جست‌وجوی استاد و پژوهشگر

Claus Brabrandمشاهده پروفایل

استاد

• Computing Education Research
• Gender Studies in STEM
• Diversity in Technology
• +۳ مورد دیگر

Claus Brabrand is a Professor of Software Engineering and Head of the Center for Computing Education Research at the IT University of Copenhagen. His research focuses on computing education, gender diversity in STEM, and software product line analysis. He has led projects like DIREC (Digital Research Centre Denmark) and ATTiKA (Adaptive Tools for Technical Knowledge Acquisition), funded by the Innovation Fund Denmark and Villum Foundation. PhD in Computer Science Over 45 publications in computing education and software engineering Active in curriculum development and educational policy Research interests include improving teaching quality through learning technologies, gender representation in IT materials, and cognitive competencies in programming education. His work bridges software engineering principles with pedagogical innovations, addressing issues like novice programmer performance and curriculum design.

Selin Karaمشاهده پروفایل

استاد

• Industrial Biotechnology
• Biocatalysis
• Enzyme Immobilisation
• +۳ مورد دیگر

Professor Selin Kara is affiliated with the Department of Biological and Chemical Engineering at Aarhus University, Faculty of Engineering. Her primary research focuses on Industrial Biotechnology, Biocatalysis, and Process Intensification, with expertise in Enzyme Immobilisation and Sustainable Process Engineering. She leads projects exploring novel biocatalytic processes, non-conventional reaction media, and the optimization of enzymatic reactions for industrial applications. Her research integrates experimental and computational approaches to enhance enzyme performance in non-aqueous solvents like deep eutectic solvents (DES) and lipid-based systems. Projects include developing photocatalytic synthesis methods, fusion enzymes for enzymatic cascades, and novel hydrogel characterization techniques. She has contributed to advancing light-driven sustainable biocatalysis and improving antimicrobial biomaterials. Key projects include the PHOTOX-f initiative (2025) and PhotoBioCat network (2018-2022), focusing on light-dependent biocatalytic systems and sustainable chemical production. Her work emphasizes industrial applications, environmental impact analysis, and process optimization for scalable biocatalytic solutions. Award-winning educator and researcher, Professor Kara collaborates internationally and holds a prominent role in training early-stage researchers in sustainable biocatalysis through EU-funded networks. Her labs specialize in enzymatic process development, material characterization, and continuous flow bioreactors.

Daniel Hardtمشاهده پروفایل

دانشیار

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +۴ مورد دیگر

Daniel Hardt serves as Associate Professor in the Department of Management, Society and Communication at Copenhagen Business School. His interdisciplinary research bridges computational linguistics, artificial intelligence, and social analysis, with particular focus on natural language processing applications and theoretical linguistic phenomena. His primary research domains include Computational Linguistics (specializing in ellipsis resolution and sluicing phenomena), Natural Language Processing (developing methods for psychographic classification and sentiment analysis), and Artificial Intelligence (examining large language model capabilities and limitations). Recent work analyzes travel behavior during crises, gender effects in evaluations, and GDPR policy comprehension through NLP techniques. His publications span top venues including Linguistic Inquiry , Tourism Management , and ACL proceedings. Hardt actively engages with practical business applications through 27 media contributions discussing AI implementation, ChatGPT transparency, and data-driven leadership strategies. His academic service includes organizing events like the 2019 "Fake News" conference at CBS and presenting at international venues including JSAI 2024. With 28 supervised academic works documented, he maintains substantial mentoring activity while contributing to public discourse on digital transformation challenges.

Matteo Acclavioمشاهده پروفایل

استادیار

• Proof Theory
• Logic in Computer Science
• Concurrency Theory
• +۵ مورد دیگر

Matteo Acclavio is an Assistant Professor in Computer Science at the School of Engineering and Informatics, University of Sussex, affiliated with the Foundations of Software Systems (FoSS) research group. A logician specializing in proof theory and its applications to computer science, his work bridges mathematical logic with concurrency theory and process calculi. Education: PhD in Mathematics, Aix-Marseille University, France Master in Discrete Mathematics and Foundations of Theoretical Computer Science, Aix-Marseille University Master in Mathematics, Roma Tre University, Italy Bachelor in Mathematics, Roma Tre University His research focuses on graphical proof systems, linear logic, modal logic, and concurrency theory. Publications highlight contributions to deep inference, sequent calculus, and the intersection of logic with distributed systems. Recent work explores logical frameworks for concurrency, such as choreographic programming, and graphical models for proof systems. He teaches courses like Operating Systems and maintains active collaborations in theoretical computer science.

