Find and connect with academic supervisors, professors, and scholars for your research journey.
Showing 181-195 of 223 supervisors
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Research Fellow
Ian Walter Orzel is a PhD Fellow in the Machine Learning section at the Department of Computer Science, University of Copenhagen . He is affiliated with the SCIENCE AI Centre and contributes to interdisciplinary research bridging machine learning with quantum computing, healthcare diagnostics, and environmental sustainability.
Lecturer
Simon Krogh Anderson serves as a Lecturer at the Department of Computer Science (DIKU), Faculty of Science, University of Copenhagen. He is an active member of the Machine Learning section which focuses on theoretical foundations and applications across domains including natural language processing, medical image analysis, and biological data modeling. The department participates in the SCIENCE AI Centre and maintains powerful compute resources including the TreeSense platform for remote sensing. His research spans multiple cutting-edge areas in artificial intelligence with particular emphasis on machine learning, quantum computing applications, and algorithmic fairness. Key interests include sustainable AI development, reproducibility in recommender systems, and cross-cultural adaptation frameworks. His work often bridges theoretical computer science with practical applications in environmental monitoring, healthcare analytics, and quantum information processing. Recent publications demonstrate strong interdisciplinary connections across quantum computing, sustainable AI, and fairness metrics. Trends show increasing focus on environmentally conscious AI development, integration of quantum methods with classical machine learning, and ethical considerations in recommendation systems. His work frequently leverages Denmark's extensive health registries and environmental data resources. Anderson actively contributes to the department's research ecosystem through teaching and collaboration within the Machine Learning section. While specific grants aren't detailed in available materials, his publications indicate involvement in projects related to quantum computing infrastructure, environmental monitoring systems, and AI ethics frameworks. The department provides significant computational resources including a dedicated cluster and specialized labs like TreeSense for remote sensing applications. His work appears connected to the SCIENCE AI Centre's initiatives in sustainable computing and quantum information processing.
Lecturer
Marko Bauer serves as a Lecturer at the Department of Computer Science, University of Copenhagen, contributing to the department's research and educational mission within the Faculty of Science. His academic profile is centered on interdisciplinary work bridging computer science and medical applications, with a physical office at Universitetsparken 1, 2100 Copenhagen Ø. Bauer's research expertise lies in medical image analysis, specializing in deep learning applications for brain MRI processing. He develops innovative augmentation techniques to address data scarcity in neurological diagnostics, particularly focusing on ischemic stroke lesion segmentation. His methodological contributions include gamma distribution modeling and variational autoencoder frameworks for generating synthetic medical imagery, advancing the precision of computer-aided diagnosis systems in neurology. Analysis of his recent publications reveals a concentrated research trajectory in medical AI, with two significant 2023-2024 publications demonstrating technical innovation in instance-specific MRI augmentation. These works exemplify cross-disciplinary collaboration between computer vision and clinical neuroscience, addressing critical challenges in medical image segmentation through novel generative approaches that enhance model robustness with limited training data. As an integral member of DIKU's research ecosystem, Bauer aligns with the Image Analysis, Computational Modelling, and Geometry section and Machine Learning section. His work contributes to the department's broader engagement with the SCIENCE AI Centre, fostering synergies between theoretical computer science and real-world healthcare applications through collaborative projects with medical imaging specialists.
Lecturer
Nicklas Boserup serves as an Instructor at the Department of Computer Science (DIKU) at the University of Copenhagen, located at Universitetsparken 1 in Copenhagen Ø. His academic appointment places him within the institution's research and teaching structure, contributing to computer science education and research activities. Boserup's research interests center on machine learning applications in medical imaging, with particular focus on self-supervised learning techniques for histopathology image analysis. His work bridges computer vision and healthcare applications, developing algorithms that can process and segment medical images without requiring extensive labeled training data. This research direction addresses critical challenges in digital pathology where annotation by medical experts is time-consuming and costly. Analysis of Boserup's publication record shows a consistent focus on contrastive learning approaches for medical image segmentation, with his 2022-2023 work establishing foundational techniques in patch-based contrastive learning for histopathology. His 2024 publication represents an expansion into parameter inference methods using differentiable diffusion models, indicating a broadening research scope into statistical computing and probabilistic modeling. The trajectory suggests increasing sophistication in handling complex medical imaging data through advanced machine learning techniques. Boserup maintains an active research profile with publications appearing in both preprint repositories and conference proceedings, demonstrating engagement with the academic community. His email contact (nibos@di.ku.dk) provides a direct channel for academic collaboration and professional communication within the university framework.
