind energy systems. With a PhD from Nagoya University (2011), his work focuses on structural integrity, fatigue analysis, and Digital Twins for wind turbine blades. PhD in Engineering, Nagoya University (2011) Senior Researcher (2019–present) and Researcher (2017–2018) at DTU Associate Professor (2016–2017) and Assistant Professor (2013–2015) at Chinese Academy of Sciences His research explores nonlinear buckling, fracture mechanics, and Industry 4.0 technologies for structural health monitoring. Recent publications highlight AI-driven damage detection, thermographic analysis, and finite element modeling of composites. He leads projects like QualiDrone and AQUADA-GO, funded by EUDP and VILLUM FONDEN. Key article trends include composite fatigue , digital twins , drone-based inspection , and machine learning for structural monitoring. He received the 2022 Best Presentation Award at an international conference. Projects: Villum Experiment Project DiscoverBlaDE AQUADA-GO QualiDrone DARWIN RELIABLADE RELIfe

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +3 more

Freja Stær Hincheli serves as a Lecturer at the Department of Computer Science , University of Copenhagen. Her work intersects multiple domains within machine learning, with a particular emphasis on quantum-inspired algorithms, medical imaging, and sustainable AI development. Keywords : Machine Learning, Quantum Computing, Medical Imaging, Natural Language Processing, Computational Biology Key Collaborations : SCIENCE AI Centre Her research spans quantum-enhanced neural networks, explainable AI for medical diagnostics, and energy-aware model design. Recent publications highlight applications in cross-cultural recipe adaptation, emotion-aware dialogue systems, and climate-conscious AI strategies. The Machine Learning Section at DIKU focuses on theoretical foundations and applications including medical image analysis , biological data modeling , and quantum computing , aligning with her contributions.

Jakob KibsgaardView profile

Professor

• Catalysis
• Electrocatalysis
• Ammonia Synthesis
• +5 more

Jakob Kibsgaard is a Surface Physics and Catalysis Professor and Section Leader at the Department of Physics, Technical University of Denmark (DTU). He leads research in the VISION – Center for Visualizing Catalytic Processes and maintains an active research program focused on electrocatalysis, ammonia synthesis, and energy conversion technologies. His research interests span catalysis, electrocatalysis, ammonia synthesis, hydrogen production, CO2 conversion, and nanomaterials. Kibsgaard's work primarily addresses sustainable energy solutions through advanced catalyst design and characterization. His research group investigates fundamental processes at atomic and molecular levels to develop efficient catalysts for energy conversion reactions, with particular emphasis on electrochemical ammonia synthesis and CO2 hydrogenation. Analysis of his recent publications reveals a strong focus on dual and triple atom electrocatalysts, advanced imaging techniques for catalyst characterization, and innovative approaches to ammonia synthesis. His work spans multiple energy conversion reactions including CO2 reduction, nitrogen reduction, oxygen reduction, oxygen evolution, and hydrogen evolution. The research combines experimental approaches with theoretical insights to develop next-generation catalysts for sustainable energy applications. Kibsgaard actively supervises multiple PhD students including Thorlacius-Ussing, Tokman, Wu, Griffin, Kazaz, and Pedersen, with projects spanning controlled synthesis of catalysts, exploration of electrocatalysts at atomic scale, and discovery of new dual and triple atom electrocatalysts. His research is supported by various projects with both active and completed funding. His laboratory work centers around the Surface Physics and Catalysis section at DTU Physics, where he utilizes advanced characterization techniques including transmission electron microscopy, X-ray photoelectron spectroscopy, and custom-designed electrochemical cells to study catalytic processes at fundamental levels.

Claus Brøndgaard MadsenView profile

Associate Professor

• Computer Vision
• Virtual Reality
• Augmented Reality
• +7 more

Claus Brøndgaard Madsen is an Associate Professor in the Department of Architecture, Design and Media Technology at Aalborg University, under The Technical Faculty of IT and Design. He is based at Campus Aalborg and is affiliated with the Computer Graphics Group and VR Light research environment. His work bridges computer vision, virtual reality, and architectural design, with a strong focus on real-time visualization and human interaction in digital environments. Research Interests: His primary research areas include Computer Vision , Virtual Reality (VR) , Augmented Reality (AR) , 3D Reconstruction , Structure-from-Motion (SfM) , and Image-Based Lighting . These are applied in industrial VR, urban design evaluation, and sensory augmentation systems. His research fingerprint highlights strong engagement with Virtual Reality (100%), Augmented Reality (71%), Illumination Conditions (41%), and Wind Turbine Engineering (28%). The recent publications (2023–2025) reflect a consistent trend in enhancing user experience in VR/AR environments, with emphasis on spatial cognition, locomotion techniques, exposure control, and machine learning integration for lighting classification and part direction prediction in industrial contexts. These works are published in top-tier conferences such as EG, GRAPP, WACV, and CAADRIA. Scientific Awards: No specific awards or fellowships are mentioned in the provided text. Advising and Grants: Madsen has supervised PhD students and served as a supervisor in research projects like AAIVR. He is Principal Investigator (PI) in multiple funded projects, including BUMUS: Sustainable Expansion of Mussel Production (2024–2027), CityVR: Evaluating Parametric City Designs in VR (2020–2022), and V-BRT: Virtual Bus Rapid Transit (2019). These projects reflect substantial grant acquisition and leadership in applied VR research. Labs and Research Groups: He is a key member of the Computer Graphics Group and VR Light at Aalborg University, which focus on real-time visualization, lighting techniques, and immersive technologies for industrial and architectural applications.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +4 more

