جست‌وجوی استاد و پژوهشگر

• Aging and Motor Skill Learning
• Neuroimaging (MRI)
• Brain Structural Networks
• +۵ مورد دیگر

Mikael Novén serves as an Assistant Professor in the Movement and Neuroscience division of the Department of Nutrition, Exercise and Sports at the University of Copenhagen. His research program bridges aging studies, motor control, and neuroimaging with a focus on how the aging brain learns fine-motor skills using advanced MRI techniques including functional imaging, diffusion-weighted data, and quantitative T1 mapping. His research interests center on aging-related changes in motor learning , brain structural networks , and language processing mechanisms . Using MRI methodologies, he investigates functional and structural reconfigurations during precision pinch tasks while also exploring brain correlates of phonological proficiency. His work spans cognitive neuroscience, gerontology, and MRI physics development with particular emphasis on sensorimotor integration across the lifespan. Analysis of his publication record reveals consistent research trajectories in age-related motor control differences (particularly 2024-2025 bimanual tracking studies), neurolinguistics (2021-2024 language processing work), and MRI methodology development (2021 physics paper). His recent work increasingly focuses on comparative analyses between younger and older adults, with three 2024-2025 publications specifically addressing aging effects on motor skill acquisition. Dr. Novén maintains active professional engagement through Twitter (@NeuroLingMi) and GitHub (MikNoven), with research outputs appearing in high-impact journals including NeuroImage, Human Brain Mapping, and Frontiers in Aging Neuroscience. His Scopus Author ID is 57199058849 and ORCID is 0000-0003-0256-0522.

• Semantic Web
• Formal Methods
• Digital Twins
• +۳ مورد دیگر

Eduard Kamburjan is an Assistant Professor in the Department of Software Engineering at the IT University of Copenhagen. His research focuses on integrating Semantic Web technologies with formal methods to develop and analyze data-heavy computational systems, particularly Digital Twins. He is the creator of the SMOL language for knowledge graph integration and Crowbar, a deductive verification system for Active Objects. His current work explores novel approaches to modularity in program verification, emphasizing contracts for distributed and hybrid systems. In 2024, he contributed to a publication on probabilistic dynamic logic proof systems. Eduard's research is affiliated with the Software Quality Research (SQUARE) group. His ORCID profile (0000-0002-0996-2543) and contact details, including his office in Copenhagen (Denmark), are publicly accessible.

• Programming Languages
• Theory of Computation
• Computer Security
• +۳ مورد دیگر

William Shi is an Instructor at the Department of Computer Science, University of Copenhagen. His academic work is aligned with the Programming Languages and Theory of Computation Section (PLTC), which focuses on programming language technology and theoretical aspects of computation. The PLTC section engages in interdisciplinary research across areas such as decentralized systems, security and privacy, functional programming for high-performance architectures, and quantum computing. Specific projects include blockchain technology, formal verification, and probabilistic programming with applications in bioinformatics and AI-driven vaccine design. Research interests intersect algorithmic programming, formal methods, and applied domains like fintech and cybersecurity. While no direct information about William Shi's specific research contributions, awards, or publications is available in the provided text, his affiliation with PLTC suggests involvement in cutting-edge computational theory and its practical implementations.

• Programming Languages
• Formal Verification
• Privacy Risk Analysis
• +۲ مورد دیگر

Willard Rafnsson is an Associate Professor in Programming Logic and Semantics at IT University of Copenhagen. His research develops formal methods for security and privacy, particularly probabilistic programming approaches for quantifying privacy risks. Research investigates privacy risk assessment methodologies, attacker knowledge modeling, and formal verification techniques. Recent work examines privacy vulnerabilities in genetic data and develops tools for leakage quantification. Rafnsson leads projects on practical static analysis and reliable AI for autonomous systems, focusing on security foundations.