Zhanhao Zhangمشاهده پروفایل

پژوهشگر ارشد

• Model Predictive Control
• Process Control
• Time-Delay Systems
• +۳ مورد دیگر

Zhanhao Zhang is a Postdoc researcher at the Department of Applied Mathematics and Computer Science (DTU Compute) of the Technical University of Denmark, affiliated with the Scientific Computing Center for Energy Resources Engineering. His work centers on developing advanced control strategies for industrial processes with emphasis on sustainability and computational efficiency in energy-intensive sectors. His research specializes in Model Predictive Control (MPC) for time-delay systems, numerical discretization methods, and real-time implementation of cyber-physical control systems. Key application domains include zero-emission industrial processes, cement production blending operations, and oil/gas zero-discharge systems, where he bridges theoretical control algorithms with practical industrial constraints through close industry collaboration. Analysis of his 2022-2024 publications reveals consistent innovation in extending MPC methodologies to handle complex industrial realities like time delays and discontinuities while targeting environmental sustainability. His work demonstrates strong integration of computational mathematics with process engineering to solve critical challenges in carbon-intensive industries. Zhang completed his PhD project "Model Predictive Control for Zero Emission Industrial Processes" (2021-2024) under Professor John Bagterp Jørgensen at DTU, resulting in his doctoral thesis and multiple high-impact publications. This industry-collaborative project focused on developing control frameworks for decarbonizing industrial operations. As an active member of DTU's Scientific Computing Center for Energy Resources Engineering, he contributes to interdisciplinary research that applies high-performance computing and mathematical modeling to optimize energy resource extraction, processing, and utilization while reducing environmental impact.

• Distributed Computing
• Data-Centric Systems
• Software Engineering
• +۳ مورد دیگر

Tilman Zuckmantel is a Research Assistant at the Software, Data, People & Society (SDPS) section of the Department of Computer Science , University of Copenhagen. His work focuses on distributed computing and data-centric systems, particularly in asynchronous choreographies and microservices architecture. Recent research outputs include: DACEO (2025): A declarative framework for asynchronous choreographies with event-ordering and object-oriented extensions. Event-based Data-Centric Semantics (2022): A model for consistent data management in microservices environments.

• Image Registration
• Image Segmentation
• Image Classification
• +۶ مورد دیگر

Sune Darkner is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in the Image Analysis, Computational Modelling, and Geometry research section. His work focuses on medical image processing with particular emphasis on neuro-imaging data including MRI and PET scans. His primary research interests include Image Registration, Segmentation and Classification of Medical Image Data , with a specific focus on estimation of image similarity as his main research interest. Darkner strongly believes that the implementation of image processing algorithms should be thoroughly tested and reflect the theoretical properties as accurately as possible. His work primarily centers on neuro-imaging data such as MRI and PET. His recent publications (2024-2025) reveal a strong focus on medical image analysis, with particular emphasis on tumor volume delineation, deformable image registration with physics constraints, and applications of deep learning in medical imaging. His work spans both theoretical foundations of image processing and practical clinical applications. Darkner previously held a Post Doc position at the Technical University of Denmark from February 2009 to January 2010, demonstrating his longstanding engagement with image analysis research in the Danish academic community.

Peter Sestoftمشاهده پروفایل

استاد

• Programming Languages
• Software Development
• Parallel Programming
• +۴ مورد دیگر

Peter Sestoft is a Professor at the IT University of Copenhagen (ITU), leading the Computer Science Department since 2017. His primary roles include academic leadership, research in programming languages and software engineering, and teaching. He holds a PhD in Computer Science from the University of Copenhagen (1991) and has held academic positions at institutions like the Royal Veterinary and Agricultural University and the Technical University of Denmark before joining ITU in 1999. His research focuses on programming languages, functional and managed object-oriented languages, parallel programming, compilers, and spreadsheet implementation technologies. He has developed influential tools like the C5 Generic Collection Library for C# and Moscow ML, a Standard ML implementation. His work on Funcalc and Corecalc advanced spreadsheet technology with user-defined functions and efficient recalculation algorithms. Key contributions include over 30 publications, including books on programming language concepts and Java/C# syntax. He has led major research projects such as 'Popular Parallel Programming' (P3) and 'Probabli' for actuarial calculations. His academic service includes roles on national grant committees and international conference organizing committees. Notable advising includes PhD students like Andrzej Wasowski (ITU Professor) and David Christiansen (Director of Haskell Foundation). His work has been recognized through grants exceeding 25 million DKK and collaborations with institutions like Microsoft Research and Harvard University.

