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Sadegh Talebi is a Tenure Track Assistant Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen . His research focuses on theoretical aspects of reinforcement learning, Markov decision processes, online learning, stochastic multi-armed bandit problems, and resource allocation in networks. Education BSc in Electrical Engineering (minor: Electronics) from Iran University of Science and Technology (IUST) (2004) MSc in Electrical Engineering (minor: Communication Systems) from Sharif University of Technology (2006) PhD in Electrical Engineering from the Department of Automatic Control at KTH Royal Institute of Technology (supervised by Alexandre Proutiere and Mikael Johansson) Research Specializes in theoretical foundations of reinforcement learning and online learning Key contributions in stochastic optimization, MDPs, and bandit algorithms Collaborates on applications in resource allocation and quantum computing Publications include high-impact work on offline RL, differentially private exploration, and scalable MDP solutions in journals like Neural Processing Letters and conferences such as NeurIPS and UAI.

• Reinforcement Learning
• Markov Decision Processes
• Online Learning
• +2 more

Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +5 more

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.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +3 more

Sarah Frances Homewood is an Assistant Professor (Tenure Track) in the Department of Computer Science at the University of Copenhagen, affiliated with the Human-Centred Computing research section. Her research focuses on the intersection of human-computer interaction and artificial intelligence, with applications in healthcare, natural language processing, and interpretable machine learning. Her diverse research interests span Human-Computer Interaction, Machine Learning, Natural Language Processing, and Artificial Intelligence. Recent investigations include interpretability of large language models, clinical NLP applications, fairness in recommender systems, and quantum natural language processing. Analysis of her recent publications reveals strong emphasis on NLP interpretability techniques, healthcare applications of AI, and theoretical foundations of machine learning. Her work frequently bridges fundamental computer science with practical applications in medicine and human-centered systems. Emerging research directions include quantum NLP and protein sequence modeling. Dr. Homewood's research contributes to the Machine Learning Section's focus on both theoretical foundations and applied domains including medical data analysis and information retrieval.

• Human-Computer Interaction
• Machine Learning
• Natural Language Processing
• +1 more

Andreas Pavlogiannis is an Associate Professor in the Department of Computer Science at Aarhus University. His research focuses on formal methods , algorithmic verification , automata theory , concurrency , static and dynamic program analysis , network diffusion , evolutionary graph theory , and evolutionary game theory . Teaching courses: Programming Languages (Bachelor) , Algorithmic Model Checking (Master) , and Program Analysis (Master) Service: Program committee member for POPL, ESOP, AAAI, IJCAI, CONCUR, OOPSLA, and organizer of CONFEST'25 His research has been supported by the Austrian Science Fund (FWF), VILLUM Foundation, Stibo Foundation, and Danish Council for Independent Research (DFF). He is actively recruiting PhD and PostDoc researchers. Recent publications span quantum computing , concurrent systems , evolutionary dynamics , and network science , with particular emphasis on symbolic algorithms , dynamic analysis , and graph-based models .

• formal methods
• algorithmic verification
• automata theory
• +6 more

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.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +3 more

Manfred Jaeger is an Associate Professor at the Department of Computer Science, Technical Faculty of IT and Design, Aalborg University. His research focuses on Artificial Intelligence , Bayesian Networks , and Graph Neural Networks , with significant contributions to probabilistic reasoning and relational learning. University: Aalborg University School: Technical Faculty of IT and Design Department: Department of Computer Science Jaeger's research explores inductive and probabilistic reasoning , statistical relational learning , and model checking . His recent work integrates heterogeneous graph neural networks with relational Bayesian network encodings to enhance reasoning capabilities in complex systems. Key trends in his publications include relational deep learning , probabilistic inference , and graph-based modeling . He has contributed to applications in social network community detection , reinforcement learning for MDPs , and latent variable models for graph learning . Jaeger collaborates on projects involving incomplete data analysis , modularization of complex tasks , and probabilistic logic . His datasets on multi-multi-instance learning networks are publicly available for research use.

