جست‌وجوی استاد و پژوهشگر

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

Freja Stær Hincheli serves as a Lecturer at the Department of Computer Science , University of Copenhagen. Her work intersects multiple domains within machine learning, with a particular emphasis on quantum-inspired algorithms, medical imaging, and sustainable AI development. Keywords : Machine Learning, Quantum Computing, Medical Imaging, Natural Language Processing, Computational Biology Key Collaborations : SCIENCE AI Centre Her research spans quantum-enhanced neural networks, explainable AI for medical diagnostics, and energy-aware model design. Recent publications highlight applications in cross-cultural recipe adaptation, emotion-aware dialogue systems, and climate-conscious AI strategies. The Machine Learning Section at DIKU focuses on theoretical foundations and applications including medical image analysis , biological data modeling , and quantum computing , aligning with her contributions.

Sven Tarpمشاهده پروفایل

استاد

• Lexicography
• Artificial Intelligence
• Spanish Language
• +۳ مورد دیگر

Professor Sven Tarp is affiliated with Aarhus University as a faculty member at the School of Communication and Culture , specializing in Spanish Business Communication . His research focuses on lexicography, artificial intelligence, and digital tools for language learning. Education: Not explicitly detailed in the scraped text. Research Interests: Sven Tarp explores the intersection of lexicography and generative AI , with a focus on creating context-aware dictionaries and writing assistants for foreign-language learners. His work emphasizes human-AI collaboration and user-centered design in digital lexicography. Scientific Trends: His recent publications highlight the integration of AI technologies into lexicographic practices , particularly for Spanish and Chinese learners of English . Themes include grammar development , digital dictionaries , and pedagogical tools that combine machine learning with human expertise. Projects: He contributes to initiatives like the LACUA (Latin American Center Aarhus University) and leads research on disruptive technologies in lexicography , spanning 2018–2023.

Chenjuan Guoمشاهده پروفایل

دانشیار

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +۴ مورد دیگر

Chenjuan Guo is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. She is affiliated with the Data Engineering, Science and Systems group and the AI for the People initiative, and is part of the Daisy - Center for Data-intensive Systems. Her research focuses on machine learning, data engineering, spatio-temporal data analysis, and time series forecasting. Key projects include the Villum Foundation-funded 'Explainable AI for Complex Microbial Community Interactions and Predictions' (2021-2024) and the Astra project on time series analytics in spatial networks (2018-2021). Her research interests span representation learning, autoencoders, path representation, outlier detection, trajectory data analysis, and time series modeling. She has supervised 3 PhD students and contributed to over 60 publications, with a recent emphasis on transformer-based forecasting, neural architecture search, and continuous learning frameworks for spatio-temporal data. Her work bridges theoretical advancements with practical applications in environmental science, cloud computing, and urban mobility systems. Key achievements include developing frameworks like AutoCTS++ for automated time series forecasting and LightGTS for lightweight models. She actively collaborates internationally, contributing to conferences like ECML PKDD and CVPR. Her research is supported by grants from the Villum Foundation and other institutions.

Klaus Ingemann Pedersenمشاهده پروفایل

استاد

• Wireless Communication Networks
• Dependable Wireless Communications
• 5G-Advanced Technology
• +۱۰ مورد دیگر

Klaus Ingemann Pedersen is a Professor in the Department of Electronic Systems at the Technical Faculty of IT and Design, Aalborg University, Denmark. His research focuses on wireless communication networks and dependable wireless communications with substantial contributions to 5G-Advanced and 6G technologies. His research interests include: Wireless Communication Networks Dependable Wireless Communications 5G-Advanced Technology 6G Networks Radio System-Level Simulations Ultra-Reliable Low Latency Communication Heterogeneous Networks Professor Pedersen's recent work demonstrates cutting-edge research in next-generation wireless systems, with particular emphasis on deep reinforcement learning applications, link adaptation techniques, and system-level simulations. His publications show consistent high-impact output with 152 total publications spanning from 2012 to 2025, reflecting ongoing productivity and relevance in the rapidly evolving field of wireless communications. His notable achievements include: 5G-prisen (The 5G Award) received on October 23, 2019, shared with E. Mogensen and I.R. Larrad Professor Pedersen has supervised 8 PhD students according to institutional records. His research has practical applications in wireless communication, as evidenced by his involvement in patented technology 'Method and apparatus for wireless communication' which delivers social impact and quality of life improvements. His work shows strong international collaboration across multiple countries as indicated by the research network visualization.