Andreas Kühne Larsenمشاهده پروفایل

پژوهشگر ارشد

• Operations Research
• Optimization
• Artificial Intelligence
• +۳ مورد دیگر

Andreas Kühne Larsen is a PhD Fellow at Aalborg University's Department of Materials and Production within the Faculty of Engineering and Science. He conducts research in the Section of Applied Systems Research specializing in Artificial Intelligence for Operations Research and is affiliated with AAU BLUE – Marine & Maritime Research. Education: MSc in Mathematical Engineering Research Interests: Larsen focuses on developing optimization algorithms and AI-driven solutions for complex operational problems. His work integrates deep learning, genetic algorithms, and dynamic probability modeling to address challenges in maritime search and rescue, drone-based detection systems, and satellite scheduling under real-world constraints. Publication Trends: His 2024-2025 publications reveal a consistent emphasis on constraint optimization for dynamic environments, particularly in maritime operations (e.g., path-finding with probabilistic target modeling) and industrial systems (e.g., throughput tradeoff mitigation). These works frequently employ heterogeneous sensor networks and AI techniques to enhance decision-making in time-sensitive scenarios. Professional Engagement: Larsen actively participates in the academic community, including attendance at the 33rd European Conference on Operational Research (2024). His research is embedded within interdisciplinary initiatives like AAU BLUE, targeting marine and operational challenges through collaborative frameworks.

• Bisimulation
• Probabilistic Automata
• Mobile Networks
• +۷ مورد دیگر

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 Jensenمشاهده پروفایل

دانشیار

• Model Checking
• Formal Verification
• Reinforcement Learning
• +۸ مورد دیگر

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).

Lys Sanz Moretaمشاهده پروفایل

پژوهشگر ارشد

• Molecular Biology
• Machine Learning
• Probabilistic Programming
• +۳ مورد دیگر

Lys Sanz Moreta is a Research Fellow at the University of Copenhagen, holding dual appointments as a Postdoc in the Department of Cellular and Molecular Medicine (Faculty of Health and Medical Sciences) and a Guest Researcher in the Department of Computer Science's Programming Languages and Theory of Computation group. Her work bridges molecular biology and advanced machine learning to solve complex biomedical problems. Education PhD in Computer Science, University of Copenhagen, 2022 Dr. Moreta specializes in interdisciplinary research, applying Bayesian modeling and deep learning to molecular biology challenges. Her expertise spans protein structure prediction, viral epitope analysis, and probabilistic programming, enabling cross-disciplinary collaborations between computational and biomedical fields. She emphasizes optimizing programming and mathematical skills to connect diverse knowledge domains. Her 2019-2025 publications reveal a consistent focus on integrating machine learning with structural biology and immunology, particularly through variational autoencoders and probabilistic frameworks for protein superposition, ancestral sequence resurrection, and viral T-cell epitope prediction. This research has significant implications for drug design and molecular evolution studies. Scientific Awards No major scientific awards are documented in the provided profile. While no student advising or grant details are listed, her active roles in the Glycomics Program and Programming Languages research group suggest ongoing collaborative projects. Her interdisciplinary approach facilitates knowledge transfer between computer science and biomedical research teams. She maintains affiliations with the Glycomics Program (Department of Cellular and Molecular Medicine) and the Programming Languages and Theory of Computation group (Department of Computer Science), driving innovation at the computational-biology interface.

Søren Højsgaardمشاهده پروفایل

دانشیار

• Statistics
• Graphical Models
• Bayesian Networks
• +۷ مورد دیگر

Søren Højsgaard is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His work bridges statistical theory, computational methods, and pedagogical innovation, with a strong focus on graphical models, Bayesian networks, and computer algebra systems in R. His research interests lie at the intersection of statistics, machine learning, and software development. He is particularly known for developing R packages such as caracas , gRbase , and sparta , which facilitate symbolic computation and efficient inference in probabilistic models. His work supports both advanced research and accessible teaching in data science. The recent publications highlight a consistent trend toward integrating symbolic mathematics with statistical computing, improving scalability in Bayesian network predictions, and enhancing statistics education through tools like Quarto and R. His contributions span theoretical algorithms, software implementation, and educational applications. Active contributor to open-source statistical software Focus on model-based prediction and symbolic computation Emphasis on teaching innovation using computational tools He has been involved in academic outreach through conference presentations and media engagement, discussing topics ranging from R programming to workplace safety modeling. His leadership role in the Department of Mathematical Sciences was recently highlighted in university communications. Søren Højsgaard leads and contributes to projects that combine rigorous statistical methodology with practical implementation, supporting both research and education in modern data science.

