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 .
Robert Krarup Feidenhans'l is a Professor in X-ray Physics and Visiting Professor in Condensed Matter Physics at the Niels Bohr Institute, University of Copenhagen. His research bridges fundamental physics with practical applications in materials characterization, biological imaging, and nanotechnology through advanced X-ray techniques. Feidenhans'l earned his Master's degree in Physics and Mathematics in 1983 and his Ph.D. in Physics from the University of Aarhus in 1986, where he was awarded the A. Angelo prize. His career progressed from staff scientist at Risø National Laboratory to leadership positions including Head of the Materials Research Department at Risø, Professor at the Niels Bohr Institute, Vice Institute Leader for Research, and Interim Institute Leader. His research interests span Surface and Interface Science, X-ray Physics, Synchrotron Radiation, Materials Science, Crystallography, Nanoscience, and Biophysics. His work demonstrates a progression from fundamental materials research toward innovative applications of X-ray techniques in biological systems while maintaining strong contributions to materials characterization. With approximately 140 publications and an h-index of 31, his research has garnered over 3,940 citations as of 2012, reflecting significant impact across multiple disciplines. Feidenhans'l has held prestigious leadership positions including Chairman of the Council of European Synchrotron Radiation Facility (2006-2010, with a €90M annual budget and 600 employees), Chairman of the European XFEL Council (2010-present, overseeing a €1.1 billion construction project), and Director of DANSYNC/DANSCATT. He has served on advisory committees for major international facilities including MAXLAB in Lund, HASYLAB/DESY, and the LCLS in Stanford. As an educator, Feidenhans'l has supervised approximately 21 master's students and 15 Ph.D. students, currently guiding 6 Ph.D. and 8 master's students. He teaches core courses including Introduction to Solid State Physics, Experimental X-ray Physics, and Structural Tools in NanoScience. His leadership extends to chairing the PhD Committee at the Faculty of Sciences and serving on the Academic Council of the Faculty of Science at the University of Copenhagen. He has co-organized numerous international conferences and summer schools, including the Nordic Summerschool on Synchrotron Radiation and the International Conference on Solid Films and Surfaces.
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.
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 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.
Lisbet Tarp is an Associate Professor in Art History at the School of Communication and Culture, Aarhus University. Her academic work bridges traditional art historical scholarship with digital methodologies and interdisciplinary research, particularly in the fields of painting, materiality, and conservation. She is actively engaged in several high-impact research projects exploring the intersections of art, science, and technology. Research Interests: Digital Art History and computational analysis of paintings Materiality and technique in historical and contemporary painting Conservation science and hidden layers in artworks Mathematics and geometry in visual art Early modern court culture and ceremonial representation Interdisciplinary practices between art and anatomy Her recent publications reflect a strong trend toward integrating digital tools into art historical inquiry, with a focus on uncovering the material and technical dimensions of paintings. Projects such as Digital Art History: Rediscovering the Painting and ANAT: Anatomical Theater demonstrate her innovative approach to visual culture through scientific and digital lenses. Her work often involves collaborative, peer-reviewed publications and digital dissemination. Scientific Projects & Activities: ANAT: Anatomical Theater (2023–2029) – Investigating dissection as aesthetic practice in early modern and contemporary contexts Digital Art History: Rediscovering the Painting (2019–2022) – Using digital methods to analyze painting techniques and material composition MoCMa: Mobility Creates Masters (2017–2019) – Studying transnational influences in European art LUMEN Center (2015–2025) – Researching Lutheran theology and its impact on confessional societies and visual culture Lisbet Tarp has contributed extensively to academic discourse through peer-reviewed journals, anthologies, and digital publications. While no formal students or awards are listed, her leadership in major research initiatives underscores her scholarly influence. She employs digital platforms for both research and pedagogy, including student-organized seminars and open-access digital versions of her work.
