جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Natural Language Processing
• Sustainable Artificial Intelligence
• +۳ مورد دیگر

Casper Dorph-Jensen serves as a Lecturer in the Department of Computer Science at the University of Copenhagen, where he is an active member of the Machine Learning section. His academic work bridges theoretical machine learning foundations with practical applications across diverse domains, contributing to both research and educational initiatives within Denmark's premier computing institution. His research spans multiple critical frontiers in artificial intelligence. Key interests include: Natural Language Processing with emphasis on emotion-aware dialogue systems and cross-cultural adaptation frameworks Sustainable AI development addressing environmental impacts of large models Quantum machine learning applications for biomolecular simulations Medical image analysis techniques for clinical diagnostics Fairness-aware information retrieval systems and recommender algorithms Analysis of his 2024-2025 publications reveals strong interdisciplinary trends combining machine learning with quantum physics, healthcare informatics, and sustainability science. Notable patterns include the application of large language models to clinical contexts (particularly nursing values evaluation), energy efficiency concerns in AI infrastructure, and novel quantum-classical hybrid approaches for scientific computing. His work frequently addresses real-world implementation challenges in noisy environments and resource-constrained settings. No scientific awards were documented in available sources. Information regarding student supervision or specific research grants remains unavailable in current public records, though his Machine Learning section affiliation suggests participation in broader departmental initiatives like the SCIENCE AI Centre and TreeSense remote sensing project. He operates within the Department of Computer Science's Machine Learning section, which maintains dedicated high-performance computing resources and participates in the university-wide SCIENCE AI Centre. This section focuses on both theoretical ML foundations and applications in medical imaging, biological data modeling, and sustainability-focused computing, with recent projects including quantum computing initiatives and environmental monitoring systems.

• Formal Methods in Software Engineering
• Programming Languages and Formal Logic
• Trustworthy Distributed Systems
• +۹ مورد دیگر

Dr. Boris Düdder is an Associate Professor at the Department of Computer Science (DIKU), University of Copenhagen, focusing on Formal Methods, Programming Languages, and Trustworthy Distributed Systems. He leads the Software Engineering & Formal Methods research group and serves as Deputy Head of Department for Teaching, advocating for neurodiversity and inclusion. Primary Affiliation: Department of Computer Science, University of Copenhagen External Lectureship: Copenhagen Business School (CBS), University of Electronic Science and Technology of China (UESTC) Honorary Research Fellowship: Institute for Intelligent Production Systems, Qingdao, China His research bridges formal computer science foundations with industrial applications, including automated program generation for adaptive systems (Combinatory Logic Synthesizer), verifiable AI, blockchain for healthcare IT, and secure data ecosystems. Key projects involve Smart Factories, Data Markets, and Ethical AI assessment via Z-Inspection. Scientific recognition includes the 2022 SSIP Excellence In Service Innovation Award for Z-Inspection. He has secured international grants in blockchain, FinTech, and secure data sharing, operating in multinational consortia. Contact: boris.d@di.ku.dk | Phone: +4593565748 | Website: Research Profile

• Computer Science
• Software Engineering
• Data Management
• +۱ مورد دیگر

Linas Einikis serves as an Instructor at the Department of Computer Science (DIKU), University of Copenhagen, located at Universitetsparken 1, 2100 Copenhagen Ø, Denmark. He is part of the department's teaching staff, contributing to undergraduate and graduate educational programs in computer science. His academic focus spans core computer science disciplines: Computer Science (broad theoretical and practical foundations) Software Engineering (development methodologies and systems) Data Management (storage, processing, and analysis techniques) Algorithms (design and computational complexity) These research areas align with DIKU's active sections including Software, Data, People & Society and Programming Languages and Theory of Computation, where interdisciplinary collaboration with industry is emphasized. Contact: lein@di.ku.dk

