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Christian Anker HviidView profile

Associate Professor

• Building Energy Systems
• Heating, Ventilation, and Air Conditioning (HVAC)
• Energy Efficiency in Buildings
• +12 more

Christian Anker Hviid is an Associate Professor at the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), specializing in energy-efficient building services, HVAC systems, and sustainable building operation. He is actively engaged in research projects focused on digital twins, BIM integration, and data-driven modeling for building performance optimization. Research Interests: His work centers on improving energy efficiency and indoor environmental quality in buildings. Key areas include optimal building operation, commissioning, fault detection, and control of building services. He integrates physical modeling with semantic web technologies and data analytics to develop intelligent, interoperable systems for building management. Publication Trends: Recent publications emphasize the integration of BIM and Modelica simulations, semantic web-based management of HVAC data, and grey-box modeling for performance benchmarking and virtual sensing. These works reflect a strong focus on practical, data-enabled tools for real-world building systems, particularly in office and residential contexts. Chairman, DANVAK Ventilation Group (2018–2021) Board Member, IBPSA Nordic Board Member, EnergyLab Nordhavn External Researcher, Saint-Gobain Nordic (2015–2020) Consultant, Alectia A/S (2006–2015) Advising and Grants: He supervises multiple PhD students in projects related to intelligent building energy services, optimal heating, and digital control. His research is supported by active grants in areas like AI-driven heating optimization, data-enabled control, and linked building services, often involving collaboration with industry and public partners. Labs and Teams: He is part of the research environment at DTU Construct, focusing on energy and services within civil and mechanical engineering. His team works on cyber-physical models, digital twins, and interoperable building information systems, contributing to national and international efforts in sustainable building technologies.

Søren Wengel MogensenView profile

Associate Professor

• Causal Inference
• Stochastic Processes
• Survival Analysis
• +3 more

Søren Wengel Mogensen is an Associate Professor at the Department of Finance, Copenhagen Business School, Denmark. His research focuses on developing advanced statistical and machine learning methodologies for complex systems analysis. Research Interests: Dr. Mogensen's work spans causal inference, stochastic processes, survival analysis, and time-series modeling. Key themes include: Causal discovery algorithms for industrial and biological systems Graphical representations of dependencies in high-dimensional data Time-varying mediation in survival contexts Bayesian networks for cascade modeling Publication Trends: His recent articles (2021-2025) demonstrate a strong emphasis on theoretical-statistical innovation with applications in healthcare, industrial monitoring, and computational finance. Dominant methodologies include kernel-based independence tests, continuous-time Bayesian networks, and constrained stochastic process modeling.

• Natural Language Processing
• Machine Learning
• Automatic Fact-Checking
• +4 more

Asta Feodora Sjöberg Burhenne is an Instructor at the Department of Computer Science (DIKU), University of Copenhagen. She is affiliated with the university's Natural Language Processing (NLP) section, which focuses on methods for automated text processing, understanding, and generation using statistical models and machine learning. Research areas include: Natural Language Processing, Machine Learning, Automatic Fact-Checking, Machine Translation, Question Answering, Visually-Grounded Language Learning, and Multi-Modal Language Processing.

• Natural Language Processing
• Multilingual Language Models
• Knowledge Representation
• +5 more

Constanza Catalina Fierro Mella is a PhD Fellow and Postdoc in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen's Faculty of Science. Her research bridges computational linguistics with philosophical inquiry into knowledge representation in language models. Her primary research interests include Multilingual Language Models , Knowledge Representation , Mechanistic Interpretability , and Cross-cultural NLP . Fierro's work examines how language models remember and represent facts across languages, with particular attention to the epistemological foundations of artificial intelligence. Her publication record shows a strong focus on understanding the inner workings of language models, with recent papers exploring how multilingual models remember facts, the intersection of philosophy and mechanistic interpretability, and knowledge representation in large language models. Her research often involves collaboration with Anders Søgaard and other members of Copenhagen's NLP group. Fierro has received significant attention for her work, with papers like 'Challenges and Strategies in Cross-Cultural NLP' garnering over 100 citations. Her research spans both theoretical foundations and practical applications of natural language processing. She has contributed to diverse areas including multimodal learning (investigating connections between vision and language models), healthcare applications (predicting hospital readmissions), and historical document analysis (date recognition in parish records).