Haeun Yu is a PhD Fellow at the Department of Computer Science, University of Copenhagen, affiliated with the Natural Language Processing and Pioneer AI sections. Her research focuses on language models, retrieval-augmented generation, and model interpretability within artificial intelligence and natural language processing. Key Research Themes: Context utilization in generative AI, knowledge conflicts in QA systems, parametric knowledge attribution. Collaborations: Engages in interdisciplinary projects with researchers in NLP, machine learning, and computer vision. Publications: Recent work addresses dynamic QA systems and parametric knowledge attribution in ACL and EMNLP conferences.

• Image Registration
• Image Segmentation
• Image Classification
• +6 more

Sune Darkner is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in the Image Analysis, Computational Modelling, and Geometry research section. His work focuses on medical image processing with particular emphasis on neuro-imaging data including MRI and PET scans. His primary research interests include Image Registration, Segmentation and Classification of Medical Image Data , with a specific focus on estimation of image similarity as his main research interest. Darkner strongly believes that the implementation of image processing algorithms should be thoroughly tested and reflect the theoretical properties as accurately as possible. His work primarily centers on neuro-imaging data such as MRI and PET. His recent publications (2024-2025) reveal a strong focus on medical image analysis, with particular emphasis on tumor volume delineation, deformable image registration with physics constraints, and applications of deep learning in medical imaging. His work spans both theoretical foundations of image processing and practical clinical applications. Darkner previously held a Post Doc position at the Technical University of Denmark from February 2009 to January 2010, demonstrating his longstanding engagement with image analysis research in the Danish academic community.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +3 more

Ingemar Johansson Cox serves as a Professor within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His research bridges theoretical machine learning foundations with practical applications across medical data analysis, information retrieval, remote sensing, and sustainability initiatives. His research portfolio emphasizes machine learning applications in high-impact domains, particularly medical data analysis (e.g., early detection of gynecological malignancy using online search activity) and sustainability (e.g., reducing AI's carbon footprint). The Machine Learning section actively contributes to the university's SCIENCE AI Centre, focusing on both algorithmic innovation and real-world problem-solving in biological modeling and environmental monitoring. Recent publication trends reveal expanding work in quantum computing applications for biomolecular modeling, sustainable AI frameworks, and cross-cultural NLP systems. His 2024-2025 output demonstrates strong interdisciplinary collaboration, especially in medical informatics and climate-related AI research. Professor Cox operates within the Department of Computer Science's robust research ecosystem, which includes dedicated compute clusters and specialized initiatives like TreeSense for global tree resource monitoring through remote sensing and deep learning. The department's infrastructure supports large-scale machine learning projects requiring significant computational resources.

• Artificial Intelligence
• Medical Imaging
• Machine Learning
• +3 more

Anna Anikina is a PhD Fellow at the Department of Computer Science (DIKU), University of Copenhagen, affiliated with the Image Analysis, Computational Modelling and Geometry section. Her research focuses on applying artificial intelligence and machine learning to analyze eye-tracking data for predicting radiological diagnostic errors. Current position: PhD Fellow in Image Analysis and Computational Modelling University: University of Copenhagen Department: Image Analysis, Computational Modelling and Geometry Anna’s work spans Medical Imaging , Eye Tracking , and Transformers for diagnostic error prediction, as well as Latent-Space Modelling in reinforcement learning systems. Her recent publications highlight applications of Graph Neural Networks and Multi-Agent Systems to biomedical image analysis and visual data processing. Her research outputs include four peer-reviewed publications (2023–2025) addressing radiological decision errors through gaze analysis and advanced machine learning architectures. Collaborations with researchers like B. Ibragimov, C. Mello-Thoms, and others demonstrate her focus on AI-driven healthcare solutions.

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +4 more

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.