Lecturer
Dongyu Gao serves as an Instructor in the Machine Learning section at the Department of Computer Science (DIKU), University of Copenhagen. His position places him within one of Scandinavia's leading computer science departments, which hosts the SCIENCE AI Centre and maintains strong connections with both theoretical and applied machine learning research. Dr. Gao's research interests center around machine learning with applications spanning information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. His work appears to bridge theoretical foundations with practical implementations, as evidenced by publications addressing quantum computing applications, environmentally sustainable AI practices, and advanced neural network architectures. The Machine Learning section at DIKU provides substantial computational resources including a powerful dedicated cluster and specialized initiatives like TreeSense for remote sensing applications. Analysis of recent publications associated with Dr. Gao reveals a diverse research portfolio spanning multiple cutting-edge AI domains. His work demonstrates particular strength in quantum machine learning applications, sustainable computing practices, and interpretable AI systems. The publications show a consistent pattern of interdisciplinary collaboration, connecting computer science with healthcare, environmental science, and quantum physics. Notably, several publications address the critical challenge of making AI systems more environmentally sustainable without sacrificing performance. The Machine Learning section operates within DIKU's broader research ecosystem, which includes strong connections to the SCIENCE AI Centre. This environment provides access to substantial computational resources and fosters collaboration across various AI subfields including natural language processing, computer vision, and theoretical machine learning. The department's location in Copenhagen positions it at the intersection of European AI research initiatives with strong connections to both academic and industry partners across the continent.
Associate Professor
Bulat Ibragimov is an Associate Professor in the Department of Computer Science at the University of Copenhagen, specializing in the Image Analysis, Computational Modelling, and Geometry research section. His work focuses on applying advanced computational techniques to medical imaging problems, particularly in radiology and diagnostic applications. His research interests span multiple domains of medical AI, including: Medical image analysis and computer vision for diagnostic applications Eye tracking analysis to understand radiologist decision-making processes Deep learning applications for disease detection and severity classification Explainable AI systems for medical image interpretation Computational geometry applications in medical imaging Dr. Ibragimov's recent publications demonstrate a strong focus on applying artificial intelligence to improve diagnostic accuracy and efficiency in medical settings. His work frequently bridges the gap between computer science and clinical applications, with particular emphasis on radiology, cardiology, dentistry, and gastroenterology. Many of his studies incorporate eye tracking data to better understand and augment human decision-making in medical imaging contexts. His notable contributions include work on: Cardiometric analysis from chest X-rays Dental image analysis for abnormality detection Ulcerative colitis severity classification Explainable AI for medical image models Prediction of radiological decision errors using eye tracking data
Jakob Hestbjerg Karrer is an Instructor at the Department of Computer Science , University of Copenhagen. He is affiliated with the Image Analysis, Computational Modelling, and Geometry section, focusing on research areas such as image processing, computer vision, computational modelling, geometry, and machine learning. Position: Lecturer (mapped from "Instructor") Location: Universitetsparken 1, 2100 Copenhagen Ø
Lecturer
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
Mathias Weirsøe Klitgaard is a Lecturer at the Department of Computer Science, University of Copenhagen. He is affiliated with the Image Analysis, Computational Modelling, and Geometry section, which conducts research spanning theoretical analyses, algorithm development, and practical applications in image processing, computer simulation, and geometric statistics. His work aligns with the section's focus on interdisciplinary research, bridging computational methods with real-world challenges in science and industry. The department provides access to advanced facilities, including a powerful compute cluster and modern research labs.
Lecturer
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.
Lecturer
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
August Maigaard Rubin is an Instructor at the Department of Computer Science , Faculty of Science, University of Copenhagen, and holds a secondary affiliation with the Department of Communication (Faculty of Humanities). His research spans image analysis, computational modelling, geometry, machine learning, computer vision, and numerical optimization. Infrastructure and collaborations include the SCIENCE AI Centre, a GPU cluster, and physical/virtual research facilities like robot labs and toolshops. Education details were not explicitly provided. Research Focus : Image Analysis & Processing Mathematical Imaging & Applied Geometry Medical & Biological Imaging Computer Vision, Robotics, Graphics & Simulation Emails : auru@di.ku.dk zkb163@hum.ku.dk Collaborations : SCIENCE AI Centre Center for Quantification of Imaging Data from Max IV (QIM) Denmark’s participation in open-source projects like OpenTissue and PROX
Jacob Aleksandar Siegumfeldt is an Instructor at the Department of Computer Science (DIKU), University of Copenhagen. Key Affiliation : Pioneer AI Section, DIKU Research in the Pioneer AI section focuses on foundational topics in artificial intelligence, including: Computer Vision (fine-grained classification, self-supervised learning, multimodal AI for misinformation detection) Medical Image Analysis (large-scale observational studies, brain data analysis) Geometric Statistics (shape modeling, phylogenetic inference) Teaching activities in the section include machine learning, deep neural networks, vision/image processing, and data science courses.
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.