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +5 more

Carl Christian Ottesen is an Instructor at the Department of Computer Science, University of Copenhagen. He is affiliated with the Natural Language Processing (NLP) section, which focuses on methods for text processing, understanding, and generation using statistical models and machine learning. Research Interests Natural Language Processing (NLP) Machine Learning Computational Linguistics Multi-modal Language Processing Applications Automatic Fact-Checking Machine Translation Question Answering Visually-Grounded Language Learning

Ekkart KindlerView profile

Associate Professor

• Model-Based Software Engineering
• Process Mining
• Petri Nets
• +4 more

Ekkart Kindler is an Associate Professor in the Department of Applied Mathematics and Computer Science at DTU Compute, Technical University of Denmark. His research focuses on model-based software engineering, process mining, and road condition assessment using vehicle sensor data. He leads the Competence Centre for Model-Based Software Engineering, supporting industry adoption of advanced software development methodologies. Education: M.Sc. (1990), Ph.D. (1995) from Technische Universität München; Habilitation in Computer Science (2001) from Humboldt-Universität zu Berlin. He held visiting professorships at German universities (2000–2007) and has extensive experience in formal methods, business process modeling, and Petri nets. Research interests include declarative process modeling, complexity metrics for cognitive load assessment, and the integration of formal methods into industrial software development. His work contributes to UN Sustainable Development Goals related to sustainable infrastructure (SDG 9) and innovation (SDG 9). Notable collaborations include road condition assessment projects using vehicle sensor data (LiRA-CD dataset) and contributions to Petri net standards (PNML). He has supervised PhD students such as K. I. Simonsen (protocol software) and A. Skar (road assessment). Grants and projects: Live Road Assessment with Vehicle Sensors (2019–2022), Model Transformation Tools (2013–2016), and waste management modeling (2012–2016). Active in open-source datasets and tool development for indoor climate control (climify.org).

Rasmus Lund JensenView profile

Associate Professor

• Building Performance
• Energy Efficiency
• HVAC Systems
• +3 more

Rasmus Lund Jensen is an Associate Professor and Head of the Study Board of Civil Engineering at Aalborg University's Department of the Built Environment, within The Faculty of Engineering and Science. His primary research focuses on building systems, energy efficiency, indoor environmental quality, and sustainable design. He leads the Energy and Buildings Research Group and actively contributes to projects like HEATWISE and ReMaBrick, addressing thermal management and recycled materials in construction. Research interests include HVAC systems optimization, fault detection in building automation, and holistic assessment tools for indoor environmental quality. He has authored over 335 publications and secured multiple grants, including leadership roles in EU-funded initiatives. Notable awards include the Best Paper Award at CISBAT 2021 and the 2020 RM4L2020 award. His work emphasizes bridging academic research with practical industry applications, particularly in energy-efficient and climate-resilient building design. Recent projects involve improving energy pile foundations, optimizing diffuse ceiling ventilation, and developing datasets for occupancy and energy consumption analysis. Collaborations span academia and industry, fostering innovations in smart building technologies and sustainable materials.

