جست‌وجوی استاد و پژوهشگر

• Mathematical Image Analysis
• Variational Methods
• Partial Differential Equations
• +۶ مورد دیگر

Francois Bernard Lauze is an Associate Professor at the Department of Computer Science, University of Copenhagen, specializing in the Image Analysis, Computational Modelling and Geometry section. His research bridges mathematical image analysis and geometric methods for shape analysis in medical imaging. His primary research interests include variational methods, partial differential equations, and Riemannian geometry applied to image inpainting, motion estimation, segmentation, and medical imaging. Recent work focuses on physics-integrated deformable registration, SE(3) group equivariant neural networks, and multi-view 3D reconstruction techniques. The trends in his publications highlight interdisciplinary approaches combining geometry, computer vision, and deep learning. Notable examples include locally orderless networks, SE(3) group convolutions for DWI segmentation, and RNb-NeuS for 3D reconstruction. He is affiliated with the Pioneer AI section at DIKU, which anchors foundational research in artificial intelligence, machine learning, and medical imaging. The section hosts the Applied Geometry lab and participates in major international conferences like MICCAI and CVPR.

• Natural Language Processing
• Physics Education
• Computational Linguistics
• +۳ مورد دیگر

Helge Kastrup is a Part-Time Lecturer at the Department of Computer Science and Department of Mathematical Sciences at the University of Copenhagen . His work bridges Natural Language Processing , Physics Education , and Human-Computer Interaction , with a focus on addressing student challenges in physics learning. Email: helge@di.ku.dk Email: hk@math.ku.dk In 2023, he co-authored the book Fear of Physics: And How to Help Students Overcome It , exploring educational strategies to reduce physics anxiety through pedagogical innovation. His research intersects disciplines like Cognitive Science , Psychology , and Computational Linguistics . Helge contributes to the Natural Language Processing section at DIKU, which focuses on statistical models for text processing, misinformation detection, and multi-modal language learning. He is affiliated with the SCIENCE AI Centre at the University of Copenhagen, engaging in interdisciplinary AI research.

• Image Analysis
• Computational Modelling
• Geometry
• +۳ مورد دیگر

Magnus Rahbek Dalgaard Hansen is an Instructor at the Department of Mathematical Sciences and a Visiting PhD Student at the Department of Computer Science, Algorithms and Complexity section, both at the University of Copenhagen. He works in interdisciplinary research areas including image analysis, computational modelling, geometry, and machine learning.

• Probabilistic Machine Learning
• Deep Probabilistic Programming
• Statistical Structural Bioinformatics
• +۵ مورد دیگر

Thomas Wim Hamelryck is a Professor at the Department of Computer Science (Programming Languages and Theory of Computation) and the Department of Biology (Computational and RNA Biology) at the University of Copenhagen . With a PhD in Protein Crystallography from the Free University of Brussels (VUB), he specializes in Bayesian modeling , probabilistic machine learning , and statistical structural bioinformatics , focusing on protein structure prediction, evolution, and non-Euclidean data representation.

• Machine Learning
• Artificial Intelligence
• Information Retrieval
• +۴ مورد دیگر

Adrián Avelino Sousa-Poza is a Researcher at the Department of Computer Science , University of Copenhagen, specializing in Machine Learning and its applications across diverse domains including Artificial Intelligence , Medical Data Analysis , and Quantum Computing . His research intersects with the SCIENCE AI Centre , focusing on both theoretical and applied aspects of machine learning. Recent work explores environmentally sustainable AI , quantum-enhanced models , and cross-cultural adaptation systems , reflecting his interdisciplinary approach. Key themes in his publications include large language models , quantum computing applications , and healthcare informatics , with a particular emphasis on interpretability , fairness , and hardware optimization in AI systems.

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +۳ مورد دیگر

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.

• Image Analysis
• Computational Geometry
• Computer Vision
• +۳ مورد دیگر

Tore Ziegler Hougaard Andersen serves as a Lecturer in the Department of Computer Science (DIKU) at the University of Copenhagen. He is affiliated with the Image Analysis, Computational Modelling, and Geometry research section, which focuses on interdisciplinary work spanning theoretical algorithms to real-world applications in science and industry. His research interests derive from the section's core themes, including medical/biological imaging, computer vision, robotics, and mathematical geometry. The section actively contributes to initiatives like the SCIENCE AI Centre and collaborates with facilities such as Max IV and the Danish Bioimaging Network. DIKU provides robust infrastructure for this work, including high-performance computing clusters and specialized labs. While specific advising or grant activities for Andersen are unreported in the source material, the department emphasizes cross-sector collaboration through networks like the Pioneer Centre for Artificial Intelligence and MADE (Manufacturing Academy of Denmark).

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Florestan Robin Valentin M Brunck is a Research Fellow at the Department of Computer Science (DIKU), University of Copenhagen, specializing in the Algorithms and Complexity group within the Pioneer AI section. His work intersects artificial intelligence, computational complexity, and geometric statistics. Research Focus: AI and machine learning applied to sliding block puzzles and combinatorial problems Computational complexity of algorithmic challenges Geometric modeling and discrete mathematics Contact: Email: flbr@di.ku.dk Phone: +45 3533 0744 Location: Universitetsparken 1, 2100 Copenhagen Ø

• Computer Science
• Mathematical Sciences
• Algorithms
• +۳ مورد دیگر

Jeppe Esbjerg is an Instructor affiliated with both the Department of Computer Science and Department of Mathematical Sciences at the University of Copenhagen. His contact details include two email addresses: jees@di.ku.dk and jes@math.ku.dk , with offices located at Universitetsparken 1 and 5, 2100 Copenhagen Ø.

• Machine Learning
• Artificial Intelligence
• Information Retrieval
• +۳ مورد دیگر

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.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

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

• Machine Learning
• Quantum Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

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

• Machine Learning
• Artificial Intelligence
• Information Retrieval
• +۴ مورد دیگر

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.

• Machine Learning
• Natural Language Processing
• Medical Data Analysis
• +۳ مورد دیگر

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.

Claus Thorn Ekstrømمشاهده پروفایل

استاد

• biostatistics
• statistics
• bioinformatics
• +۵ مورد دیگر

Claus Thorn Ekstrøm is a Professor at the University of Copenhagen , affiliated with the Faculty of Health and Medical Sciences and the Section of Biostatistics . His work integrates advanced statistical methods with biomedical and public health research. Contact details include ekstrom@sund.ku.dk and phone numbers +4535327597, +4535327629. The Section of Biostatistics is located at Øster Farimagsgade 5 opg. B, 1014 København K. Research Focus : Bioinformatics, statistical genetics, genetic epidemiology Teaching : Biostatistics, statistics, and bioinformatic methods Ekstrøm’s recent research trends emphasize causal inference , longitudinal data modeling , and integrated analysis of genetic and metabolic data . His publications span topics like biomarker development in psychiatric disorders , statistical frameworks for complex family data , and computational efficiency in Gaussian process regression , reflecting interdisciplinary applications in public health, genetics, and epidemiology.