Tommy Sonne Alstrøm is an Associate Professor at the Department of Applied Mathematics and Computer Science, DTU Compute, Danmarks Tekniske Universitet (DTU). His research focuses on machine learning, signal processing, and nanotechnology-based sensor systems. Key research areas include: Explainable AI for time series and spectroscopy Diffusion models for speech enhancement Federated learning optimization Probabilistic modeling of biosignals His recent publications demonstrate expertise in neural network geometry, federated learning privacy, and spectroscopic data analysis. Current work involves self-supervised learning for variable-channel time series and road condition modeling using LiRA-CD dataset. Contact: tsal@dtu.dk
Dongyu Gao serves as an Instructor in the Machine Learning section at the Department of Computer Science (DIKU), University of Copenhagen. His position places him within one of Scandinavia's leading computer science departments, which hosts the SCIENCE AI Centre and maintains strong connections with both theoretical and applied machine learning research. Dr. Gao's research interests center around machine learning with applications spanning information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. His work appears to bridge theoretical foundations with practical implementations, as evidenced by publications addressing quantum computing applications, environmentally sustainable AI practices, and advanced neural network architectures. The Machine Learning section at DIKU provides substantial computational resources including a powerful dedicated cluster and specialized initiatives like TreeSense for remote sensing applications. Analysis of recent publications associated with Dr. Gao reveals a diverse research portfolio spanning multiple cutting-edge AI domains. His work demonstrates particular strength in quantum machine learning applications, sustainable computing practices, and interpretable AI systems. The publications show a consistent pattern of interdisciplinary collaboration, connecting computer science with healthcare, environmental science, and quantum physics. Notably, several publications address the critical challenge of making AI systems more environmentally sustainable without sacrificing performance. The Machine Learning section operates within DIKU's broader research ecosystem, which includes strong connections to the SCIENCE AI Centre. This environment provides access to substantial computational resources and fosters collaboration across various AI subfields including natural language processing, computer vision, and theoretical machine learning. The department's location in Copenhagen positions it at the intersection of European AI research initiatives with strong connections to both academic and industry partners across the continent.
Bulat Ibragimov is an Associate Professor in the Department of Computer Science at the University of Copenhagen, specializing in the Image Analysis, Computational Modelling, and Geometry research section. His work focuses on applying advanced computational techniques to medical imaging problems, particularly in radiology and diagnostic applications. His research interests span multiple domains of medical AI, including: Medical image analysis and computer vision for diagnostic applications Eye tracking analysis to understand radiologist decision-making processes Deep learning applications for disease detection and severity classification Explainable AI systems for medical image interpretation Computational geometry applications in medical imaging Dr. Ibragimov's recent publications demonstrate a strong focus on applying artificial intelligence to improve diagnostic accuracy and efficiency in medical settings. His work frequently bridges the gap between computer science and clinical applications, with particular emphasis on radiology, cardiology, dentistry, and gastroenterology. Many of his studies incorporate eye tracking data to better understand and augment human decision-making in medical imaging contexts. His notable contributions include work on: Cardiometric analysis from chest X-rays Dental image analysis for abnormality detection Ulcerative colitis severity classification Explainable AI for medical image models Prediction of radiological decision errors using eye tracking data
Ernst á Heygum Kass is an Instructor at the Department of Computer Science (DIKU), University of Copenhagen. He is affiliated with the Natural Language Processing (NLP) section, which focuses on methods for automated text processing, understanding, and generation using statistical models and machine learning. Core research areas include natural language understanding, misinformation detection, explainable AI, and multi-modal machine learning intersecting with computer vision. Applications of his affiliated section's work span automatic fact-checking, machine translation, question answering, and visually-grounded language learning.
Daniel Nicholas Mølhave is an Instructor at the Department of Computer Science (DIKU) , University of Copenhagen, located at Universitetsparken 1, 2100 København Ø. His work is associated with the Natural Language Processing (NLP) section, which focuses on methods for automated text processing, understanding, and generation using statistical models and machine learning. University affiliation: University of Copenhagen Department: Department of Computer Science (DIKU) Email: damo@di.ku.dk The NLP section at DIKU engages in cutting-edge research spanning core areas such as: Natural language understanding Multi-modal machine learning Explainable AI Visually-grounded language learning Cross-lingual NLP Language technologies Research applications include machine translation, misinformation detection, image captioning, and multilingual multimodal representation learning. The section contributes to the SCIENCE AI centre and offers courses in the Bachelor's and Master's programs in Computer Science, Machine Learning, and Data Science.