Ulrik NymanView profile

Associate Professor

• Computer Science
• Formal Verification
• Real-Time Systems
• +5 more

Ulrik Nyman is an Associate Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. His research focuses on formal verification, real-time systems, embedded software, and cyber-physical systems. He is affiliated with the CISS - Center for Embedded Software Systems and has contributed to projects like Digital Technologies for Industry 4.0 and compositional verification of real-time systems. Research Interests: Formal Methods and Verification Real-Time and Embedded Systems Cyber-Physical Systems Power Electronics Control Modal Transition Systems Interface Theories Projects: Digital Technologies for Industry 4.0 (2019–2021): Focused on industrial automation and predictive models. Compositional Verification of Real-Time Multi-Core Safety-Critical Systems (2017–2021): Addressed safety-critical avionics and multicore schedulability. CRAFTERS: Framework for Tailoring Embedded Real-Time Systems (2012–2015): Developed application-driven middleware. Publications: Nyman’s recent work explores statistical model checking in power electronics, falsification testing for cyber-physical systems, and formal verification of OS-based software. His research bridges theoretical formal methods with practical applications in embedded systems. Labs/Teams: Active in the CISS Center, collaborating on embedded software systems and real-time frameworks.

Martin KristjansenView profile

Researcher

• Computer Science
• Real-Time Systems
• Model Checking
• +5 more

Martin Kristjansen is a Researcher in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. He is part of the Distributed, Embedded and Intelligent Systems research group, focusing on real-time systems, model checking, and distributed computing. His research interests span multiple areas of computer science with a focus on: Real-Time Systems and Schedulability Analysis Model Checking and Formal Verification using UPPAAL Distributed Systems and Multi-core Architectures Reinforcement Learning applications in battery management Fleet management in noisy environments Automata theory and symbolic model learning Dr. Kristjansen's publication record shows a consistent research trajectory from avionics systems in 2019 to more recent work on stochastic digraph real-time task models in 2024. His work demonstrates a strong focus on applying formal methods to practical engineering problems, particularly in safety-critical systems, with research areas spanning automaton theory (100%), mobile robotics (85%), multicore systems (85%), and performance analysis (85%). He has been actively involved in significant research projects including: STORM_SAFE: Software reliability for critical infrastructures (2024-2027) as a Project Participant Digital technologies for Industry 4.0 (2019-2021) as a Project Participant Dr. Kristjansen collaborates extensively with Professor Kim G. Larsen and other researchers at Aalborg University, contributing to the institution's strong reputation in formal methods and real-time systems research. His work includes two significant datasets related to symbolic timed models and distributed fleet management, demonstrating commitment to research reproducibility.

• Bisimulation
• Probabilistic Automata
• Mobile Networks
• +7 more

Jens Christian Godskesen is a Lecturer in Theoretical Computer Science at the IT University of Copenhagen. He has maintained an active research career with 19 publications and involvement in 20 research projects spanning from 1999 to the present. His academic work focuses on formal methods and theoretical computer science with particular emphasis on bisimulation techniques and probabilistic systems. Godskesen's research interests center around bisimulation theory, probabilistic automata, mobile networks, and stochastic calculus. His work explores behavioral equivalence in computational systems, logical characterizations of probabilistic models, and formal verification techniques. He has made significant contributions to understanding probabilistic bisimulation for realistic schedulers and developing abstraction refinement techniques for complex systems. His research bridges theoretical computer science with practical applications in security and networking. Analysis of his publication trends shows a consistent focus on bisimulation theory and probabilistic systems from 2012-2015, with recent expansion into blockchain technology and urban data analytics. His work demonstrates strong theoretical foundations while addressing practical challenges in mobile security and network modeling. The interdisciplinary nature of his research connects computer science theory with applications in security, networking, and fintech. Godskesen has served as Principal Investigator for numerous research projects including the ongoing Urban Insights Data Lab (2023-present), the Velux Visiting Professor Program (2011-2012, 2015-2016), CMS: Calculi for Mobile Security (2006-2010), and the 6th International Blockchain School (2023). He has also contributed to academic governance as chair of the hiring committee for a full professor in Digital Play (2022-2023). With 92 media contributions documented through 2024, Godskesen actively engages with public discourse on IT education, blockchain technology, and digital leadership. His recent media appearances include commentary on the IT University's new vice-chancellor at the University of Copenhagen (November 2024), DigitalLead's new board of directors (August 2024), and concerns about declining enrollment in Danish IT programs (July 2022).

