جست‌وجوی استاد و پژوهشگر

• Sustainable transport planning
• Urban spatial modeling
• GIS applications in planning
• +۳ مورد دیگر

Professor Yngve Karl Frøyen works at the Department of Architecture and Planning, Faculty of Architecture and Design, Norwegian University of Science and Technology (NTNU). With expertise in sustainable transport solutions, urban spatial modeling, and GIS applications for planning, he has taught and supervised urban and regional planning topics since 2010. His work spans multiple master programs and involves improving transport modeling tools for walking, transit, and bicycling modes. Research Focus: Sustainable urban transport (walking, bicycling, public transit), urban density, GIS methods, land use-transport interactions, and urban logistics. Teaching: Courses in GIS methods, land use-transport integration, regional planning, and master thesis supervision. Publications: 15+ works on transport modeling, urban density impacts, bicycle infrastructure, and planning methodology. Outreach: Regularly presents at conferences and seminars on topics like urban logistics, walking modeling, and transport policy. His academic career spans from 1981 civil engineer graduation to current professorship, with prior roles at Norwegian Institute of Urban and Regional Research (NIBR) and SINTEF. He combines technical GIS proficiency with policy-oriented planning expertise.

Per-Arne Andersenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Reinforcement Learning
• Tsetlin Machines
• +۷ مورد دیگر

Per-Arne Andersen is an Associate Professor at the Department of Information and Communication Technology within the University of Agder . His research focuses on artificial intelligence , reinforcement learning , Tsetlin machines , and deep learning , with applications in real-time strategy games , industrial environments , and IoT systems . Projects: RESTORE Research Groups: CAIR - Center for Artificial Intelligence Research, CIEM - Center for Integrated Crisis Management, Intelligent Mechatronics (iTron) His work explores safe and sustainable reinforcement learning , interpretable AI , and generative environment modeling . He has developed frameworks like CaiRL and CostNet for high-performance RL environments and goal-directed learning. Recent publications include advancements in Tsetlin automaton analysis , GNSS jamming classification , and road quality detection . Articles from 2025-2016 span machine learning , computer vision , and environmental modeling . He contributes to IEEE , Springer , and LNCS publications, with a focus on interdisciplinary AI applications in crisis management , cybersecurity , and industrial optimization .

Eduard Kamburjanمشاهده پروفایل

پژوهشگر

• Digital Twins
• Formal Methods
• Hybrid Systems Verification
• +۵ مورد دیگر

Eduard Kamburjan is a Researcher at the University of Oslo , affiliated with the Reliable Systems (PSY) and Data and Knowledge Systems (DKM) research groups. His work bridges formal methods , digital twin engineering , and knowledge graph applications . Research interests include: Formal verification of hybrid systems using deductive methods Digital twin architecture with compositional correctness guarantees Semantic lifting and ontology-driven modeling for complex systems Concurrency analysis and non-determinism in program verification Interactive visualization as serious games for formal methods His 2024-2023 publications demonstrate expertise in digital twin reconfiguration , semantic interoperability , and knowledge-based runtime enforcement . Key contributions include Crowbar for active object verification and ABS simulator toolchain for model-driven engineering. Collaborations span institutions like Springer , ACM , and IEEE , with work featured in Lecture Notes in Computer Science (LNCS) , Software and Systems Modeling (SoSyM) , and Science of Computer Programming . His research integrates RDF data management , behavioral contracts , and modular analysis for distributed systems.

• Scientific Computing
• Numerical Analysis
• Applied Mathematics
• +۵ مورد دیگر

Eirik Valseth is an Associate Professor of Scientific Computing at the Norwegian University of Life Sciences (NMBU), Department of Data Science. He holds concurrent roles as a research associate at the Oden Institute, University of Texas at Austin, and an affiliated researcher at Simula Research Laboratory (Department of Numerical Analysis and Scientific Computing). His expertise lies in advanced finite element methods for PDEs with applications in flood modeling and hydropower systems. Current Affiliation: NMBU (Norwegian University of Life Sciences) Secondary Affiliations: Oden Institute (UT Austin), Simula Research Laboratory Research interests span numerical methods for challenging PDE systems, including: Stabilized finite element formulations Hurricane storm surge and riverine flood modeling Hydropower infrastructure analysis Computational mechanics and applied mathematics His recent publications (2024–2025) emphasize flood risk assessment (compound flooding, dam breaks, dredging impacts), advanced numerical methods (isogeometric analysis, stochastic finite elements, graph-grammar algorithms), and environmental applications (pollution transport, pathogen distribution, mosquito population dynamics after hurricanes). Key trends include cross-disciplinary integration of physics-aware machine learning and robust hydrodynamic simulation tools. Valseth's work extends to software development (e.g., WAVEx for spectral wave models, SWEMniCS for coastal circulation) and large-scale modeling frameworks like the ADCIRC unstructured mesh model for US coasts. Collaborative projects involve institutions such as University of Texas at Austin, Simula, and NOAA.