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۶ مورد دیگر

Hu Guo serves as a Lecturer in the Machine Learning section at the Department of Computer Science (DIKU), Faculty of Science, University of Copenhagen. Located at Universitetsparken 1, Copenhagen Ø, Hu Guo contributes to the department's research activities which span from theoretical foundations of machine learning to practical applications in diverse domains including medical data analysis, remote sensing, sustainability, and biological data modeling. The department participates in the SCIENCE AI Centre at the University of Copenhagen and maintains powerful compute resources for research. The research interests of Hu Guo encompass a broad spectrum of machine learning applications. Their work bridges theoretical machine learning with practical implementations across multiple disciplines. Notably, their research spans quantum computing applications in biomolecular simulations, natural language processing for emotion recognition and cross-cultural applications, medical AI for patient analysis and diagnostics, sustainable AI development with environmental considerations, and recommender systems with fairness evaluations. This interdisciplinary approach demonstrates strong connections between computer science theory and real-world problem solving in healthcare, environmental science, and cultural applications. Analysis of Hu Guo's recent publications reveals a strong focus on interdisciplinary applications of machine learning. Their work shows particular strength in quantum computing applications, medical AI, and sustainable computing practices. The publications demonstrate a pattern of addressing both theoretical challenges and practical implementations, with significant contributions to explainable AI, cross-cultural applications of language models, and energy-efficient computing solutions. The research consistently connects machine learning theory with real-world applications across healthcare, environmental science, and computational physics domains. Hu Guo actively contributes to the Machine Learning section's research activities, which include participation in the SCIENCE AI Centre and utilization of the department's powerful compute cluster. Their research aligns with the section's focus on both theoretical foundations and practical applications of machine learning across diverse domains including information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. The work contributes to initiatives like TreeSense - Centre for Remote Sensing and Deep Learning of Global Tree Resources.

• Machine Learning
• Quantum Computing
• Artificial Intelligence
• +۴ مورد دیگر

Thore Husfeldt is a Visiting Professor at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning , Quantum Computing , and related disciplines. He is affiliated with the SCIENCE AI Centre , focusing on theoretical and applied aspects of AI, including ethical considerations, medical informatics, and algorithm design. His research spans interdisciplinary domains, integrating Quantum Computing with Computational Biology and Medical Informatics . Recent work explores Environmentally Sustainable AI , Neural Network Architecture , and Cross-Cultural Adaptation . His contributions address challenges in AI Ethics , Healthcare Applications , and Quantum Simulation . The department’s Machine Learning Section and SCIENCE AI Centre provide resources such as a powerful compute cluster and modern labs. His role involves advancing AI research through collaborative projects and leveraging computational tools for societal and scientific impact.

• Machine Learning
• Computer Science
• Natural Language Processing
• +۳ مورد دیگر

Mikolaj Tymon Mazurczyk is a Research Fellow at the Department of Computer Science, University of Copenhagen. His research intersects with machine learning and computer science, focusing on areas such as natural language processing, medical image analysis, and biological data modeling. The Machine Learning Section at DIKU explores both theoretical and applied aspects, including information retrieval, medical data analysis, remote sensing, and sustainability. They participate in the SCIENCE AI Centre and collaborate with various research groups. Dr. Mazurczyk's recent publications highlight advancements in optical neural networks, sustainable AI, and quantum computing applications. His work also addresses fairness in recommender systems and interpretability in large language models, as well as clinical informatics and biomedical data analysis.

• Machine Learning
• Natural Language Processing
• Medical Data Analysis
• +۴ مورد دیگر

Rasmus Mikelsons serves as a Lecturer at the Department of Computer Science , University of Copenhagen . His email contact is rami@di.ku.dk . He is affiliated with the Machine Learning section and participates in the SCIENCE AI Centre, focusing on both theoretical and applied machine learning research. Research Interests Machine Learning Natural Language Processing Medical Data Analysis Remote Sensing Sustainability Computational Biology The Machine Learning section conducts research ranging from theoretical foundations to applications in domains such as information retrieval, medical imaging, and biological data modeling. Recent publications highlight trends in quantum machine learning, sustainable AI, and multimodal applications. Labs and Teams He is part of the Machine Learning section at the Department of Computer Science, which has access to a dedicated compute cluster and participates in initiatives like TreeSense, a center for remote sensing and deep learning of global tree resources.