Marco Pizzolato is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in Visual Computing with a focus on Magnetic Resonance Imaging (MRI), particularly diffusion MRI and biophysical modeling. He is also affiliated with the inter-departmental Microstructure & Plasticity (MAP) research group and has held visiting positions at the University of Verona, EPFL, and DRCMR. His educational background includes a PhD in Signal and Image Processing from INRIA Sophia Antipolis, a Master’s in Bioengineering from the University of Padua, and a Bachelor’s in Biomedical Engineering from the same institution. He previously served as an Assistant Professor at DTU and was a postdoctoral researcher under the Marie Curie COFUND Eurotech programme. Dr. Pizzolato's research centers on image and signal denoising, inverse problems, optimization, diffusion MRI, tractography, and Monte Carlo simulations. He actively contributes to the development of microstructural models for brain imaging, with applications in neurodegenerative diseases and brain connectivity. His work aligns with UN Sustainable Development Goals, particularly in advancing education and health through imaging technology. The recent publications reflect a strong trend in advancing diffusion MRI techniques, including ACID imaging, microscopic propagator modeling, myelin integrity mapping, and multi-scale white matter organization. These works emphasize biophysical accuracy, model validation, and integration across imaging modalities and species. Magna Cum Laude , ISMRM 2020 Magna Cum Laude , ISMRM 2022 First Place , Macaque Validation Challenge at ISBI 2018 First Place (Overall and HCP) , IronTrack Challenge 2019 (MICCAI) MICCAI Student Travel Award 2015 He has supervised PhD students such as Thøgersen, T. L. and Corral Bolaños, M. in projects related to microstructure MR imaging and myelin mapping. He has also been involved in significant grants and collaborative projects, including the Multimodal Microstructure-Informed Connectivity (MMINCARAV) initiative between Inria and EPFL, and the Sinergia consortium for Brain Communication Pathways . He co-organized multiple international events, including the MICCAI CDMRI workshops and challenges (2019–2021), and the ESMRMB Leaps in Microstructure Imaging workshop (2024). Dr. Pizzolato is an active member of the scientific community, serving as an editor for MICCAI workshop proceedings, a reviewer for major journals and conferences, and an invited speaker at ISMRM 2025. He leads and participates in several ongoing research projects at DTU focused on quantitative imaging, myelin mapping, and MRI-based connectivity, demonstrating sustained research leadership and external funding success.
Hans Martin Kjer is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the UltraSound and Biomechanics group within the Visual Computing Center and the Center for Fast Ultrasound Imaging. His research bridges engineering and medical imaging, with a strong emphasis on developing and validating advanced ultrasound techniques for biomedical applications. Research Interests: His work focuses on super-resolution ultrasound imaging, microvascular analysis, 3D reconstruction of biological structures, and image registration. He applies computational methods to improve the resolution and accuracy of ultrasound, particularly in renal and lymph node vasculature imaging. His research contributes to the UN Sustainable Development Goals in health and well-being through innovative diagnostic tools. Publication Trends: Over the past several years, Kjer has consistently published in high-impact journals and conferences in biomedical engineering and imaging. His recent work emphasizes the validation of super-resolution ultrasound against micro-CT, realistic 3D blood flow simulation, and the application of AI in enhancing imaging resolution. These studies reflect a strong trend toward quantitative, reproducible, and clinically relevant imaging solutions. Scientific Contributions: While no specific awards are listed, his leadership in major research projects and frequent collaborations with leading experts in ultrasound (e.g., Jørgen Arendt Jensen) underscore his significant role in the field. Advising and Funding: Kjer serves as a supervisor and principal investigator in several funded research initiatives, including AI for Extreme Super-Resolution CT , 3DIM: 3D Imaging Center , and QIM: Center for Quantification of Imaging Data from Max IV . He mentors PhD students and collaborates across disciplines, contributing to both biomedical and materials science imaging projects. Laboratories and Teams: He is an integral member of the Center for Fast Ultrasound Imaging and the Visual Computing Center at DTU. These teams focus on cutting-edge ultrasound technologies, image processing algorithms, and multimodal imaging integration, positioning Kjer at the forefront of computational biomedical imaging in Denmark.
Hans Magnus Henrik Lundell is an Associate Professor at the Department of Health Technology, Technical University of Denmark , specializing in Magnetic Resonance research. His work bridges biomedical engineering and neuroscience through advanced imaging techniques. Active in diffusion MRI and neurodegeneration research Current supervisor of two PhD students in multi-modal imaging projects Contributor to 11 publications with international collaborations Focus on tumor microstructure and cerebellar imaging applications His research on Diffusion-weighted MRS and time-dependent diffusion imaging has applications in glioblastoma diagnostics and neurodegenerative disease studies. Recent work explores clinical MRI-Linac integration for radiotherapy monitoring and extracellular diffusion dynamics in human tissue.
Knud Henriksen serves as a Part-time Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Image Analysis, Computational Modelling and Geometry research section. His academic profile integrates teaching and research in computer graphics, physics-based animation, and computer vision, contributing to DIKU's expertise in visual computing methodologies. His educational background comprises: Elektroingeniør (Electrical Engineering) Cand. Scient (Master of Science) Ph.D. Henriksen's research focuses on Computer Graphics, Physics-Based Animation, Computer Vision, and Mathematics, with current investigations into inverse kinematics using bone representations and quaternions. His work emphasizes improving computational efficiency and realism in character animation through mathematical modeling and geometric algorithms. Analysis of his 30 publications (2002-2008) reveals consistent innovation in animation algorithms, particularly in inverse kinematics, physics simulation, and spline-based modeling. These contributions bridge theoretical computer graphics with practical applications in gaming and virtual reality, demonstrating robust solutions for 3D interaction and character rigging. No scientific awards are documented in the available information. Henriksen has supervised graduate projects including a 2008 thesis on quaternion-based inverse kinematics. While specific grant details are absent, his research aligns with DIKU's interdisciplinary approach. He operates within the Image Analysis, Computational Modelling and Geometry section, which collaborates extensively with industry on visual computing challenges.