Michael Reichhardt Hansenمشاهده پروفایل

دانشیار

• Formal Methods
• Embedded Systems
• Real-Time Systems
• +۳ مورد دیگر

Michael Reichhardt Hansen is an Associate Professor at the Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark. He holds a MSc in Engineering (1982) and a PhD in Computer Science (1987) from DTU. He has held postdoctoral and visiting roles, including a fellowship at IBM Research Center (1984–1985) and a guest professorship at Oldenburg University (1992–1993). His research focuses on formal methods, embedded systems, and real-time systems, emphasizing mathematically grounded software development. He has authored/co-authored three books and over 70 scientific articles. Research interests include model-based program construction, temporal logic, functional programming, and ontology alignment. He has led projects on ontology matching frameworks (e.g., MDMapper) and formal methods for real-time systems. Hansen has supervised PhD students in areas like data integration and genetic circuit synthesis. He has contributed to international conferences and serves as a reviewer/examiner for academic programs. His work bridges theoretical foundations with industrial applications, particularly in embedded systems and data management. He has collaborated internationally, with projects addressing ESG reporting standards and regulatory compliance through automated data traceability.

Rosario Giustolisiمشاهده پروفایل

دانشیار

• Computer Security
• Cryptographic Protocols
• Decision Systems
• +۷ مورد دیگر

Dr. Rosario Giustolisi is an Associate Professor in the Department of Computer Science at the IT University of Copenhagen . His research centers on computer security with a focus on cryptographic protocols for decision systems (e.g., voting and exams), automated security analysis, accountability frameworks, and sociotechnical security aspects. Before joining ITU, he held postdoctoral roles at SICS RISE and Lund University, Sweden, and earned his PhD from the University of Luxembourg with work on secure exam protocols, culminating in his book Modelling and Verification of Secure Exams (Springer, 2018). Research Trends: His 15 most recent articles (2016–2025) span cryptographic protocol design, coercion-resistant voting/exams, zk-SNARK applications, differential privacy, automated security analysis, and sociotechnical threat modeling. Key subfields include secure decision systems, privacy-preserving mechanisms, and formal verification. Scientific Awards: Best Paper Award, NordSec 2017 Best Paper Award, SECRYPT 2014 Villum Experiment Grant (Sole PI) 2020 ICT TNG Postdoc Grant (Sole PI) 2016 CSC Best PhD Thesis Award 2016 Acknowledgments from Apple for Security Advisory Professional Service: Organized cybersecurity breakfast talks at ITU and served on conference committees for ACM SAC (Security track), STAST, E-VOTE ID, and NordSec. He also contributed as a journal referee and sub-reviewer for multiple conferences.

Ole Madsenمشاهده پروفایل

استاد

• Robotics
• Automation
• Industry 4.0
• +۶ مورد دیگر

Ole Madsen is a Professor at the Department of Materials and Production within The Faculty of Engineering and Science at Aalborg University . His research focuses on Robotics and Automation , particularly in 5G Smart Production , AI for Manufacturing , and Industry 4.0 applications. He also holds a part-time position at Adding Robotics , applying his expertise in robot integration for industrial and healthcare settings. Research Interests : Robotics, Automation, AI, Sensor Systems, Modular Manufacturing, Welding Technology, Digital Twins, Human-Centered Robotics, Industry 4.0 His work spans 35+ years with over 205 publications , emphasizing smart production systems and robot-assisted processes . Key contributions include Swarm Production Architectures , 5G-Enabled Robotics , and Human-Robot Collaboration frameworks. He has supervised 10 PhD students and led major projects like RAU (Robot-Assisted Ultrasound) and GINP: Robotics & AI Innovation Network . Scientific Awards : SCAP2020 Best Presentation Award (2020) Ole's 31 projects include AP2030: Aseptic Factory 2030 (pharma production), AddSmart (robotics R&D), and 5G-Enabled Autonomous Systems . His 127 press/media mentions highlight advancements in robotic welding , industrial metaverse , and swarm production . He maintains an ORCID: 0000-0003-2133-2541 with extensive publication records across Swarm Robotics , Modular Manufacturing , and AI-Driven Production .