• Statistical modeling
• Machine learning
• Artificial intelligence
• +۵ مورد دیگر

Line Katrine Harder Clemmensen is a Professor at the Department of Mathematical Sciences, University of Copenhagen. She specializes in statistical modeling, machine learning, and AI, with emphasis on low resource domains, explainability, and fairness in health/life science applications. She co-founded Interhuman AI as Chief Scientific Officer and maintains an active research program across multiple disciplines. Statistical Modeling Machine Learning Explainable AI Fairness in AI Health/Life Science Applications Her recent publications (2024-2025) span computational biology, neuroscience, environmental science, and emotion recognition. Notable collaborations include interdisciplinary work in pediatric OCD analysis, fungal microbiome prediction, and facial emotion recognition systems. She actively explores fairness and scalability in AI models. Dr. Clemmensen holds 60 publications with significant impact across computational biology (40+ citations), neuroscience (68+ readers), and machine learning (20+ Scopus citations). She has been referenced in news outlets, blogged, and discussed across multiple social platforms.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +۳ مورد دیگر

Ingemar Johansson Cox serves as a Professor within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His research bridges theoretical machine learning foundations with practical applications across medical data analysis, information retrieval, remote sensing, and sustainability initiatives. His research portfolio emphasizes machine learning applications in high-impact domains, particularly medical data analysis (e.g., early detection of gynecological malignancy using online search activity) and sustainability (e.g., reducing AI's carbon footprint). The Machine Learning section actively contributes to the university's SCIENCE AI Centre, focusing on both algorithmic innovation and real-world problem-solving in biological modeling and environmental monitoring. Recent publication trends reveal expanding work in quantum computing applications for biomolecular modeling, sustainable AI frameworks, and cross-cultural NLP systems. His 2024-2025 output demonstrates strong interdisciplinary collaboration, especially in medical informatics and climate-related AI research. Professor Cox operates within the Department of Computer Science's robust research ecosystem, which includes dedicated compute clusters and specialized initiatives like TreeSense for global tree resource monitoring through remote sensing and deep learning. The department's infrastructure supports large-scale machine learning projects requiring significant computational resources.

Leon Derczynskiمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Misinformation Detection
• +۳ مورد دیگر

Leon Derczynski is an Associate Professor of Computer Science at the IT University of Copenhagen , with a dual role as Principal Research Scientist/LLMSEC at NVIDIA . He leads the Strømberg NLP research group and coordinates NLP South at ITU, while also being affiliated with the Machine Learning group. Specializes in Natural Language Processing , Machine Learning , and LLM security Focus on Misinformation detection , Clinical text mining , and Danish language technology Research grants include Verif-AI (2.9M DKK), ClinRead (544K DKK), and LITHME (EU COST action, €11K). He has coordinated major projects like COMRADES and PHEME , and contributed to uComp and TrendMiner . Scientific recognition includes the University of Sheffield Exceptional Contribution Award (twice), WEBIST Best Student Paper award , and FP7 funding . His technical work includes the garak.ai LLM vulnerability scanner and generalised-brown clustering library. Actively supervises students and maintains numerous GitHub repositories (86 public projects) related to NLP, machine learning, and computational linguistics. He has delivered keynotes and guest lectures , including at Innopolis University (Russia) and PET (Danish Security and Intelligence Service).

• Science and Technology Studies
• Digital Methods
• Generative AI for Social Research
• +۵ مورد دیگر

Anders Kristian Munk is a Professor at DTU Management, Technical University of Denmark, based in the Department of Technology, Management and Economics within the Division for Technology and Business Studies. His research focuses on the critical intersection of technology and society through Science and Technology Studies (STS) and advanced digital methodologies. His primary research domains include: Generative AI integration in social research Digital methods development (notably data sprints) Controversy mapping of sociotechnical issues Computational ethnography and network analysis Critical examination of data imaginaries Urban studies through computational visual methods Recent publications (2023-2025) demonstrate a strategic pivot toward generative AI applications, with significant contributions in developing AI-native research frameworks like synthetic interlocutors and irreductionist mapping. His work uniquely bridges STS theory with practical tool development, exemplified by Gephisto for critical network analysis. Key application areas span public health (e.g., inflammatory bowel disease trend analysis in Africa), cultural industries (film audience analytics), and urban studies (pandemic place attachments). Prof. Munk co-developed the data sprint methodology for collaborative controversy mapping and has contributed to major projects including Climaps and EMAPS. His interdisciplinary approach combines social science rigor with computational innovation through partnerships across computer science, design, and domain-specific fields. Current work emphasizes ethical AI integration and methodological adaptation for complex sociotechnical challenges.