Tobias Nordholm-Højskov is an Instructor at the Department of Computer Science , University of Copenhagen (DIKU). His research intersects machine learning with healthcare, sustainability, and quantum computing, focusing on theoretical foundations and applications in medical data analysis, climate-aware AI, and quantum systems. He is affiliated with the SCIENCE AI Centre and contributes to projects like QDarts (quantum dot array simulation) and TreeSense (remote sensing for environmental monitoring). His work spans diverse subfields, including Explainable AI for healthcare records Federated Learning in rare disease research Quantum-inspired neural networks Retrieval-Augmented Generation frameworks Environmental impact mitigation in AI
Henning Christiansen is a Professor at Roskilde University's Department of People and Technology, affiliated with the Programming, Logic and Intelligent Systems (PLIS) research group. He is also a Knight/Chevalier of the Dannebrog (2018) and serves as Coordinator for International Student Exchanges in Computer Science, Informatics & Humanities-Technology Studies. His research spans Deep Learning for medical diagnosis, Robotics in theatrical performances, Constraint Logic Programming, Probabilistic-logic models, Natural Language Processing, Logical methods for context comprehension, Interactive art installations, Database query systems, Cultural technology projects like Viskbook. Key projects include SEAFACTS (digital maritime history platform), EXPLAIN-ME (explainable AI in medical education), NDH (cross-border health data collaboration). Publications highlight contributions to AI ethics, robot choreography, medical image analysis, constraint-based formal methods. He has supervised over 16 projects and 285+ activities, including international conferences and exhibitions. His photography has been displayed in Roskilde libraries and cultural venues.
Morten Hasselstrøm Jensen is a Professor at Aalborg University Hospital's Department of Health Science and Technology, affiliated with the Faculty of Medicine. He holds additional roles as Data Lead Project Director at Novo Nordisk A/S and previously served as Senior Researcher at Steno Diabetes Center North Denmark. His research focuses on diabetes management, telemedicine, and glycemic control, particularly in insulin-treated populations. He leads projects like ADAPT-T2D (cloud-based personalized diabetes treatment) and Sten-O Starter (clinical effects of diabetes management tools). Education: Formal academic qualifications not explicitly listed in the provided text. Jensen's research interests include optimizing insulin therapy, predicting hypoglycemia, and leveraging machine learning for clinical decision support. His work emphasizes translating data into actionable insights for diabetes care, such as continuous glucose monitoring (CGM) analysis and telemonitoring systems. Key contributions include studies on cardiovascular risks in diabetes patients and adherence to insulin regimens. His articles explore topics like CGM data analysis, glycemic variability, and AI-driven treatment titration. Projects highlight interdisciplinary collaboration across clinical, engineering, and computational domains. Grants & Awards: No specific awards mentioned, but his projects (e.g., ADAPT-T2D) are funded through institutional and industry partnerships. Advising: Supervised 8 PhD students (names not listed). Jensen collaborates with Steno Diabetes Center and Novo Nordisk, contributing to clinical trials and digital health solutions aimed at improving diabetes outcomes through technology integration.
Kristian Bernt Karlson is a Professor in the Department of Sociology at the University of Copenhagen, affiliated with the Faculty of Social Sciences. He serves as Director of Graduate Studies in Social Science and leads the ERC-funded SIBMOB project on siblings' social mobility. His research focuses on educational stratification, social mobility, and quantitative methods, with notable contributions to intergenerational mobility analysis and nonlinear probability models. Karlson has held tenured positions since 2016 and serves on editorial and advisory boards including Sociological Science and the Nils Klim Prize Committee. Education: PhD, Faculty of Arts, Aarhus University (2013) MA Sociology, University of Copenhagen (2009) BA Sociology, University of Copenhagen (2006) Research Interests: Karlson investigates mechanisms behind educational and socioeconomic inequality, including sibling mobility comparisons and the role of educational expectations. He co-leads the UddanKvant research center and has led major projects funded by the ERC, Danish government ministries, and international foundations. Key Achievements: ERC Starting Grant (2021–2026) for SIBMOB project 2023 ASA Leo Goodman Mid-Career Award 2022 EAS Raymond Boudon Early Career Achievement Award Co-developed influential methodology for comparing logit/probit models (top-1% cited paper) Grants & Projects: Principal Investigator on 7 externally funded projects (including NORFACE, Rockwool Foundation) Managing interdisciplinary education research center (UddanKvant) Labs/Teams: Active in the Danish National Centre for Social Research (SFI), NORFACE network, and international collaborations with institutions like Oxford University and Yale University.
Andreas Holck Høeg-Petersen is a PhD fellow at the Department of Computer Science, Aalborg University, specializing in Reinforcement Learning (RL) , Explainable AI (XAI) , and Cyber-Physical Systems . His research focuses on integrating formal verification techniques with RL to enhance transparency and safety in autonomous systems. Fields of Interest : Reinforcement Learning, Explainable AI, Formal Verification, Autonomous Driving, Cyber-Physical Systems, Machine Learning. Email : ahhp@cs.aau.dk Research Trends : Andreas works on bridging formal methods with AI, particularly in applications like autonomous vehicles and environmental systems (e.g., reducing combined sewer overflows). His work emphasizes causality enforcement and model-predictive control in RL frameworks. Collaborations : He collaborates with researchers across Denmark and internationally, including supervisors Kim G. Larsen , Amir Ploeger , and Andrzej Wasowski , contributing to interdisciplinary projects in AI, formal verification, and environmental engineering.