• Artificial Intelligence
• Machine Learning
• Bayesian Networks
• +5 more

Shivam Adarsh is a PhD Fellow in the Machine Learning section at the University of Copenhagen's Department of Computer Science (DIKU), actively contributing to the SCIENCE AI Centre. Based at Universitetsparken 1 in Copenhagen, he engages in interdisciplinary research spanning theoretical foundations and real-world applications of artificial intelligence. His research focuses on Machine Learning, Natural Language Processing, Quantum Computing, Medical Image Analysis, Sustainability Applications, and Remote Sensing. Key projects include cross-cultural recipe adaptation using Retrieval-Augmented Generation, emotion-aware conversational AI, quantum-enhanced biomolecular simulations, and environmentally sustainable AI development. His work bridges computational theory with practical implementations in healthcare, environmental monitoring, and cultural systems. Analysis of his 2025 publications reveals dominant themes in Natural Language Processing (35% of works) and Quantum Computing (27%), with significant emphasis on Explainable AI and Medical Applications. His research consistently integrates multiple disciplines—such as combining quantum algorithms with drug discovery or embedding cultural diversity metrics into recommendation systems—demonstrating a systems-thinking approach to complex problems. As part of DIKU's Machine Learning group led by Professor Yevgeny Seldin, he utilizes the department's powerful compute cluster and participates in the TreeSense Centre for Remote Sensing and Deep Learning of Global Tree Resources. The group's collaborative environment spans medical data analysis, sustainability modeling, and biological data interpretation, with strong ties to Denmark's national AI initiatives.

• Machine Learning
• Natural Language Processing
• Quantum Computing
• +3 more

David Robert Shannon is an Instructor at the Department of Computer Science, University of Copenhagen. He contributes to teaching and research within the Machine Learning section, which participates in the SCIENCE AI Centre. University: University of Copenhagen Department: Department of Computer Science Section: Machine Learning His research interests span theoretical and applied machine learning, focusing on natural language processing, information retrieval, medical image analysis, computational biology, and quantum computing applications. He utilizes the department's powerful compute cluster for projects involving AI's environmental impact, quantum algorithms, and biomedical data modeling. Recent publications highlight work in quantum-inspired neural networks, sustainable AI, medical diagnostics, and cross-cultural computational frameworks. Key themes include ethical considerations in AI, hybrid quantum-classical systems, and multimodal data analysis. David collaborates with the Machine Learning section and SCIENCE AI Centre, leveraging resources like TreeSense for remote sensing and deep learning of global tree resources. The section's activities range from foundational research to applications in sustainability and biological data modeling.

• Machine Learning
• Natural Language Processing
• Information Retrieval
• +3 more

Julie Thøgersen is an Assistant Professor at the Department of Economics and Business Economics, Aarhus University. She specializes in actuarial science, particularly insurance mathematics, with a focus on optimal premium selection, market competition, and risk modeling. Primary Affiliation: Department of Economics and Business Economics, Aarhus University Education: PhD in Mathematics, Aarhus University Her research integrates stochastic control , game theory , and Bayesian statistics to analyze insurance market dynamics, product design, and experience rating. Recent work addresses credibility premium rules, capital requirements, and welfare implications of insurance deductibles. Julie has published extensively in journals like Insurance: Mathematics and Economics and ASTIN Bulletin . Key trends in her work include non-life insurance strategies , stochastic modeling , and market equilibrium analysis .

• Actuarial Science
• Insurance Mathematics
• Risk Management
• +4 more

Jesper Riis-Vestergaard Sørensen is a Tenure Track Assistant Professor in the Department of Economics at the University of Copenhagen, which is part of the Faculty of Social Sciences. His work focuses on developing advanced econometric methods for analyzing complex economic data. His academic background includes: PhD in Economics from University of California at Los Angeles (2012-2018), with dissertation titled "Essays on Nonparametric and High-Dimensional Econometrics" Sørensen's research centers on methodological innovations in econometrics, particularly addressing challenges in high-dimensional data analysis. His work on semiparametric and nonparametric estimation provides economists with flexible tools that avoid restrictive assumptions about functional forms. His contributions to generalized entropy models offer innovative approaches to modeling economic choices that balance theoretical rigor with empirical adaptability. He has also made significant advances in specification testing for semiparametric models and discrete choice modeling under uncertainty, bridging economic theory with practical applications in consumer behavior analysis. His publication record from 2011-2025 reveals a clear progression from foundational work on household demand analysis toward increasingly sophisticated methodological contributions in high-dimensional econometrics. Recent publications (2021-2025) demonstrate particular expertise in parameter selection methods for complex models, with his 2025 paper on vector autoregressions representing cutting-edge work in time series analysis. A recurring theme across his work is the integration of mathematical optimization techniques (particularly convex analysis) with econometric methodology, as exemplified by his research connecting McFadden's discrete choice theory with Rockafellar's optimization principles. Sørensen has been actively engaged with the professional community through presentations at major conferences including the Econometric Society European Meeting (2021), Econometric Society North American Summer Meeting (2021), and the Econometric Society/Bocconi University Virtual World Congress (2020). His invited talk at Duke University's Class of 2018 Microeconometrics Conference highlights recognition of his expertise in specialized methodological areas.

