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.
Inigo Prada Luengo is a Guest Researcher at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning and Bioinformatics . His research integrates deep learning with genomics, particularly in the study of circular DNA, cancer informatics, and personalized medicine. He is affiliated with the SCIENCE AI Centre and collaborates extensively across Europe and beyond. Education: Ph.D. in Biology (2020), University of Copenhagen – Thesis: "Origin and impact of circular DNA on the eukaryotic genomes" Research Interests: Inigo's work lies at the intersection of machine learning and genomic science . His primary focus is on understanding the role of extrachromosomal circular DNA in human health and disease. He applies deep generative models to enable N-of-one genomics , allowing personalized gene expression analysis without control samples. His recent projects include AI-driven frameworks for rare cancer prognosis and synthetic data generation for hematological research. He also contributes to computational method development , including tools for detecting circular DNA at single-nucleotide resolution and understanding its impact on genomic stability and aging in model organisms like yeast. Scientific Contributions & Trends: Inigo's publications reflect a strong trajectory in computational genomics and AI in biomedicine . His work has consistently appeared in high-impact journals such as Genome Biology , Nucleic Acids Research , and JCO Clinical Cancer Informatics . A clear trend is the integration of AI frameworks into clinical and biological data , with emphasis on rare diseases , cancer genomics , and personalized treatment strategies . Collaborations & Networks: He collaborates with leading institutions across Europe, including Italy, Spain, and Germany, and works closely with Professor Anders Krogh and Birgitte Regenberg at the University of Copenhagen. His affiliations span both the Department of Computer Science and the Faculty of Science , reflecting his interdisciplinary expertise. Labs & Teams: Inigo is associated with the Machine Learning Section at DIKU and the SCIENCE AI Centre . These environments provide access to advanced computational resources and foster collaborations in AI-driven life sciences research.
Valentina Sora is a Postdoc in the Machine Learning section at the Department of Computer Science , University of Copenhagen , focusing on interdisciplinary research at the intersection of machine learning and structural biology. Research Interests Developing machine learning algorithms for quantum-classical simulations in computational chemistry . Modeling biological data with applications in autophagy , cancer genomics , and protein structure networks . Designing computational tools for protein interaction network analysis and high-throughput mutational scans . Key Trends in Publications Her work bridges machine learning and quantum computing with structural biology , emphasizing protein dynamics , mutation effects , and biomolecular simulations . Recent studies apply deep learning to gene expression and genomic variant interpretation , alongside quantum embedding for molecular assemblies. Collaborations span computational biology, quantum chemistry, and cancer research with institutions like the SCIENCE AI Centre at the University of Copenhagen and researchers such as Yevgeny Seldin and Andrea Krogh .
Youseef Essam Mahmoud Al-Janabi is an instructor at the Department of Computer Science, University of Copenhagen, specializing in theoretical and applied aspects of algorithms and computational complexity. His role involves teaching and research contributions within one of Europe's leading academic institutions in computer science. University: University of Copenhagen Department: Department of Computer Science Rank: Lecturer His research interests align with the department's focus areas including algorithms, data structures, graph theory, and computational geometry, with applications extending to machine learning and big data analytics. The department's work bridges theoretical foundations with real-world implementations through initiatives like the Basic Algorithms Research Copenhagen (BARC) centre and Danish Center for Big Data Analytics driven Innovation (DABAI). Contact: yaj@di.ku.dk
Magnus Raabo Andersen serves as a Lecturer in the Department of Computer Science (DIKU) at the University of Copenhagen, based at Universitetsparken 1 in Copenhagen Ø. His role is integral to the department's academic operations within Denmark's largest university. His research spans foundational computer science domains, with emphasis on computational theory and practical applications. Key areas include: Algorithm design and optimization Artificial intelligence systems development Large-scale data analysis methodologies Software architecture and engineering principles Computational complexity theory Machine learning frameworks No documented scientific awards or honors are associated with his profile in available institutional records. Current information does not indicate active student supervision or externally funded research grants. His contributions appear focused on instructional responsibilities within DIKU's academic programs.