Mohsen Ghaffari is a postdoctoral researcher in Computer Science at the IT University of Copenhagen, Denmark, affiliated with the Digital Research Center Denmark (DIREC). His research focuses on decision-making under uncertainty in multi-agent systems, leveraging software analysis to enhance the reliability and safety of reinforcement learning. He holds a PhD in Computer Science and is a supervisor at the Institute for Advanced Studies in Basic Sciences, Zanjan. Research Areas: Reinforcement Learning, Multi-Agent Systems, Game Theory, Smart Grids, Formal Methods External Roles: Teaching Assistant for Advanced Programming Projects: Co-Investigator on DIREC initiatives focusing on AI safety and autonomous systems. His work emphasizes theoretical foundations and practical applications of reinforcement learning, including formal specification frameworks and real-world implementations like epidemic modeling and smart grid optimization.
Merete Monrad is an Associate Professor at Aalborg University's Department of Sociology and Social Work, affiliated with The Faculty of Social Sciences and Humanities. She is a core member of WISER (Welfare, Innovation, Social Work and Employment Relations) and CUBB (Center for Udvikling af Borgerinddragende Beskæftigelsesindsatser). Her research focuses on emotions in welfare contexts, temporal control in unemployment, and citizen-state interactions, particularly regarding anger and normative frameworks. She leads projects like the ANGER study (2021-2024) examining societal anger expression norms and CUBB 2.0 (2024 ongoing) on participatory employment services. Her work intersects UN Sustainable Development Goals related to social inclusion and quality education. Key projects include analyzing risk assessments in social work (2018-2019) and exploring client participation in employment services. Monrad teaches social work theory, methodology, and professional practice at bachelor and PhD levels, emphasizing emotion sociology and temporal dynamics. Publications span 2008-2025, addressing topics like emotional capital in citizen agency, temporal control in unemployment, and disability rights advocacy. She collaborates internationally and engages media on societal issues like anger management and welfare policy. Projects: 8 funded initiatives including Velux Foundation grants Grants: Over DKK 10M from Velux, municipalities, and universities Media: 48+ press engagements explaining sociological concepts Her lab affiliations include the SSH Children and Youth Research Network, focusing on child welfare. Future work emphasizes digitalization impacts on welfare and temporal dynamics in marginalized populations.
Kristoffer Koch is a Clinical Associate Professor and Afdelingslæge (Department Head) at Aalborg University Hospital, affiliated with the Department of Clinical Microbiology under The Faculty of Medicine. His research focuses on clinical microbiology, hospital-acquired infections, and antimicrobial resistance, leveraging machine learning for patient risk stratification. He has contributed to studies on urinary tract infections, vancomycin-resistant Enterococcus faecium, and public health surveillance. His work integrates clinical practice with epidemiological analysis, emphasizing translational research. Notable research interests include Bayesian network models for infection risk prediction, nationwide cohort studies on antimicrobial resistance trends, and the impact of workplace exposure on respiratory disease outcomes. His datasets and publications reflect collaborations across disciplines, including informatics and public health.
Tung Kieu is a Tenure Track Assistant Professor in the Department of Computer Science at Aalborg University (Denmark), affiliated with The Technical Faculty of IT and Design and the Daisy Center for Data-intensive Systems. His research focuses on data engineering, time series analysis, anomaly detection, and machine learning applications in traffic forecasting and smart systems. Education: Ph.D. in Computer Science (Awarded May 2021). Research interests include time series forecasting, traffic modeling, robust autoencoder architectures for anomaly detection, and spatio-temporal data analysis. His work contributes to UN Sustainable Development Goals related to smart cities and infrastructure. Recent publications explore bias mitigation in text-video retrieval (BiMa), topology-aware traffic forecasting (TEAM), and stochastic routing in uncertain road networks. His frameworks emphasize lightweight algorithms (LightTS), causal relational learning, and continual calibration for quantized models (QCore). Collaborations involve international teams in data management and AI, with notable work on ensemble methods, explainable AI, and transfer learning in smart building systems.
Theis Erik Jendal is a Researcher at the Department of Computer Science, Aalborg University, affiliated with the Technical Faculty of IT and Design. He specializes in Recommender Systems and Knowledge Graphs, focusing on areas like explainable AI, graph neural networks, and inductive recommendation architectures. He participates in the Poul Due Jensen Professorate in Big Data and Artificial Intelligence (2019–2025), addressing challenges in query processing, knowledge graphs, semantic web, and open data. Key research interests include hypergraph models for explainable recommendation, gated architectures in knowledge graphs, and addressing challenges in knowledge graph embeddings. His work emphasizes interpretability, similarity search, and practical use cases in AI systems. Contributions include 5 peer-reviewed publications since 2020, spanning conferences like ECIR and CIKM, and a dataset contribution to the Yelp Collaborative Knowledge Graph (Zenodo, 2023). His research bridges theory and application, particularly in improving recommendation systems through advanced graph-based methodologies.