• Algorithms and Complexity
• Human-Centred Computing
• Image Analysis
• +۱۱ مورد دیگر

Andreas Peder Agerskov Pedersen is an Instructor affiliated with both the Department of Computer Science (DIKU) and the Department of Economics at the University of Copenhagen . His work spans multiple research sections, including Algorithms and Complexity, Machine Learning, and Human-Centred Computing. Research Interests His research focuses on interdisciplinary areas within computer science and economics, particularly: Algorithm design and complexity analysis Human-computer interaction and user-centered systems Computational modeling for economic and social systems Machine learning applications in data-intensive domains Programming language theory and software engineering

• Machine Learning
• Quantum Computing
• Artificial Intelligence
• +۷ مورد دیگر

Jonatan Ruiz-Molsgaard is an Instructor at the Department of Computer Science , University of Copenhagen. His research spans interdisciplinary applications of machine learning in quantum computing , medical data analysis , and environmentally sustainable AI . Research trends include: Quantum-inspired algorithms for biomolecular modeling Interpretability techniques for large language models Fairness frameworks in recommender systems Neural network applications in healthcare and climate science

Stefan Horst Sommerمشاهده پروفایل

استاد

• Machine Learning
• Riemannian Geometry
• Stochastic Processes
• +۳ مورد دیگر

Stefan Horst Sommer is a Professor at the Department of Computer Science (DIKU), University of Copenhagen . He leads the Pioneer AI (P1AI) section, serves as Head of Studies for Machine Learning and Data Science, and co-founded the Center for Computational Evolutionary Morphometry (CCEM) with Rasmus Nielsen. His work bridges stochastic processes , geometric statistics , and machine learning with applications in computational anatomy and diffusion modeling . Key Roles : Head of Pioneer AI, Head of MLDS Studies, CCEM PI Labs : Applied Geometry Lab, CCEM Research focuses on Riemannian geometry , anisotropic diffusion , and stochastic shape analysis . Current projects include geometric machine learning for aerodynamic modeling and probabilistic image registration with applications in medical imaging and evolutionary biology . His 96+ publications emphasize manifold-valued processes and geometric deep learning . Collaborations span computational anatomy , stochastic mechanics , and AI-driven scientific computing . He co-organizes international workshops on geometric statistics and maintains active GitHub repositories for open-source research tools.

Birthe Dinesenمشاهده پروفایل

استاد

• Telehealth
• Telerehabilitation
• Welfare Technology
• +۷ مورد دیگر

Birthe Dinesen is a Professor at Aalborg University's Department of Medicine and Health Technology within the Faculty of Health Sciences. Her work centers on the Center for Sensory-Motor Interaction and ExerciseTech, with extensive contributions to telehealth implementation across cardiac, respiratory, and mental health domains. PhD in Health Technology (specific institution not stated) Extensive international clinical trial experience Focus on technology transfer from research to clinical practice Professor Dinesen's research spans telehealth , telerehabilitation , and welfare technology with particular emphasis on gamification and user-driven innovation . Her fingerprint analysis shows dominant work in Telerehabilitation (100%), Heart Failure management (46%), and Rehabilitation Engineering (45%). Current projects integrate machine learning for sleep apnea screening and AI for emergency department imaging analysis, reflecting her commitment to translating technology into practical healthcare solutions. Publication trends reveal consistent output since 2003 with significant acceleration after 2011. Her 211 publications demonstrate specialization in cardiac telerehabilitation (particularly atrial fibrillation and heart failure), COPD management, and stroke rehabilitation using digital tools. Recent work increasingly incorporates machine learning and social robotics. Best poster award 2023 (Klinisk Kvalitet) Professor Dinesen has secured 8 major research projects including the Velux Foundation-funded 'Brain-controlled exoskeletons for stroke rehabilitation' and the Heart Association's 'The Patient of the Future' atrial fibrillation study. She has supervised 3 PhD students and maintains 231 press/media appearances highlighting her role as a public health technology advocate. Her datasets on COPD telerehabilitation have been widely shared through Figshare. Current leadership includes the 'Future Patient: Telerehabilitation and Gamification' project (2019-2022) and active development of AI applications for emergency radiology. Her ExerciseTech lab focuses on sensor-based rehabilitation systems with international clinical validation studies.