• Operational Research
• Healthcare Optimization
• Stochastic Optimization
• +۷ مورد دیگر

Troels Martin Range is an Associate Professor at the Department of Clinical Research, University of Southern Denmark, affiliated with Odense University Hospital (OUH) and the Research Unit of Emergency Medicine in Odense. His work bridges operational research and clinical medicine, focusing on optimizing healthcare delivery systems. His research interests lie primarily in operational research , stochastic optimization , and healthcare analytics . He investigates emergency department patient flow , surgery scheduling , and resource allocation using advanced mathematical models such as column generation , simulation modeling , and machine learning techniques . His work often integrates real-world data including meteorological and calendar variables to improve forecasting accuracy. The analysis of his recent publications reveals a consistent trend toward applying rigorous mathematical and computational methods to solve complex problems in healthcare operations. His work spans both theoretical contributions in Mathematical Programming and practical implementations in hospital settings, particularly in emergency and surgical care. He frequently employs stochastic modeling , decision forests , and neural networks to address uncertainty in medical systems. Editor, International Journal of Advanced Operations Management Member, Dansk Selskab for Operationsanalyse (DORS) Guest Lecturer on hospital staff rostering and resource planning He has taught courses in operations analysis and has supervised academic work, though specific advisees are not listed. His research has been supported through academic collaborations and implementation projects in hospital environments. He is actively involved in knowledge transfer between academia and healthcare institutions. Dr. Range is part of a research network focused on emergency medicine and operational efficiency, contributing to both national and international collaborations. His lab or team context centers on the Research Unit of Emergency Medicine at OUH, where data-driven approaches are developed to enhance clinical decision-making and system performance.

• Machine Learning
• Computational Biology
• Protein Structure Prediction
• +۳ مورد دیگر

Wouter Boomsma is a Professor in the Machine Learning section of the Department of Computer Science at the University of Copenhagen. He leads the BioML research group, which focuses on developing machine learning techniques to address fundamental challenges in biology, particularly in the realm of protein science. His research interests center around understanding the relationship between protein sequence, structure, and function through advanced machine learning approaches. Key research areas include representation learning of protein sequences and structures, 3D structure prediction, and systematic protein optimization using techniques like Bayesian optimization. His work has significant applications in protein engineering, basic biological research on mutation effects in protein interaction networks, and health-related studies on human disease mutations. Boomsma's research program recently received substantial funding from the Novo Nordisk Foundation to establish the Center for Basic Machine Learning Research in Life Science (MLLS), a collaborative effort involving six machine learning research groups in the Copenhagen area. This initiative underscores the significance and potential impact of his work at the intersection of machine learning and life sciences. His publication record demonstrates a consistent focus on applying machine learning to protein science challenges, with recent work emphasizing protein structure prediction, variant effect analysis, and Bayesian optimization for protein engineering. The research output shows strong interdisciplinary connections between computer science, biology, and medicine. Boomsma maintains active collaborations with industry partners and research institutions, with his work gaining recognition through citations and media coverage. His group's research has been featured in high-impact journals including Nature Methods, Nature Communications, and PLOS Computational Biology. The BioML group operates within the Department of Computer Science's Machine Learning section at the University of Copenhagen, with physical location at Universitetsparken 1, Copenhagen Ø. The group maintains an active presence through their website (https://ku-bioml.github.io) and continues to expand its research portfolio in machine learning for biological applications.