• Mathematical Image Analysis
• Differential Geometry
• Variational Methods
• +7 more

Francois Lauze is an Associate Professor at the Department of Computer Science , University of Copenhagen, affiliated with the Image Analysis, Computational Modelling and Geometry research group. His work bridges mathematical rigor and practical applications in image processing and shape analysis. Research Focus: Mathematical Image Analysis (variational/PDE methods) Differential and Riemannian geometry for shape statistics Applications: image inpainting, motion estimation, segmentation, medical imaging Contact: Email: francois@di.ku.dk Phone: +4535335671, +4521553933 Location: Universitetsparken 1, 2100 Copenhagen Ø Recent publications highlight advancements in SE(3) group CNNs for diffusion imaging, locally orderless networks for efficient processing, and refractive multi-view stereo techniques. His work integrates geometric modeling with computational implementations, emphasizing medical and video applications.

Anders Bjorholm DahlView profile

Professor

• Medical Imaging
• Computer Vision
• Biomedical Engineering
• +6 more

Anders Bjorholm Dahl is a Professor at the Department of Applied Mathematics and Computer Science , DTU Compute , Technical University of Denmark (DTU). His research focuses on medical imaging, computer vision, and biomedical engineering. He leads projects in ultrasound imaging, AI-driven medical diagnostics, and advanced imaging technologies for healthcare applications. Education: Ph.D. in Computer Science (Image Analysis and Computer Vision), DTU (2005–2009) Forestry, Royal Veterinary and Agricultural University (1997–2004) Research Interests: Combines machine learning and advanced imaging techniques to address challenges in medical diagnostics, including ultrasound super-resolution, stenosis detection in coronary angiographies, and material anisotropy analysis. His work bridges anatomy and histology using X-ray tomography and explores AI applications in healthcare. Key Projects: Crowd Counting through Remote Sensing (2025–2027) AI for Extreme Super-Resolution CT (2024–2026) Fighting Cancer with Generative AI (2024–2027) Labs/Teams: Leads the UltraSound and Biomechanics Visual Computing Center for Fast Ultrasound Imaging , focusing on real-time medical imaging solutions.

Dimitris ChrysostomouView profile

Associate Professor

• Robotics
• Computer Vision
• Human-Robot Interaction
• +4 more

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.

Henning Tangen SøgaardView profile

Associate Professor

• Mechanical Engineering
• Agricultural Engineering
• Control Systems
• +4 more

Henning Tangen Søgaard is an Associate Professor at the Department of Mechanical and Production Engineering, part of AU Engineering at Aarhus University. He teaches mathematics, numerical methods, and mathematical statistics to BEng students. His research focuses on robotics in agriculture , dynamic modeling , and precision agriculture . Primary Affiliation: Department of Mechanical and Production Engineering, AU Engineering, Aarhus University Research Expertise: Computer vision, control systems for agricultural robotics, environmental modeling (ammonia emissions, spray drift), and wireless sensor networks. His work includes developing autonomous systems for weed control, GPS-based geo-referencing of crops, and mathematical models for fertilizer-related emissions. Publications span peer-reviewed journals and reports in agricultural engineering , robotics , and environmental science . Scientific awards are not mentioned in the provided text. He has no listed PhD students but has collaborated on multiple projects. For direct contact, his email is hts@mpe.au.dk .

Rune W. BergView profile

Associate Professor

• Functional Neuronal Networks
• Sensory and Motor processing
• Complex physics
• +3 more

Rune W. Berg is an Associate Professor in the Promotion Programme at the Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen. He leads the Berg Lab with research focused on Neuronal Signalling and maintains an active research profile with 66 publications to date. Dr. Berg's educational background includes a Ph.D. in Biophysics from the University of California, San Diego (2003), an M.S. in Physics from UC San Diego (2000), and Cand. Scient. and B.Sc. degrees in Biophysics from the Niels Bohr Institute at the University of Copenhagen (2000 and 1997 respectively). His research interests span Functional Neuronal Networks, Sensory and Motor processing, and Complex physics. The Berg Lab investigates neural signaling mechanisms with a strong emphasis on developing novel technologies for neural interfacing and brain research. Recent work demonstrates an interdisciplinary approach combining neuroscience, physics, and engineering to create advanced tools for neural monitoring and modulation. Analysis of Dr. Berg's recent publications reveals a strong focus on neural engineering technologies, particularly optical and electromagnetic approaches for brain research. His work bridges fundamental neuroscience with practical engineering solutions for neural interfaces, with applications in understanding brain function and developing neural prosthetics. The research spans from molecular-level interactions to circuit-level neural dynamics. Dr. Berg has participated in specialized training workshops including 'Construction of the brain' at Kristineberg Marine research station, 'Neurophysics' at the Institute of Theoretical Physics in Santa Barbara, and 'Neuron as a nonlinear oscillator' at the Salk Institute. His professional experience includes continuous work since January 2004 as a Post Doctoral member of Jorn Hounsgaard's Lab at the University of Copenhagen, following a Visiting Post Doctoral fellowship at Taipei Veterans General Hospital and National Yang-Ming University in Taiwan (September-December 2003).