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Peter Uhd JepsenView profile

Professor

• Terahertz Engineering
• Time Domain Engineering
• Photonics
• +7 more

Peter Uhd Jepsen is a Professor and Deputy Head of Department at the Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU). He leads the Ultrafast Infrared and Terahertz Science section, focusing on advanced spectroscopic techniques and photonic materials. His work contributes to UN Sustainable Development Goals through innovations in photonics and materials science. Expertise: Terahertz Engineering, Photonics, Graphene, Ultrafast Spectroscopy Institution: Technical University of Denmark (DTU) Department: Electrical and Photonics Engineering Role: Professor, Deputy Head of Department, Head of Section His research centers on terahertz time-domain spectroscopy (THz-TDS), ultrafast photonics, and the optical properties of two-dimensional materials such as graphene and MoS2. He investigates complex conductivity, plasmonic effects, and dielectric permittivity using advanced terahertz nanoscopy and solid-state detection methods. His work bridges fundamental physics with practical applications in sensing, materials characterization, and next-generation optoelectronic devices. The recent publications reveal a strong trend in applying machine learning to THz spectroscopy, probing anisotropic responses in nanostructures, and extracting material properties at the nanoscale. Key fields include terahertz photonics, 2D materials, metamaterials, and nonlinear optics, with interdisciplinary applications in energy, quantum technologies, and biomedical sensing. Dr. Jepsen has supervised multiple PhD students in active projects such as Terahertz Lightwave Electronics and Confined Water in Artificial and Biological Systems . He has secured significant research grants, including projects on perovskite solar cells and non-invasive skin analyte monitoring. His collaborations span institutions in Denmark and internationally. He is involved in the Ultrafast Infrared and Terahertz Science research group at DTU, where he leads experimental efforts in terahertz generation, detection, and spectroscopic applications. The team focuses on developing novel instrumentation and methodologies for ultrafast optical science.

Thiusius Rajeeth SavarimuthuView profile

Professor

• Computer Vision
• Robotics
• Artificial Intelligence
• +3 more

Thiusius Rajeeth Savarimuthu is a Professor and Head of Unit at SDU Robotics , part of the University of Southern Denmark . His work bridges robotics, computer vision, and biomedical applications, with a focus on AI-driven medical imaging, surgical robotics, and human-robot interaction. His research explores diabetic retinopathy diagnostics via deep learning, robot-assisted ultrasound , bioimpedance sensing , and robot suturing . Projects like AIRCARE (AI-augmented robotics for cancer care) and RAPID (Robotic Arterial Puncture Device) highlight his commitment to healthcare innovation. Recent publications emphasize AI in medical imaging , robotic needle guidance , and automated diagnostics . His work is recognized through awards including the euRobotics Entrepreneurship Award 2023 and KUKA Innovation Award 2022 . Scientific Awards euRobotics Technology Transfer Award (2023) euRobotics Entrepreneurship Award (2023) KUKA Innovation Award (2022) Best Student Paper Award (2021) Innovation Prize - SDU (2020) He teaches Medical Imaging courses and supervises PhD projects on data-efficient robot learning and biomedical AI methods , reflecting his dedication to advancing robotics education and healthcare technology.

Mikkel Theiss KristensenView profile

Researcher

• Astrophysics
• Active Galactic Nuclei
• Dwarf Galaxies
• +7 more

Mikkel Theiss Kristensen is a Postdoctoral Researcher in the Department of Physics, Chemistry and Pharmacy at the University of Southern Denmark (SDU). He is actively involved in astrophysical research with a focus on active galactic nuclei (AGN), dwarf galaxies, and quasar systems, employing advanced computational and machine learning techniques. His research interests span astrophysics , galaxy evolution , machine learning applications in astronomy , and science outreach . He investigates the environments and merger histories of dwarf galaxies hosting AGN, using simulations like IllustrisTNG and observational data from surveys such as SDSS. His recent work includes developing novel machine learning models to identify dwarf AGN candidates, contributing to gravitational lensing studies, and promoting STEM education through international outreach. The recent publications reveal a strong trend in combining computational astrophysics with data-driven discovery , particularly in identifying rare astronomical objects. His work bridges theoretical modeling, observational analysis, and educational innovation. Dr. Kristensen is actively engaged in teaching and public outreach. He teaches courses in Astrophysics and Introduction to Cosmology and Fundamental Astronomy , and has delivered numerous conference presentations and guest lectures on planetariums and interactive learning environments. He led outreach projects such as ThaiPASS and regional physics clubs, emphasizing STEM education for high school students. His projects include: Fysikklubber i Region Syddanmark : A project supporting high school physics clubs in Southern Denmark (2023–2025). National Youth Space Conference 2023 : A one-day event promoting youth engagement in space science. He has presented at multiple conferences and participated in workshops, demonstrating a commitment to knowledge dissemination and collaborative research.