Tønnes Nygaardمشاهده پروفایل

دانشیار

• Robotics
• Evolutionary Algorithms
• Morphological Adaptation
• +۳ مورد دیگر

Tønnes Nygaard is an Associate Professor at the Department of Technology Systems, University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. His research focuses on evolutionary robotics, morphological adaptation, and embodied artificial intelligence. He leads projects like COCOMO (Co-evolution of Control and Morphologies) and works extensively with the DyRET (Dynamic Robot for Embodied Testing) platform. Key research interests include robot control systems, adaptive morphology design, and real-world implementation of evolutionary algorithms. His work bridges theoretical computer science with practical robotics applications, emphasizing hardware-software co-evolution and embodied cognition principles. Publications span topics like morphological adaptation in quadruped robots, semi-supervised learning for terrain classification, and overcoming convergence issues in multi-objective evolutionary algorithms. Nygaard collaborates internationally and contributes to both academic journals and conferences in robotics and AI. No scientific awards are explicitly listed, though his impactful contributions to real-world evolutionary robotics suggest potential recognition pending explicit mentions. Advising and grant activities are central to his role, though specific student names or grant amounts are not detailed in the provided texts. Labs/Teams: Core contributor to the DyRET project and affiliated with the Section for Autonomous Systems and Sensor Technologies at UiO.

• Biomedical Instrumentation
• Medical Technology
• Clinical Engineering
• +۳ مورد دیگر

Jan Olav Høgetveit is an Associate Professor in the Department of Physics at the University of Oslo (UiO), within the Faculty of Mathematics and Natural Sciences. He also serves as Head of Research & Development in the Department of Biomedical and Clinical Engineering at Rikshospitalet, Norway’s national hospital. His work bridges physics and clinical practice, focusing on medical instrumentation. Education: Bachelor of Electronic Engineering (Technical Cybernetics), Oslo University College, 1993 Master of Electronics, University of Oslo (Department of Physics), 1997 Ph.D. in Physics (Technology Applications for Medical Devices), University of Oslo, 2008 Research Interests: Høgetveit specializes in biomedical instrumentation and clinical engineering, particularly in surgical technology and wireless communication impacts on medical devices. His work addresses challenges like real-time physiological monitoring during heart-lung machine use, non-invasive blood glucose detection, and bioimpedance-based viability assessment of organs. He emphasizes interdisciplinary collaboration between engineering and medicine to enhance patient safety and surgical outcomes. Scientific Contributions: His research trends span bioimpedance applications in ischemia/reperfusion injury, machine learning for surgical decision support, and electrosurgery safety. He has explored ventilator optimization during pandemics and implant-related thermal risks. Contributions highlight both hardware development (e.g., optically isolated current sources) and software innovations (e.g., neural networks for viability prediction). Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: Høgetveit received a 1998–2001 research council scholarship. As Head of R&D since 2001, he oversees translational projects. No formal advisees/students listed, though he collaborates extensively with teams on device development and clinical trials. Labs & Teams: Affiliated with UiO’s Department of Physics and Rikshospitalet’s Biomedical and Clinical Engineering department. Active in the Electronics research group at UiO. Engages with multidisciplinary teams addressing surgical instrumentation and physiological monitoring challenges.

• Analog CMOS Design
• Circuit Design Automation
• Integrated Circuit Compilers
• +۲ مورد دیگر

Carsten Wulff is Associate Professor at NTNU's Department of Electronic Systems, focusing on analog CMOS design automation and circuit compilers. Research: Develops software tools for automated analog IC design, including ciccreator—an open-source compiler for generating custom integrated circuits. Aims to simplify analog design workflows through programmable frameworks. Teaching: Leads courses in Advanced Integrated Circuits (AIC) with public lecture notes and YouTube resources. Current focus includes Python-based circuit design tools.

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +۴ مورد دیگر

Özlem Özgöbek is an Associate Professor at the Department of Computer Technology and Informatics, Norwegian University of Science and Technology (NTNU). Her research spans artificial intelligence, machine learning, and recommender systems with a focus on privacy, fake news detection, and educational technology. NTNU - Department of Computer Technology and Informatics Her work explores multimodal fake news detection, privacy implications in recommender systems, and technology-enhanced classroom interaction. Recent publications analyze digital education trends and classroom tools. Özgöbek collaborates with international researchers and contributes to news recommendation workshops. Her projects address ethical AI, environmental sustainability, and real-time information processing.