Peter C. Petersenمشاهده پروفایل

دانشیار

• Systems Neuroscience
• Computational Neuroscience
• Electrophysiology
• +۳ مورد دیگر

Peter C. Petersen is an Associate Professor in the Department of Neuroscience within the Faculty of Health and Medical Sciences at the University of Copenhagen. Holding a Civilingeniør (MSc) in Technical Physics from DTU and a PhD in Neuroscience, he specializes in systems-level neural mechanisms using electrophysiological approaches. His educational background includes: Civilingeniør (MSc) in Technical Physics, DTU PhD in Neuroscience Petersen's research focuses on neural dynamics in memory and motor systems, combining in vivo electrophysiology with computational modeling. He investigates hippocampal place cells for spatial working memory and rotational dynamics in spinal cord networks, while developing neurotechnology tools like CellExplorer for single-neuron analysis. His work bridges experimental neuroscience, engineering, and data science to decode circuit-level computations. Recent publications (2020-2024) reveal a dual emphasis on hippocampal memory mechanisms (e.g., temperature effects on sharp wave ripples) and innovative methodology (e.g., 3D-printed microdrives). This trajectory demonstrates consistent advancement from tool development to fundamental discoveries in neural coding, with increasing collaboration intensity as evidenced by multi-institutional authorship. Scientific awards: No specific awards were documented in the source material. While explicit advising details are absent, his leadership in software/hardware development (CellExplorer, microdrive systems) implies active mentorship of technical researchers. Grant information isn't specified, though high-impact publications suggest sustained funding for neurotechnology and systems neuroscience projects. Petersen directs the Petersen Lab (https://petersenlab.org/), which employs chronic electrophysiology in rodent models to study memory and movement. The lab maintains strong ties with the Buzsáki lab (hippocampal research) and continues collaborations initiated during his NYU Langone Health tenure (2016-2022), reflecting an integrated approach to neural circuit analysis across institutions.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +۳ مورد دیگر

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.

Henrik Madsenمشاهده پروفایل

استاد

• Energy Systems
• Stochastic Processes
• Demand Response
• +۷ مورد دیگر

Henrik Madsen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His work focuses on energy systems, stochastic processes, and mathematical modeling. He leads research in areas such as demand response in district heating, probabilistic forecasting, and integration of renewable energy sources. Madsen has supervised multiple PhD students and contributes to projects like the IEA DHC Annex TS9 and SEEDS initiative. His expertise includes statistical analysis, time series modeling, and data-driven approaches for energy systems. He actively engages in collaborative research on smart grids, thermal energy storage, and sustainable energy solutions. Education: Academic qualifications from DTU, though specific details are not provided in the text. Research Interests: Mathematical modeling of energy systems Probabilistic forecasting methodologies Integration of wind and solar power Dynamic modeling for district heating Data assimilation in hydrological systems Optimization of energy flexibility Publications & Trends: Recent works emphasize demand response strategies, district heating optimization, and machine learning tools like the nabqr Python package and evalprob4cast R package. His research bridges theoretical stochastic methods with practical applications in energy infrastructure. Grants & Projects: IEA DHC Annex TS9: Digitalization of district heating SEEDS: RES-integrated electrified heating systems Data-Driven Methods for Demand-Side Flexibility Labs/Teams: Collaborates with DTU's Dynamical Systems group and participates in interdisciplinary initiatives like the Frigg 2.0 energy system analysis framework.

Jens Abildskovمشاهده پروفایل

دانشیار

• Thermodynamic Modeling
• Chemical Process Simulation
• Biocatalysis
• +۳ مورد دیگر

Jens Abildskov is an Associate Professor at the Department of Chemical and Biochemical Engineering, Technical University of Denmark (DTU). He is affiliated with the KT Consortium PROSYS - Process and Systems Engineering Centre. His research focuses on thermodynamic properties of fluids, chemical process modeling, and simulation, with applications in biocatalysis, mixed solvents, and separation process optimization. Key areas include CO2 capture, biofuel processing, and energy-efficient separation systems. He collaborates extensively on integrating process modeling with control systems. His work aligns with UN Sustainable Development Goals addressing clean energy and responsible consumption. Recent projects include biogas upgrading technologies and open-source simulation tools like CADET-Julia. He supervises multiple PhD students in areas such as carbon capture, process digitalization, and safety engineering. Abildskov leads the PROSYS Centre, emphasizing process systems engineering. His lab develops models for fluid behavior in non-ideal systems, leveraging statistical mechanical methods and molecular correlation functions. He also explores applications in organic rankine cycles and heat integration for energy savings. Publications emphasize practical solutions for industrial challenges, such as solvent systems for CO2 capture and dynamic feeding strategies in separations. His research bridges theoretical and applied aspects of chemical engineering, with a focus on sustainability and innovation.