Mirko SalewskiView profile

Professor

• Plasma Physics
• Fusion Energy
• Energetic Particle Physics
• +16 more

Mirko Salewski is a Professor in the Department of Physics at the Technical University of Denmark (DTU), specializing in Plasma Physics and Fusion Energy. He is actively involved in international fusion research, working with major tokamaks and stellarators such as JET, ASDEX Upgrade, DIII-D, EAST, MAST, TCV, Wendelstein 7-X, and LHD. He serves as an ITER Scientist Fellow and chairs the ITPA Topical Group on Energetic Particle Physics, coordinating global research efforts to support ITER. Education: Dr. techn. in Plasma Physics, Technical University of Denmark (2020) PhD in Fluid Mechanics, Lund University (2007) MSc in Aerospace Engineering, Technical University of Berlin (2002) BSc in Mechanical Engineering, RWTH Aachen University (1998) Additional studies at University of Washington (2000–2001) and Moscow Aviation Institute (2000) His research focuses on developing advanced diagnostics for energetic particles in fusion plasmas using tomography in velocity- and phase-space. He integrates physics-informed models and neural networks to improve reconstruction accuracy. His work spans experimental diagnostics including fast-ion D-alpha spectroscopy, collective Thomson scattering, neutron and gamma-ray spectroscopy, neutral particle analyzers, fast-ion loss detectors, and ion cyclotron emission. Recent publications emphasize synthetic diagnostics, phase-space tomography, and AI-enhanced modeling, reflecting a strong trend toward data-driven and physics-integrated approaches in fusion research. His work is central to understanding fast-ion behavior in burning plasmas. Scientific Awards: ITER Scientist Fellow Professor Salewski supervises multiple PhD students and leads significant research projects funded by EU and national programs. His projects include fast-ion phase-space tomography in D-T plasmas at JET, AI applications in fusion tomography, and orbit-space sensitivity studies. He teaches courses in Electromagnetism, Plasma Physics, and the Physics of Sports at BSc, MSc, and PhD levels. He is affiliated with key fusion research networks and collaborates extensively with institutions across Europe, the US, and Asia, contributing to the global effort to realize fusion energy.

Bernd DammannView profile

Associate Professor

• High-Performance Computing
• Scientific GPU Computing
• Parallel Programming
• +3 more

Bernd Dammann is an Associate Professor in Scientific Computing at the Technical University of Denmark (DTU) , Department of Applied Mathematics and Computer Science. In parallel he serves as Scientific Lead & Architect in the DTU Computing Center (DCC) and co-founded DTU’s GPUlab in 2007, giving him dual roles in both academic research and institutional HPC services. Education Dipl.Phys. in Statistical Physics, University of Mainz, 1992 PhD in Physical Chemistry, DTU, 1996 Research interests Over almost three decades Dammann has focused on computational methods for large-scale scientific problems. His core expertise lies in: High-Performance Computing and parallel programming paradigms (OpenMP, MPI, GPU) Scientific GPU computing and many-core architectures Application tuning and performance optimization for heterogeneous systems Uncertainty quantification, Monte-Carlo methods, and real-time control applications in biomedicine (diabetes technology) Ultrasound beamforming and advanced imaging techniques Publication trends His 34 publications (1996-2025) trace a clear evolution from theoretical physics and surface-science simulations to GPU-accelerated engineering and biomedical systems. Recent work clusters around real-time biomedical control (artificial pancreas, virtual clinical trials) and high-resolution ultrasound imaging, all leveraging GPU acceleration and high-performance computing to achieve real-time performance. Scientific awards & recognition Contributor to 1 patent family and widely downloaded technical reports (>10 k downloads for OpenCL tutorial) Consistently high citation counts on key optimization and control papers (e.g., 93 citations for 2014 Management Science article) Teaching, supervision & grants He teaches or co-teaches multiple DTU courses: High-Performance Computing , Large-scale Computations , Python & HPC , Mathematical Software , and HPC with FORTRAN . While individual students are not named, his role in GPUlab and DCC implies ongoing PhD and MSc supervision, and his projects list six active collaborations including EU and national HPC infrastructure grants. Labs & teams Dammann co-founded GPUlab (2007) and presently leads the HPC architecture team in the DTU Computing Center , a central facility supplying cluster resources to the entire university.

Gavrielle UntrachtView profile

Assistant Professor

• Biomedical Engineering
• Biophotonics
• Medical Imaging
• +3 more

Gavrielle Untracht is an Assistant Professor at the Department of Health Technology, Technical University of Denmark. Her work focuses on biomedical imaging, particularly using optical coherence tomography (OCT) for clinical and diagnostic applications. Role: Supervisor and researcher in interdisciplinary projects. Department: Health Technology, DTU. Research Interests: Centered on Biomedical Engineering , Biophotonics , and Medical Imaging , with applications in Cancer Research , Diabetes Research , and Machine Learning in Diagnostics . Publication Trends: Recent works explore OCT integration with machine learning, 3D tumor scaffolds for drug testing, and optical property analysis in soft tissues. These highlight her focus on advanced imaging systems and biomedical diagnostics . Supervision: She mentors PhD students in projects related to OCT technology, diabetes, and cancer imaging.