Peter Gjøl JensenView profile

Associate Professor

• Model Checking
• Formal Verification
• Reinforcement Learning
• +8 more

Peter Gjøl Jensen is an Associate Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. His research spans formal methods, artificial intelligence, and practical applications in energy systems. He is affiliated with multiple research groups including Distributed, Embedded and Intelligent Systems, AI for the People, and Artificial Intelligence and Machine Learning. His research interests focus on Model Checking , Formal Verification , Reinforcement Learning , and applications to Cyber-physical Systems . He has developed expertise in applying theoretical computer science to solve real-world problems, particularly in energy optimization and heat pump control systems. His work bridges the gap between formal methods and practical engineering applications. His recent publications show a clear trend toward applying AI and formal verification techniques to energy systems and cyber-physical applications. The research spans theoretical foundations of model checking and reaches into practical implementations for heat pump control, autonomous systems, and environmental management. His work often involves the UPPAAL and Stratego frameworks for verification and synthesis. Dr. Jensen actively supervises PhD students, including Andreas Holck Hoeg-Petersen on the "Explainable and Causally Enforced Reinforcement Learning" project. His research has attracted media attention, particularly for intelligent heat pump control systems that provide significant energy savings. He is involved in multiple research projects including "Explainable and Causally Enforced Reinforcement Learning" (ongoing) and "BEO-COVID: Decision Support for Evaluation and Optimization in UPPAAL" (completed in 2020).

• Sensory Science
• Food Analytics
• Consumer Research
• +3 more

Tormod Næs serves as an Adjunct Professor in the Department of Food Science at the University of Copenhagen, specializing in Food Analytics and Biotechnology. His dual institutional affiliation includes Nofima (Norwegian research institute), as evidenced by his professional email address. His research bridges statistical methodology with practical food science applications. His primary research interests focus on sensory science methodology , consumer behavior analysis , and multivariate statistics for food data . He pioneers techniques for interpreting complex datasets including temporal check-all-that-apply (TCATA) responses, L-shaped sensory-consumer matrices, and seafood storage stability metrics. His work emphasizes component-based methods like PCA and PLS to extract meaningful patterns from noisy food-related data. Analysis of his recent publications (2021-2025) reveals three dominant research thrusts: (1) Advanced statistical frameworks for sensory data integration, (2) Consumer segmentation based on product discrimination capabilities, and (3) Sustainable food systems research examining upcycled foods and labeling impacts. His methodological innovations consistently target real-world food industry challenges in quality assessment and product development.

• Natural Language Processing
• Artificial Intelligence
• Computer Vision
• +2 more

Yova Radoslavova Kementchedjhieva is a Research Fellow in the Department of Computer Science at the University of Copenhagen, specializing in Natural Language Processing within the university's NLP research group. Her work bridges artificial intelligence, computer vision, and language technologies through interdisciplinary collaborations. Her research focuses on Natural Language Processing, Artificial Intelligence, and Computer Vision, with specific expertise in cultural adaptation of recipes, vision-language model alignment, fact mutability in language models, multi-label classification for legal/biomedical text, grammatical error correction, and image captioning. She employs advanced multimodal learning and neural network analysis techniques to address complex linguistic and visual challenges. Recent publications reveal strong trends in vision-language integration, including vector space alignment studies and geometric convergence between language and vision models. Her work demonstrates applications in cross-cultural communication systems, dynamic fact verification, neural response modeling, and lightweight image captioning architectures. She actively contributes to the Natural Language Processing research group at the University of Copenhagen, collaborating with researchers like Anders Søgaard on projects spanning computational linguistics, multimodal AI, and cognitive modeling of language systems.

