جست‌وجوی استاد و پژوهشگر

Titus Theodorus Robroekمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Resource-aware Computing
• +۴ مورد دیگر

Titus Theodorus Robroek is a Postdoctoral Researcher at IT University of Copenhagen, working within the Data, Systems, and Robotics section. His research focuses on resource-aware data systems and data-intensive systems and applications, with affiliations to both the Resource-Aware Data Systems group and the Center for Climate IT. Dr. Robroek's research interests span multiple areas in machine learning systems, including: Resource-aware Machine Learning Deep Learning Training Optimization GPU Computing and Collocation ML Benchmarking Frameworks Data Management for Machine Learning Scientific Visualization for Training Systems His recent publications demonstrate a strong focus on optimizing machine learning workflows, particularly in resource-constrained environments. His work addresses critical challenges in data selection, pipeline orchestration, and efficient utilization of hardware resources for deep learning training. The research shows a progression from foundational work on data management to more comprehensive frameworks for resource-aware machine learning. Dr. Robroek has received research funding through two major projects: MOTH: Machine Learning on Tiny Hardware (Novo Nordisk Foundation, 2023-2026) RAD: Extremely Parallel and Incredibly Diverse Data Processing on Many Heterogeneous Cores (Independent Research Foundation of Denmark, 2021-2025) His PhD thesis "Resourceful Learning: Training More Models with Fewer Resources" (2024) represents a significant contribution to the field of efficient machine learning systems.

• Machine Learning
• Quantum Computing
• AI Ethics
• +۴ مورد دیگر

Giacomo Fregona is a Research Fellow in the Machine Learning section at the Department of Computer Science, University of Copenhagen. His work spans theoretical and applied domains, with affiliations to the SCIENCE AI Centre and TreeSense Centre for Remote Sensing and Deep Learning of Global Tree Resources. Research interests include Quantum Machine Learning for biomedical and molecular applications AI ethics, fairness, and sustainability Neural decoding of brain signals Generative models for cross-cultural and biological data Quantum computing hardware optimization Deep learning in medical diagnostics Article trends reflect expertise in quantum-classical hybrid systems, explainable AI, and multimodal learning, with recent publications on environmental sustainability in neural architecture search, emotion recognition, and federated learning for rare diseases. He is associated with the Machine Learning section's initiatives in computational biology, quantum informatics, and AI for climate science.

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

Lennard Hilgendorf is a Researcher at the Department of Computer Science , University of Copenhagen . His work focuses on Machine Learning with applications in quantum computing, medical imaging, natural language processing, and environmental sustainability. Research Trends: His recent publications highlight interdisciplinary work at the intersection of quantum mechanics and machine learning, efficient AI architectures for environmental sustainability, explainable models for medical diagnostics, and multimodal approaches to ecological monitoring. Key keywords include Machine Learning , Quantum Computing , Medical AI , and Environmental Science . Labs & Collaborations: Affiliated with the SCIENCE AI Centre and the TreeSense Centre , which specialize in foundational machine learning research and remote sensing for global tree resources, respectively.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +۵ مورد دیگر

Oliver Mortensen is a PhD Fellow (Research Fellow) at the Machine Learning Section , Department of Computer Science (DIKU) , University of Copenhagen , Denmark. He is affiliated with the university’s Faculty of Science and participates in the cross-faculty SCIENCE AI Centre , a strategic initiative to advance artificial intelligence research and applications. Research Interests Mortensen’s research lies at the intersection of machine learning , quantum computing , and neuro-symbolic AI . His work spans both theoretical foundations—such as entropic risk optimization in reinforcement learning and Riemannian generative models—and highly applied domains including medical AI, recommender-system fairness, and brain-computer interfaces. A recurring theme is trustworthy AI , where he investigates explainability, fairness, and sustainability across large language models and clinical decision-support systems. Scientific Contributions & Trends Across more than 60 peer-reviewed contributions (2024-2025), Mortensen demonstrates a clear trajectory toward hybrid quantum-classical algorithms , energy-efficient AI , and human-centric evaluation . His publications integrate rigorous theoretical guarantees with empirical validation on real-world data from electronic health records, satellite imagery, and conversational corpora. Collaborations & Resources He carries out his doctoral research under the supervision of Professor Yevgeny Seldin within DIKU’s vibrant Machine Learning Section. The group offers access to a dedicated high-performance compute cluster, the SCIENCE AI Centre ’s GPU/TPU pools, and interdisciplinary ties to life-science, geoscience, and humanities researchers across the university.

• Machine Learning
• Neural Architecture Search
• Energy Efficiency in AI
• +۲ مورد دیگر

Pedram Bakhtiarifard is an Instructor and PhD Fellow at the Department of Computer Science, University of Copenhagen, specializing in the Machine Learning section. His research interests include: Machine Learning Neural Architecture Search Energy Efficiency in AI Sustainable Artificial Intelligence Green Computing Pedram's research focuses on developing energy-efficient approaches to neural architecture search, addressing the growing concern about the environmental impact of AI model development. His work bridges theoretical machine learning with practical applications aimed at reducing the carbon footprint of AI systems through innovations like EC-NAS (Energy Consumption Aware Neural Architecture Search). His publications demonstrate a clear trend toward sustainable AI development, with research spanning from foundational benchmarking (2022 preprint) to conference presentations (ICASSP 2024) and forward-looking policy considerations (2025 work on climate awareness). These contributions position him at the intersection of technical machine learning research and environmental responsibility. Pedram actively contributes to teaching activities within the Department of Computer Science, supporting the department's educational mission in machine learning and related fields.