• Translation Studies
• Cognitive Translation Processes
• Dubbing Translation
• +۵ مورد دیگر

Kristian Tangsgaard Hvelplund is an Associate Professor with the Department of English, Germanic and Romance Studies at the University of Copenhagen's Faculty of Humanities. His academic work centers on translation studies, with a particular emphasis on cognitive processes in translation and dubbing translation. Translation cognition and automaticity Eye-tracking and keystroke logging methodologies Media translation and dubbing Translation process modeling Hvelplund's research combines theoretical and applied approaches, utilizing experimental methods to analyze translation quality and cognitive resource allocation. Recent publications explore emotional dimensions in translation, post-editing workflows, and methodological innovations in cognitive translation studies. His work demonstrates strong interdisciplinary connections across Translation Studies , Cognitive Science , Applied Linguistics , and Human-Computer Interaction . Methodologically, he focuses on Eye-tracking , Process modeling , and Quantitative translation analysis . As a dedicated educator, Hvelplund supervises BA, MA and PhD students in translation studies while maintaining active participation in international academic collaborations.

Peter Mayerمشاهده پروفایل

استادیار

• Cybersecurity
• User-centred Security
• User Authentication
• +۹ مورد دیگر

Peter Mayer is an Assistant Professor at the University of Southern Denmark's Department of Mathematics and Computer Science. His research focuses on end-user viable security and privacy solutions, emphasizing usability for diverse audiences, including laypersons, administrators, and developers. He coordinates the Human and Societal Factors research group within the Helmholtz Association's Engineering Secure Systems initiative at Karlsruhe Institute of Technology (KIT), where he is a KASTEL Fellow. Research Areas: User-Centred Security Email and Password Security Security Awareness and Training Cybersecurity in SMEs Phishing Detection and Prevention Key Projects: Cybersecurity and Business Continuity in Danish SMEs (Industriensfond-funded) INSPECTION: Website Hacks in Fake Shops Mapping the Cybersecurity Landscape in Danish SMEs His work bridges technical security mechanisms with human-centric design, addressing challenges like phishing mitigation, password manager adoption, and cross-cultural privacy awareness. He collaborates internationally and publishes extensively in top venues like CHI and CCS.

Yan Zhaoمشاهده پروفایل

دانشیار

• Anomaly Detection
• Autoencoder
• Attention (Machine Learning)
• +۵ مورد دیگر

Yan Zhao is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, science, and systems, with a particular emphasis on anomaly detection, machine learning, and spatio-temporal data analysis. He holds a Ph.D. in Computer Science (specific education details not explicitly provided). Research Interests: Dr. Zhao's work spans anomaly detection, autoencoders, attention mechanisms, preference learning, multivariate time series, and computational efficiency. His research often integrates machine learning with real-world applications in spatial crowdsourcing, trajectory analysis, and data privacy. Publications: With 72+ publications, his recent work emphasizes spatio-temporal prediction frameworks, federated learning, and efficient time series analysis. Notable contributions include frameworks for continuous learning on streaming data and privacy-preserving clustering in spatial crowdsourcing. Grants & Supervision: He has supervised one Ph.D. student and actively contributes to research grants focusing on data engineering and smart systems. His work bridges theoretical advancements with practical applications in transportation, social networks, and IoT.

Anders Møllerمشاهده پروفایل

استاد

• Programming Languages
• Software Engineering
• Static Program Analysis
• +۴ مورد دیگر

Anders Møller is a Professor and Vice Head of Department at the Department of Computer Science, Aarhus University, Denmark. He is a leading researcher in programming languages and software engineering, with a primary focus on static and dynamic program analysis. He serves as Chairman of the PhD Committee and holds leadership roles in the international research community, including Vice-Chair of ACM SIGPLAN and Associate Editor for ACM TOPLAS and ACM TOSEM. His research interests include programming languages, software engineering, static and dynamic analysis, program verification, and security. His work bridges theoretical foundations and practical applications, particularly in improving software reliability and security through advanced analysis techniques. The trends in his recent publications reflect a strong emphasis on static analysis for security, scalability, and real-world impact—especially in web applications, smart contracts, and open-source software supply chains. His research has evolved toward practical deployment, demonstrated by the founding and acquisition of Coana by Socket in 2025 for enhanced vulnerability detection. Recipient of the Danish Elite Research Prize 2020 ACM Distinguished Member He actively mentors students and contributes to the academic community through conference leadership (e.g., OOPSLA, PLDI, ICSE). He also co-authored the widely used textbook Static Program Analysis with Michael I. Schwartzbach. His work is deeply integrated into both academic and industrial advancements in software analysis and security.