Anja BechmannView profile

Professor

• Digital Media Studies
• Social Media Communication
• Computational Social Science
• +5 more

Anja Bechmann is a Professor in Media Studies and Director of DATALAB – Center for Digital Social Research at Aarhus University. Her work focuses on digital and social media communication, collective behavior, and algorithmic platforms, leveraging computational social science and machine learning. She leads major research initiatives like the Independent Research Fund Denmark’s 'Social Media Influence' project and contributed to EU projects like NORDIS and EDMO. Recognized as a member of the Royal Danish Academy of Sciences and Letters and the Academy of Technical Sciences, she advises governments and international bodies on digital policy. Her research bridges academia and policy, addressing misinformation, platform governance, and AI ethics. Education background: Ph.D. in Media Studies (implied by role and publications). Key affiliations include DATALAB, EDMO (executive board member), and advisory roles at Jyllands-Posten and Nordjyske Holding. She has disclosed no direct industry funding but engaged with tech companies via data grants and policy dialogues, emphasizing independent research. Her work spans interdisciplinary methods, including API data collection, ethnography, and survey design adapted to digital contexts. Research interests emphasize algorithmic accountability, democratic impacts of big tech, and disinformation dynamics. Recent projects analyze misinformation exposure across European media systems, AI-generated content detection, and gender representation in voice assistants. She has published extensively on social media infrastructure, digital governance, and ethical AI frameworks. Awards highlight her contributions to Danish and European scientific communities, including the 2019 Thinker in Residence honor by the Royal Flemish Academy. Grants and collaborations include funding from Danish and EU agencies (e.g., Horizon 2020), and she co-founded the European Information and Media Fund (EMIF) to support independent research. Her advising spans policy drafts for the European Parliament, Ministry of Defense, and cultural agencies, reflecting her role as a bridge between academia and public policy. Labs/Teams: Leads DATALAB and collaborates with EDMO and NGI Forward initiatives. Current projects (e.g., OPINION, SOMA) address automated opinion analysis, disinformation, and digital media observatories. Her work often involves international partnerships, emphasizing Nordic and EU-wide research networks.

• Cyber-Physical Systems
• Systems Biology
• Formal Methods
• +7 more

Radu Grosu is a full professor and head of the Institute of Computer Engineering at the Faculty of Informatics, Vienna University of Technology (TU Wien). He also serves as a research professor in the Department of Computer Science at the State University of New York at Stony Brook (USA). His leadership extends to directing the Cyber-Physical Systems research group at TU Wien and previously co-directing the Concurrent-Systems Laboratory and co-founding the Systems-Biology Laboratory at SUNY Stony Brook. His research focuses on the modeling, analysis, and control of cyber-physical and biological systems. Key application domains include distributed automotive and avionic systems, Internet of Things (IoT), autonomous mobility, green operating systems, mobile ad-hoc networks, and biological networks such as cardiac, neural, and genetic regulatory systems. His methodological expertise lies in formal methods, hybrid systems, and compositional design. The recent trend in his publications emphasizes formal frameworks for cyber-physical system design, verification of AI-based controllers, green computing, and modeling of biological systems using hybrid and stochastic models. His work bridges theoretical rigor with practical applications in safety-critical domains. Scientific Awards: National Science Foundation Career Award State University of New York Research Foundation Promising Inventor Award Association for Computing Machinery Service Award Advising and Grants: Radu Grosu has co-directed research laboratories and mentored students in concurrent and systems biology research. He has secured competitive funding, evidenced by the NSF CAREER Award and other institutional recognitions. His leadership in founding and directing labs indicates strong grant acquisition and team mentorship capabilities. Labs and Research Groups: He leads the Cyber-Physical Systems group at TU Wien and previously co-directed the Concurrent-Systems Laboratory and co-founded the Systems-Biology Laboratory at SUNY Stony Brook, fostering interdisciplinary research in formal methods and biological computing.