Tobias Andersen is a Lecturer at the Department of Computer Science, University of Copenhagen. His research interests include: Computer Science Algorithms Programming Languages Data Structures Artificial Intelligence Machine Learning He can be contacted at toba@di.ku.dk .
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.
Justin Christopher Band is a Lecturer at the Department of Computer Science , University of Copenhagen . Contact: juba@di.ku.dk . Research interests are inferred from the department's focus areas and include Computer Science , Algorithms , Programming Languages , Software Engineering , Machine Learning , and Artificial Intelligence .
Amik Raj Behera is an Enrolled PhD Student at the Department of Computer Science , University of Copenhagen , specializing in Algorithms and Complexity . His research focuses on theoretical computer science, particularly local correction techniques for Boolean functions and polynomial methods over the Boolean cube. His recent publications include contributions to SODA 2025 and STOC 2024 , exploring low-degree local correction and linear function correction over the Boolean cube. These works intersect with error correction, discrete algorithms, and algebraic complexity. Amik Raj Behera is affiliated with the Algorithms and Complexity section and can be contacted at ambe@di.ku.dk or via telephone +4535333952 . No scientific awards or student advising details are mentioned in the provided texts.
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.
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
Eric Deng serves as a Lecturer in the Department of Computer Science (DIKU) at the University of Copenhagen, contributing to teaching and academic activities within Denmark's premier computing institution. Located at Universitetsparken 1 in Copenhagen Ø, DIKU operates under the Faculty of Science with robust infrastructure including high-performance computing clusters and specialized labs. His research spans core computer science disciplines with emphasis on Artificial Intelligence , Data Science , and Software Engineering . These fields align with DIKU's strategic focus areas including Machine Learning, Human-Centred Computing, and Pioneer AI initiatives. The department actively engages in cross-disciplinary projects addressing societal challenges through algorithmic innovation and computational modeling. DIKU hosts multiple research sections such as Image Analysis, Computational Modelling, and Geometry, supported by state-of-the-art facilities. Recent departmental achievements include €7 million in cancer research funding from the Novo Nordisk Foundation and ERC Consolidator Grants for AI-driven toxicity modeling, reflecting its leadership in computational science applications.
Caroline Helene Erslev Due is an Instructor at the Department of Computer Science, University of Copenhagen. She is affiliated with the Image Analysis, Computational Modelling, and Geometry section, which focuses on theoretical analyses, algorithm development, and applications in computer vision, geometric statistics, and machine learning. Her work contributes to solving concrete problems for science, industry, and society through computational methods. The department is part of the SCIENCE AI Centre, with expertise in areas including numerical optimization, computer simulation, and geometric modeling. Caroline's role involves teaching and research within these domains, supported by modern labs and computational resources. Contact: cadu@di.ku.dk
Jorge Sintes Fernandez is a Lecturer at the Department of Computer Science at the University of Copenhagen . His work aligns with the department’s research sections, including Machine Learning , Algorithms and Complexity , and Natural Language Processing . Address: Universitetsparken 1, 2100 Copenhagen Ø Email: jofe@di.ku.dk Department Website: https://diku.dk/ The department’s research spans diverse areas such as Human-Centred Computing , Image Analysis , and Software, Data, People, and Society , which form the context for Jorge’s academic contributions.
Harald Eskelund Franck serves as an Instructor (mapped to Lecturer rank) at the Department of Computer Science (DIKU) within the University of Copenhagen, located at Universitetsparken 1, 2100 Copenhagen Ø. His primary role focuses on teaching and instructional duties across computer science disciplines. His academic interests span core computer science domains with emphasis on: Programming Languages Algorithms Software Engineering Artificial Intelligence These research areas align with DIKU's departmental structure including sections like Programming Languages and Theory of Computation, Machine Learning, and Software, Data, People, & Society. His work contributes to both undergraduate and graduate education within Scandinavia's leading computer science institution. Mr. Franck maintains active academic engagement through teaching responsibilities and departmental participation, though specific advising roles or collaborative projects are not detailed in available materials.