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

William Henrich Due serves as a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Machine Learning section. His work intersects with the SCIENCE AI Centre and leverages the department's high-performance compute cluster for research in quantum computing, sustainable AI, and medical applications. Research focuses span quantum machine learning (biomolecular simulations, photonic processors), sustainable AI systems (energy efficiency, climate impact), and clinical applications (EEG analysis, medical imaging). His recent publications reveal strong activity in quantum-classical hybrid systems, with 8/15 recent papers addressing quantum computing challenges. The work emphasizes practical implementations in medical imaging and resource-constrained environments. His research aligns with DIKU's Machine Learning section priorities including medical imaging biomarkers and sustainable computing. Key infrastructure includes TreeSense for remote sensing and the department's dedicated compute cluster. No scientific awards were explicitly documented in the provided materials. Due contributes to DIKU's teaching mission as a Lecturer while engaging with the SCIENCE AI Centre's interdisciplinary initiatives. His work connects with medical imaging applications and quantum computing infrastructure development. Active in the Machine Learning section's research ecosystem, his work intersects with medical imaging analysis and quantum computing applications, utilizing specialized resources like TreeSense for environmental monitoring.

Michele Albanoمشاهده پروفایل

دانشیار

• Internet of Things (IoT)
• Energy Engineering
• Quality of Service (QoS)
• +۵ مورد دیگر

Affiliations & Roles Michele Albano is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design, focusing on research in IoT, Cyber-Physical Systems, and Edge Computing. He leads the Productive4.0 project (2017–2020), funded by Horizon Europe, addressing Industry 4.0 challenges in product lifecycle management. His work integrates formal verification tools like Uppaal with real-world applications in robotics, energy systems, and blockchain-based platforms. Research Interests Albano's research spans IoT architecture optimization , energy-efficient systems , and model-driven engineering . He develops tools for autonomous exploration algorithms (MAES), edge-cloud resource orchestration, and fault-tolerant computation offloading. His work bridges theoretical models (e.g., Uppaal SMC) with practical implementations in smart grids and robotic systems. Recent projects include blockchain-based crowdsourcing for machine learning and energy-aware thermal dynamics estimation in buildings. Collaborations & Impact He collaborates with the European Industry 6.0 community, contributing to the Arrowhead Framework for interoperable IoT systems. His research outputs include 112 publications, with 2025 highlights in human-inspired robotics and cognitive cloud frameworks. Media coverage in 2024–2025 highlights his work on green IT and secure API generation. Albano advises students on system modeling (e.g., ACSmt plugin development) and edge computing optimization. Labs & Teams His research group focuses on Cyber-Physical Systems and Smart Grids , with contributions to tools like RoutesMobilityModel and FlexHousing. He actively participates in workshops on New Trends in Software Architecture (SATrends '24) and IEEE conferences on Industrial Informatics.

Srinivasa Raghavendra Bhuvan Gummidiمشاهده پروفایل

استادیار

• Algorithms for Geographical Information Systems
• Database Systems
• Optimization Algorithms
• +۷ مورد دیگر

Srinivasa Raghavendra Bhuvan Gummidi is an Assistant Professor at the Department of Green Technology (IGT) within the University of Southern Denmark . His research focuses on Circular Economy , Building Stock Modeling , and Environmental Impact Assessment through advanced Geographical Information Systems (GIS) and Deep Learning techniques. Recent work includes high-precision building material identification and spatiotemporal tracking of urban material stocks. Research Keywords : Algorithms for Geographical Information Systems, Optimization Algorithms, Building Stock Modeling, Circular Economy, Deep Learning, Environmental Impact Assessment His peer-reviewed publications (2025–2015) span topics from urban material sustainability to spatial crowdsourcing systems . He teaches Geographic Information Systems for Engineering Sustainability (2024) and collaborates internationally on urban development and resource management projects.