• 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.

• 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.

• Artificial Intelligence
• Human-Computer Interaction
• Medical Imaging
• +۵ مورد دیگر

Hubert Dariusz Zajac serves as a Research Fellow at the Department of Computer Science within the Faculty of Science , University of Copenhagen. His work focuses on Human-Centred Computing , particularly the intersection of artificial intelligence and healthcare systems. Email: hdz@di.ku.dk Location: Sigurdsgade 41, 2200 København N. Zajac's research interests span Artificial Intelligence , Human-Computer Interaction , and Medical Imaging . His recent publications examine sociotechnical challenges in clinical AI integration, dataset curation for medical imaging, and context-specific adaptations of AI systems across global healthcare environments. Key research trends include: Global health disparities in AI adoption Ethical considerations in medical AI Collaborative design methodologies Diagnostic workflow optimization Radiology practice standardization Clinician-AI interaction patterns Zajac's work contributes to understanding how AI systems can be effectively integrated into clinical environments while respecting human factors and contextual variability.

• 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.

Barbara Plankمشاهده پروفایل

استاد مهمان

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Barbara Plank is a Visiting Professor at the IT-University of Copenhagen, affiliated with the NLPnorth research group. She specializes in Natural Language Processing (NLP), focusing on multilingual language understanding, computational job market analysis, and annotation tools. Her work bridges machine learning techniques with practical applications in domains like skill extraction and cross-lingual systems. Affiliations: NLPnorth, IT University of Copenhagen Key Research Areas: Multitask learning, domain adaptation, entity linking Education: Not explicitly listed in text. Research Interests: Barbara’s research emphasizes advancing NLP through robust models for under-resourced languages and real-world applications. She explores techniques like nearest neighbor methods for occupational analysis, domain-aware relation classification, and easy-to-use annotation tools (e.g., EEVEE) to democratize NLP tasks. Her work often addresses challenges in multilingual systems and ethical AI deployment. Grants & Projects: MultiSkill : Multilingual information extraction for job market analysis (2020–2024) MultiVaLUe : Multilingual variety-aware language understanding (2020–2024) Pioneer Centre for Artificial Intelligence : Focus on pre-registered replication and ethical AI (2021–2034) Awards: Amazon Research Award (2019) Outstanding paper awards at EACL 2021 and ML Evaluation Standards Workshop 2022 Labs/Teams: Active in the NLPnorth group, collaborating on projects like the 18th Linguistic Annotation Workshop and SemEval tasks.

Ehsan Yousefzadeh-Asl-Miandoabمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Computer Systems
• Computer Architecture
• +۶ مورد دیگر

Ehsan Yousefzadeh-Asl-Miandoab is a Postdoctoral Researcher at the IT University of Copenhagen (ITU), Denmark, specializing in computer systems and deep learning hardware optimization. His research targets GPU efficiency challenges in large-scale AI training workloads. He earned his PhD from ITU investigating GPU underutilization in deep learning training through analytical models and system designs. His Master's research focused on microarchitectural GPU energy efficiency, proposing novel on-chip memory architectures to reduce power consumption without performance loss. Yousefzadeh-Asl-Miandoab's core research spans deep learning systems, computer architecture, and sustainable AI. He develops resource-aware approaches for GPU resource management, collocation strategies, and monitoring frameworks to enhance hardware sustainability in AI infrastructure. His 2023-2025 publications reveal a clear trajectory toward optimizing deep learning training efficiency through GPU memory management, collocation analysis, and benchmarking methodologies. These works collectively advance sustainable AI by addressing critical hardware utilization bottlenecks in modern data centers. As Co-Investigator on the RAD (Resource-Aware Data Science) project funded by the Independent Research Fund Denmark (2021-2025), he develops resource-efficient data science frameworks. His collaborative work includes a 2023 visiting researcher position at the University of Basel.

• Natural Language Processing
• Machine Learning
• Dialogue Systems
• +۳ مورد دیگر

Ana Valeria Gonzalez is a researcher in the Department of Computer Science at the University of Copenhagen's Faculty of Science, specializing in Machine Learning with a focus on human-centered natural language processing. Her work bridges computational linguistics and cognitive science within the department's Machine Learning research section. Her research investigates bias mitigation in multilingual systems, interpretable AI evaluation frameworks, and affect-aware dialogue generation. Key contributions include developing testbeds for gender bias analysis in coreference resolution, novel methodologies for evaluating model interpretability through reverse Turing tests, and retrieval-based approaches for goal-oriented conversational agents. She employs techniques spanning attention mechanisms, BiLSTMs, and domain adaptation to address challenges in low-resource NLP settings. Analysis of her 2019-2021 publications reveals a cohesive research trajectory toward building transparent, equitable NLP systems that account for human cognitive factors. Her work consistently addresses real-world deployment challenges, particularly in dialogue systems requiring emotional intelligence and cross-lingual fairness. Collaborations with Anders Søgaard, Isabelle Augenstein, and other Copenhagen researchers demonstrate strong integration within the university's AI ecosystem. Gonzalez completed her Ph.D. in 2021 with the dissertation "Towards Human-Centered Natural Language Processing". She maintains active research in the Machine Learning group, though specific advising roles and grant details aren't documented in the available materials. Her publication record shows significant impact with multiple Scopus-cited works and substantial reader engagement across platforms.