• Computer Science
• Programming Language Technology
• Theory of Computation

Ying Liu is a Lecturer (Instructor) at the Department of Computer Science (DIKU), University of Copenhagen. Based at Universitetsparken 1 in Copenhagen, she contributes to the academic activities of the Programming Languages and Theory of Computation (PLTC) section. Her affiliation places her within a research environment focused on programming language technology, theory of computation, and their applications in security, systems, and high-performance computing. As part of the PLTC section, Liu likely engages with research topics spanning algorithmic aspects of programming, formal verification, computer security, distributed systems, and functional programming techniques. The section maintains active research groups in areas including Decentralized Systems, Security & Privacy, Functional Technology for Modern Architectures (FUTHARK), and Probabilistic Programming.

• Programming Languages
• Theory of Computation
• Formal Verification
• +۴ مورد دیگر

Tudor-Ovidiu Pal is a Lecturer at the Department of Computer Science , University of Copenhagen, contributing to teaching and research in programming language technology and theoretical computer science. He is affiliated with the Programming Languages and Theory of Computation Section (PLTC), which focuses on foundational and applied research spanning algorithmic programming, formal verification, computer security, and high-performance computing systems. His work intersects with key research groups within PLTC, including investigations into secure networked systems (blockchain, distributed ledger technology), software-based security techniques, functional programming optimizations, and probabilistic programming applications in bioinformatics and AI-driven vaccine design. He also engages with topics like program inversion, reversible computing, and quantum programming paradigms through collaborative efforts with other researchers in the section.

Hessam Golmohamadiمشاهده پروفایل

استادیار

• Smart Grid
• Energy Flexibility
• Electric Vehicles
• +۵ مورد دیگر

Hessam Golmohamadi is an Assistant Professor at Aalborg University's Faculty of Engineering and Science, specializing in Electric Power Systems and Microgrids. His research focuses on sustainable energy systems, smart grid technologies, and demand-side flexibility, particularly in residential and industrial sectors. He holds a PhD in Power and Smart Energy Systems (2019) from Aalborg University, with a thesis on retail energy management in electricity markets. Key projects include FLEXDIS (2024–2027), SUSTENANCE (2021–2024), and SERENE (2021–2025), addressing power system stability, carbon-neutral energy communities, and integrated energy systems. His work aligns with UN SDG goals for sustainable energy and climate action. Research interests span electric vehicle charging management, energy storage resilience, machine learning for thermal dynamics, and demand-response programs. Recent publications emphasize Bayesian optimization for energy efficiency, droop control in EV systems, and cognitive optimization for port energy storage. His articles explore technical advancements in grid-forming converters, hardware-in-loop testing, and multi-carrier microgrids. He collaborates widely, with a focus on丹麦 (Denmark) and欧盟 (EU) initiatives.

Mikkel Meyer Andersenمشاهده پروفایل

دانشیار

• Forensic Genetics
• Statistical Analysis of DNA Evidence
• Biostatistics
• +۳ مورد دیگر

Mikkel Meyer Andersen is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Denmark, within The Faculty of Engineering and Science. His research focuses on forensic genetics, statistical analysis of DNA evidence, and the application of mathematical models to evaluate genetic data. He specializes in Y-chromosome and mitochondrial DNA analysis, contributing to forensic investigations through methodologies like the discrete Laplace method for haplotype frequency estimation. He has participated in research projects such as Y Chromosome DNA Analysis (2010–2013) and Statistical models for forensic DNA data (2010–2012). His work bridges theoretical statistics with practical forensic applications, including collaborations on DNA evidence interpretation and genetic genealogy. Andersen is also actively involved in public engagement, appearing as an expert in media, television, and podcasts to discuss forensic science and DNA analysis. His research outputs include over 68 publications, datasets on DNA methylation and SNP genotyping, and impactful contributions like the DNAtools R package for forensic data analysis. He frequently presents at conferences and public forums, emphasizing the societal implications of forensic genetics. His recent work addresses advancements in next-generation sequencing and probabilistic genotyping methods for forensic applications. Andersen’s contributions have assisted law enforcement agencies, notably Norwegian investigators, through mathematical models for DNA evidence evaluation. His interdisciplinary approach integrates biostatistics, genetics, and computational methods to advance forensic science and public understanding of genetic evidence.