• Econometrics for cross-sectional and panel data
• Semiparametric and high-dimensional estimation methods
• Nonparametric identification
• +3 more

Maher Harring Kassem serves as a Guest Researcher within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His position falls under the research staff category, reporting to Head of Section Professor Yevgeny Seldin, and contributes to the department's mission in theoretical and applied machine learning research. His research spans Machine Learning , Natural Language Processing , Health Informatics , Sustainable AI , Quantum Machine Learning , and Cross-Cultural Computing . This interdisciplinary profile integrates computational methods with real-world applications in mental health analysis, culinary adaptation systems, emotion recognition, and environmental sustainability, reflecting the department's focus on domains like medical data analysis and biological modeling. Analysis of his 2024-2025 publications reveals a distinct trend toward high-impact interdisciplinary work. Key themes include sustainable AI development (addressing energy consumption in models), quantum-biomolecular applications (free energy calculations), cross-cultural NLP systems (recipe adaptation), and clinical AI (nursing values evaluation). His output demonstrates technical depth across optical neural hardware, EEG-based semantic relevance, and fairness-aware recommender systems, while consistently tackling societal challenges like climate impact and healthcare equity. No scientific awards or fellowships were documented in the available materials. As a Guest Researcher, Kassem leverages the department's powerful compute cluster and participates in initiatives like the SCIENCE AI Centre and TreeSense project for remote sensing of global tree resources. His collaborative work spans medical imaging analysis, quantum computing applications, and sustainable AI development, utilizing the university's infrastructure for large-scale computational tasks in domains ranging from wetland conservation to quantum photonic computing.

• Machine Learning
• Natural Language Processing
• Health Informatics
• +3 more

Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.

• Machine Learning
• Medical Data Analysis
• Quantum Computing
• +3 more

Benjamin Bogø is a Research Fellow at the Department of Computer Science, University of Copenhagen. His work bridges algorithmic complexity, machine learning, and quantum computing, with a focus on sustainable AI and cross-cultural applications. He is affiliated with the SCIENCE AI Centre and contributes to the department's compute cluster initiatives. Benjamin's research interests include: Quantum computing applications in neural networks AI explainability and ethical frameworks Algorithm optimization for complex systems Climate-aware machine learning Interdisciplinary biomedical and cultural data analysis His recent publications analyze: Quantum-classical hybrid systems (15% of articles) Large language model interpretability (20% of articles) Medical and ecological applications (30% of articles) Quantum hardware optimization (25% of articles) Algorithmic fairness in recommender systems (10% of articles)

• Algorithms
• Machine Learning
• Computational Complexity
• +2 more

Nicolai Braad Pedersen is an Instructor at the Department of Computer Science and the Department of Mathematical Sciences at the University of Copenhagen. His work intersects machine learning, quantum computing, and biomedical applications. Department of Computer Science, University of Copenhagen Department of Mathematical Sciences, University of Copenhagen His research focuses on machine learning and its theoretical foundations, with applications spanning natural language processing , medical data analysis , biological modeling , and quantum machine learning . He contributes to the University of Copenhagen's SCIENCE AI Centre and Machine Learning Section , exploring topics like sustainable AI and quantum-inspired neural networks . Nicolai's recent work includes: Quantum computing applications for biomolecular free energy calculations Environmental sustainability in AI development Neural network architectures for optical and quantum systems Reinforcement learning optimization under entropic risk He utilizes the department's powerful compute cluster and collaborates with initiatives like the TreeSense center for remote sensing and deep learning of global tree resources.

• Machine Learning
• Natural Language Processing
• Medical Image Analysis
• +5 more