• Ontology
• Formal Ontology
• Basic Formal Ontology (BFO)
• +۷ مورد دیگر

Barry Smith is SUNY Distinguished Professor of Philosophy at the University at Buffalo, with joint appointments in the Departments of Biomedical Informatics, Neurology, and Computer Science and Engineering. He serves as Director of the National Center for Ontological Research and is also a Visiting Professor at the Università della Svizzera italiana (USI) in Lugano. Professor Smith is a pioneering contributor to both theoretical and applied research in ontology. His work has been instrumental in establishing Basic Formal Ontology (BFO), which has become the most commonly adopted top-level ontology development framework and was approved as an ISO standard (ISO/IEC:21838-2). His research has also led to the formation of the Open Biomedical Ontologies (OBO) Foundry, a suite of interoperable ontology modules designed to support information-driven research in biology and biomedicine. His methodology is now being applied across diverse domains including military intelligence and industrial engineering. Smith's research interests span formal ontology, biomedical informatics, philosophy of language, cognitive science, and artificial intelligence. His work focuses on developing rigorous ontological frameworks for representing reality across various domains, with particular emphasis on applications in healthcare, biology, and information systems. He has made significant contributions to understanding the nature of substances, accidents, and universals, as well as developing theories of granular partitions and mereotopology. His recent publications demonstrate a trend toward applying ontological frameworks to pressing real-world problems including pandemic response, artificial intelligence limitations, and spatial reasoning. Wolfgang Paul Award of the Alexander von Humboldt Foundation (2002) Paolo Bozzi Prize in Ontology (2011) Fellow of the American College of Medical Informatics (FACMI) (2013) As Director of the National Center for Ontological Research, Smith leads a multidisciplinary team focused on advancing ontological research and its applications across multiple domains. His work with Hernando de Soto on property and business rights among the poor in developing countries demonstrates the practical impact of his theoretical work. His research has been funded by major organizations including the National Institutes of Health, National Science Foundations of the US, Switzerland and Austria, the Volkswagen Foundation, the European Union, and the US Department of Defense.

Anders Stockmarrمشاهده پروفایل

دانشیار

• Chronic Disease Analysis
• Machine Learning
• Environmental Modeling
• +۳ مورد دیگر

Anders Stockmarr is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), specializing in Statistics and Data Analysis. His research focuses on applying advanced computational methods to solve complex problems in public health, environmental science, and epidemiology. Key areas include chronic disease progression modeling, multimorbidity analysis, and machine learning applications in seagrass monitoring and healthcare systems optimization. He has supervised numerous PhD students and led projects addressing topics such as patient-centered care models for multimorbid patients and low-carbon dietary optimization. Stockmarr’s academic contributions span over 150 publications, with recent work emphasizing register-based studies on disease trajectories and sustainable food systems. He collaborates extensively with institutions on interdisciplinary projects, including seagrass coverage estimation using machine learning and nationwide analyses of chronic heart disease dynamics. His activities include peer reviewing, academic supervision, and public engagement through media contributions on topics like traffic accident risk and human longevity records. Notable research highlights include developing the amVAE model for age-aware multimorbidity clustering and pioneering seagrass detection techniques from underwater videos. His work bridges statistical methodology with real-world applications, addressing global challenges such as climate change mitigation and healthcare system efficiency.

Peter Dologمشاهده پروفایل

دانشیار

• Recommender Systems
• Web Engineering
• Machine Learning
• +۵ مورد دیگر

Peter Dolog is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design. His research focuses on Recommender Systems, Web Engineering, Machine Learning, and Health Informatics. He leads projects such as the MEco Medical Ecosystem and the IWIS Intelligent Web and Information Systems initiative. His work emphasizes explainable AI, knowledge graphs, and social media analytics for public health monitoring. Notable contributions include hypergraph-based recommendation models and sensitive information detection in legal documents. Projects include MEco (medical event surveillance) and IWIS (web systems personalization), funded by EU and national grants. His research spans 148 publications, with recent work on explainable recommendation systems and neural networks for medical data analysis. Collaborations include the University of Vienna and international conferences like ECIR and RecSys. Media coverage highlights his work on using social media for epidemic detection. Key research areas include recommendation systems with attention mechanisms, knowledge graph integration, and ethical AI applications in healthcare. His methodologies bridge machine learning with user-centric design, emphasizing transparency and explainability in AI-driven systems.