Efthymios Altsitsiadisمشاهده پروفایل

دانشیار

• Sustainability
• Renewable Energy Transition
• Disaster Management
• +۷ مورد دیگر

Efthymios Altsitsiadis is an Associate Professor at the Department of Management, Society and Communication, Copenhagen Business School. His research focuses on sustainability, renewable energy transition, wildfire management, stakeholder engagement, and social network analysis. He has contributed to interdisciplinary projects such as TREEADS, addressing holistic fire management ecosystems and organizational structural factors in disaster response. His work intersects environmental science, public policy, and information systems innovation. Key areas include developing frameworks for social acceptance of renewable energy and analyzing drivers of wildfire evacuation stakeholder collaboration. Recent studies also explore virtual agents' trustworthiness in human-agent interactions. Altsitsiadis collaborates with institutions like ACCELIGENCE Ltd and Marin Energi Testsenter AS. His research spans both academic and applied domains, including pandemic-related food systems resilience and optimization algorithms in manufacturing. He has supervised 2 academic works and contributed to EU-funded deliverables and policy reports. Notable projects include the Thessaloniki Metro Study on public transport innovations and analyses of market trends in collaborative production. His diverse portfolio reflects expertise in bridging organizational behavior with environmental and technological challenges.

Alberto Lluch Lafuenteمشاهده پروفایل

استاد

• Model Checking
• Cybersecurity
• Formal Methods
• +۵ مورد دیگر

Alberto Lluch Lafuente is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). He leads research in formal methods, cybersecurity, and software systems engineering. His work emphasizes model checking, blockchain protocols, and security risk modeling. He has supervised multiple PhD students and serves as Editor-in-Chief of the Journal of Logical and Algebraic Methods in Programming . Research interests include formal verification, attack-defense trees, distributed systems, and quantitative analysis of adaptive systems. Recent efforts focus on process mining for security analysis, liquidity-saving protocols in DeFi, and human-centered AI in software engineering. Key Projects: IoT security risk modeling, formal analysis of DeFi protocols, reduction techniques for biological systems Tools Developed: ERODE (Boolean network reduction), QFLan (quantitative analysis tool) His interdisciplinary work bridges theoretical computer science with practical applications in finance, cybersecurity, and distributed systems.

Heine Anton Hansenمشاهده پروفایل

دانشیار

• Density Functional Theory (DFT)
• CO2 Reduction Reaction
• Electrocatalysts
• +۶ مورد دیگر

Heine Anton Hansen is an Associate Professor in the Department of Energy Conversion and Storage at Technical University of Denmark (DTU). His research focuses on atomic-scale materials modeling with emphasis on electrochemical processes, particularly in CO2 reduction, oxygen reduction reactions, and catalyst design. He contributes to UN Sustainable Development Goals related to affordable and clean energy (SDG7) and climate action (SDG13). His work employs advanced computational methods like Density Functional Theory (DFT) and molecular dynamics simulations to study electrode-electrolyte interfaces and catalytic mechanisms. Hansen collaborates internationally on topics including hydride materials discovery and machine learning-driven materials design. Supervises 5 current PhD projects on topics ranging from magnetic catalysis to hydrogen peroxide production Active in conferences presenting on CO2 activation mechanisms and electrochemical interface modeling Develops open-access datasets for CO2 reduction studies Key research themes include optimizing electrocatalysts for renewable energy systems and understanding cation effects in electrochemical reactions.

• X-Ray Emission Spectroscopy
• Spin State
• Absorption Spectroscopy
• +۵ مورد دیگر

Sophie Canton is a Research Fellow at the Department of Chemistry , Technical University of Denmark , specializing in advanced spectroscopic techniques and materials for energy applications. Her work bridges Chemistry , Physics , and Materials Science , focusing on Hot Carrier Dynamics , X-Ray Emission Spectroscopy , and Photocatalysis . Active in Spin State Analysis , Excited State Landscapes , and Quantum Dot Catalysts Supervisor in the Engineering and Mechanistic Investigation on Doped Semiconductor Quantum Dots project (2018–2021) Recent research explores Lead Halide Perovskites and Metalloporphyrin Systems , aiming to enhance Photovoltaic Efficiency and Carbon Dioxide Reduction . Collaborations span international teams, leveraging Ultrafast Spectroscopy to study Electron Transfer mechanisms.