• Astronomy
• Astrophysics
• Galaxy Formation and Evolution
• +5 more

Sune Toft is a Professor at the Niels Bohr Institute , University of Copenhagen, and a key member of the Cosmic Dawn Center (DAWN) . His research focuses on galaxy formation, evolution, and cosmology, particularly through observations of high-redshift galaxies using the James Webb Space Telescope (JWST) and Euclid mission data. Education: PhD in Astronomy, 2003 Research Interests include galaxy mergers, dark matter halos, stellar mass assembly, and photometric redshift calibration. He leads major projects like the COSMOS-Web and DAWN JWST Archive , analyzing galaxy morphology and clustering across cosmic time. His work with the Euclid Collaboration spans instrument calibration, mock catalogues, and early universe studies. Scientific Contributions highlight trends in galaxy interactions, disk instabilities, and tidal disruption events. His peer review activities extend to journals like The Astrophysical Journal Letters and Monthly Notices of the Royal Astronomical Society . Press/media coverage includes the 2017 Hubble discovery of a 'massive dead disk galaxy' challenging galaxy evolution theories, with ongoing media engagement via blogs, social media, and Mendeley discussions.

Kenny ErlebenView profile

Professor

• Physics-based modeling and simulation
• Robotics Automation
• Data-driven modeling
• +13 more

Kenny Erleben is a Professor and Head of the IMAGE research section at the Department of Computer Science, University of Copenhagen . His work integrates physics-based simulation with data-driven approaches, advancing fields like robotics, machine learning, and computer graphics. He leads a team of 10 within IMAGE, a section housing 10 faculty, 2 postdocs, and 20 PhDs, with research foci in computer vision, medical image analysis, and numerical methods. Education: PhD in Computer Science, University of Copenhagen (2005) Erleben’s research interests span physics-based modeling and simulation , robotics automation , data-driven modeling , and digital twins . His team develops numerical methods for interactive simulation, bridging real-world data with synthetic models for applications in surgical simulators and computer games. Scientific production trends include differentiable simulation pipelines, collision detection algorithms, and friction modeling. Key applications involve NVIDIA’s PhysX/Isaac engines, surgical robotics, and medical imaging. His awards include four best paper prizes, reflecting his impact on simulation and robotics. Supervision: 15 PhDs, 8 Postdocs, and 170+ theses Grants: HORIZON-RIA (€6.19M), EU MSCA-ITN RAINBOW (€4M+), and other Innovationsfond/DFF awards Erleben’s community leadership includes coordinating the Danish Academy of Digital Interactive Entertainment (DADIU) and reviewing for 300+ scientific papers. He has held roles like Deputy Head of Department (2012-2015) and Scientific Advisor at Alexandra Institute.

• Diabetes
• Diabetic eye disease
• Diabetic retinopathy
• +7 more

Simon Joel Lowater is a PhD student at the Department of Clinical Research , University of Southern Denmark , associated with the Research Unit of Ophthalmology (Odense) . His research focuses on diabetic eye disease , machine learning applications in ophthalmology, and simulation-based medical education . Education: Cand. Med. (Medical Degree) Research Interests include diabetic retinopathy, ocular sarcoidosis, clinical trials, and advanced imaging techniques like optical coherence tomography. His work explores AI-driven lesion annotation reliability and simulation tools for ophthalmology resident training. Recent Publications address retinal thickness in sarcoidosis, diabetic retinopathy annotation standards, slit lamp simulator validation, and ranibizumab delivery systems. His studies span cross-sectional designs, Delphi methodology, and narrative reviews. Collaborations involve institutions like Karolinska Institute (KI) and Odense University Hospital (OUH), with international co-authors. Media attention includes AI screening for diabetic retinopathy (2025).

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +7 more

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.

• Machine Learning
• Quantum Computing
• AI Ethics
• +4 more

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.

• Machine Learning
• Quantum Computing
• Federated Learning
• +5 more

Hubert Marek Drazkowski is a PhD Fellow at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning . He is affiliated with the Machine Learning Section and the SCIENCE AI Centre. Email: hubert.drazkowski@di.ku.dk Location: Universitetsparken 1, 2100 Copenhagen Ø His research spans quantum computing , environmentally sustainable AI , biomolecular modeling , and recommender systems . He contributes to interdisciplinary projects in healthcare informatics , remote sensing , and cross-cultural AI applications . Recent work includes quantum-inspired neural networks , interpretability in large language models , and fairness evaluation frameworks . He collaborates with labs like TreeSense on geospatial deep learning projects.

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +3 more

Lennard Hilgendorf is a Researcher at the Department of Computer Science , University of Copenhagen . His work focuses on Machine Learning with applications in quantum computing, medical imaging, natural language processing, and environmental sustainability. Research Trends: His recent publications highlight interdisciplinary work at the intersection of quantum mechanics and machine learning, efficient AI architectures for environmental sustainability, explainable models for medical diagnostics, and multimodal approaches to ecological monitoring. Key keywords include Machine Learning , Quantum Computing , Medical AI , and Environmental Science . Labs & Collaborations: Affiliated with the SCIENCE AI Centre and the TreeSense Centre , which specialize in foundational machine learning research and remote sensing for global tree resources, respectively.

• Machine Learning
• Quantum Computing
• AI Efficiency
• +5 more

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.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +5 more

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.