Jon Sporring is a Professor at the Department of Computer Science, University of Copenhagen, specializing in theoretical and applied image processing, stochastic geometry, and biomedical imaging. He leads research in mathematical and medical image analysis, computer graphics, and pattern recognition. Education: Ph.D. in Computer Science (1998), Master in Computer Science (1995), both from University of Copenhagen Affiliations: Pioneer AI section, Applied Geometry Lab, and Faculty of Science External Roles: Visiting professor at McGill University (2012-13), co-founder of DigiCorpus Aps (2012-16) His research integrates scale-space theory, statistical shape analysis, and advanced imaging techniques for applications in medical diagnostics, materials science, and neuroscience. Recent work focuses on 3D reconstruction, persistent homology for bias correction, and AI-driven biomedical analysis. Jon teaches computer science at all academic levels, currently offering courses in bioimaging, signal processing, and deep learning. He emphasizes collaborative projects with external partners and has held administrative roles including Vice-Chair for Research at DIKU. His 15 most recent publications reflect expertise in medical imaging, 3D modeling, and AI applications, with subfields spanning neurodegenerative disease analysis, mitochondrial ultrastructure, and multi-scale image processing. Articles demonstrate interdisciplinary impact across medicine, biology, and materials science.
Ameer Tamoor Khan serves as a Postdoctoral Researcher (Research Fellow) in the Section for Crop Sciences within the Department of Plant and Environmental Sciences at the University of Copenhagen's Faculty of Science. Based at the Taastrup campus (Højbakkegård Allé 13, 2630 Taastrup), his work bridges agricultural science and computational intelligence with direct contact via atk@plen.ku.dk and +4535327865. His research centers on AI-driven solutions for agricultural challenges, specializing in computer vision applications for precision farming. Key focus areas include pest and weed detection using YOLO architectures (v8/v11), leaf phenotyping analysis, neural network-based supply chain optimization, and evolutionary computation for model-free systems. This interdisciplinary work integrates deep learning, operational research, and agricultural engineering to enhance food security and farming efficiency through real-time computational approaches. Analysis of his 2025 publications reveals a dominant trend in applying computer vision to agricultural robotics, particularly in multi-crop monitoring systems. His work consistently connects neural dynamics with practical farming applications, spanning from cotton weed management to portfolio risk optimization, demonstrating cross-domain versatility in computational intelligence. No scientific awards were documented in the source material. Information regarding student supervision or research grant acquisition was not provided in the available records. While specific laboratory affiliations weren't detailed, his research output indicates active collaboration within computational agriculture networks, particularly with co-authors Jensen and Mirjalili across multiple institutions.
Felix Björklund Osmark is an Lecturer at the Department of Computer Science , University of Copenhagen. His work is affiliated with the Machine Learning section and the SCIENCE AI Centre, focusing on interdisciplinary applications of artificial intelligence. Current research interests include quantum computing , natural language processing , and sustainable AI . Recent publications highlight hybrid quantum-classical systems, biomedical applications of machine learning, and ethical considerations in AI development. His collaborative efforts span domains like structural biology , climate science , and medical diagnostics , leveraging the department's compute cluster and TreeSense research center resources.
David Robert Shannon is an Instructor at the Department of Computer Science, University of Copenhagen. He contributes to teaching and research within the Machine Learning section, which participates in the SCIENCE AI Centre. University: University of Copenhagen Department: Department of Computer Science Section: Machine Learning His research interests span theoretical and applied machine learning, focusing on natural language processing, information retrieval, medical image analysis, computational biology, and quantum computing applications. He utilizes the department's powerful compute cluster for projects involving AI's environmental impact, quantum algorithms, and biomedical data modeling. Recent publications highlight work in quantum-inspired neural networks, sustainable AI, medical diagnostics, and cross-cultural computational frameworks. Key themes include ethical considerations in AI, hybrid quantum-classical systems, and multimodal data analysis. David collaborates with the Machine Learning section and SCIENCE AI Centre, leveraging resources like TreeSense for remote sensing and deep learning of global tree resources. The section's activities range from foundational research to applications in sustainability and biological data modeling.
Elias David Michael Theil is a PhD Fellow at the Department of Mathematical Sciences, University of Copenhagen, specializing in quantum mathematics and computational materials science. His research interests include: Quantum Algorithms Quantum Mathematics Materials Characterization Texture Analysis Metal Forming Processes Computational Mechanics Dr. Theil's work bridges theoretical mathematics with industrial applications, particularly in materials characterization and metal processing. His recent publications demonstrate strong interdisciplinary collaboration between mathematics and materials science, with papers appearing in Materials Characterization and Crystals . His research shows increasing impact, with his 2024 paper on metal forming modeling receiving 4 Scopus citations and his 2025 clustering algorithm paper gaining early attention in the field. Dr. Theil is based at Universitetsparken 5, Copenhagen Ø (DK-2100) and can be contacted at edmt@math.ku.dk.