• Internet of Things
• Fog Computing
• Edge Computing
• +۹ مورد دیگر

Amirhosein Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, dependability, mobility and data-intensiveness of distributed systems for emerging computing technologies including Internet of Things (IoT), Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). Dr. Taherkordi received his Ph.D. from the Informatics Department at the University of Oslo under the supervision of Prof. Frank Eliassen, with his thesis titled "Programming Wireless Sensor Networks: From Static to Adaptive Models." He holds an M.Sc. in Information Technology Engineering (Software Engineering) from University of Science and Technology and a B.Sc. in Computer Engineering from Sharif University of Technology. His research spans multiple domains of distributed systems with emphasis on practical applications. He investigates energy efficiency in wireless sensor networks, communication optimization in IoT systems, and adaptive resource allocation in edge computing environments. His work addresses critical challenges in network traffic classification, federated learning for vehicular networks, and data processing across heterogeneous platforms. Analysis of his recent publications reveals a strong trajectory toward communication-efficient federated learning techniques for vehicular networks, energy-aware protocols for IoT data collection, and advanced machine learning approaches for network traffic analysis. His research consistently focuses on optimizing resource usage while maintaining system performance and privacy in distributed architectures. Dr. Taherkordi actively contributes to several research initiatives including the CPS Lab at UiO for Cyber Physical Systems, DILUTE: Fluid Service Abstraction for Large-Scale Cloud IoT Systems, and the Gemini Centre on IoT at UiO. His work bridges theoretical advances with practical implementations in transportation systems, environmental monitoring, and industrial automation.

• Computer Architecture
• Microarchitecture
• Memory Systems
• +۴ مورد دیگر

Rakesh Kumar is an Associate Professor in the Department of Computer Science (IDI) at the Norwegian University of Science and Technology (NTNU) , affiliated with the Computer Architecture Lab (CAL) within the Faculty of Information Technology and Electrical Engineering . Prior to joining NTNU, he held postdoctoral and research associate positions at Uppsala University and the University of Edinburgh, and interned at Intel Barcelona Research Center. Research Interests include improving large-scale datacenter efficiency through microarchitecture and memory system optimizations, hardware/software co-designed processors, dynamic code translation, vectorization, and serverless function execution. His work explores ready-aware instruction scheduling, branch prediction organization, and address translation mechanisms. Scientific Contributions span publications at top-tier conferences like MICRO (2024, 2023, 2018, 2016) HPCA (2020, 2019, 2023, 2022) ASPLOS (2018) DATE (2019, 2021) Journal articles appear in ACM Transactions on Computer Systems and IEEE Computer Architecture Letters . Awards include Intel Spontaneous Level II/Excellence Award (2014) Best Presentation Award at HiPC-SS08 (2008) Best Paper Award at National Conference on High Computing Technologies (2008) Distinguished Artifact Award at MICRO 2023 PhD Supervision involves advising students like Roman Kaspar Brunner, Elias Orrem, and Truls Asheim on topics spanning microarchitecture, vector units, and runahead execution policies.

• Reservoir Engineering
• CO2 Enhanced Oil Recovery & Storage
• Data-Driven Reservoir Modeling
• +۵ مورد دیگر

Ashkan Jahanbani Ghahfarokhi is an Associate Professor in Reservoir Engineering at NTNU’s Department of Geosciences since 2019. He leads the CEORS Gemini Centre (CO2 Enhanced Oil Recovery & Storage) and is a core member of the BRU21 Reservoir Management group. His research focuses on data-driven subsurface modeling, CO2 storage, and optimization of oil recovery processes. Education: PhD, NTNU (2015); MEng, University of Calgary (2009); MSc/BSc, Petroleum University of Technology, Iran (2006–2009). Roles: Head of Reservoir Engineering Group (2021–2022), Project Manager for NORHED II (2022), and NTNU’s Outstanding Academic Fellow (2022–2026). Research interests include CO2-EOR/Storage, machine learning applications, proxy modeling, and reservoir simulation. He has supervised 20+ students and published over 40 journal/conference papers. Awards include DNVA postdoc scholarship (2015–2017) and PoreLab postdoc (wettability studies). Teaching: Masters/PhD courses in reservoir fluids, simulation, and geomechanics. Guest editor for Energies (AI in Oil & Gas).

Anne Danielsenمشاهده پروفایل

استاد

• Popular Music
• African-American Music
• Rhythm and Sound Analysis
• +۵ مورد دیگر

Anne Danielsen is a Professor and Deputy Head at the RITMO Center for Interdisciplinary Studies of Rhythm, Time, and Motion at the University of Oslo. Her research bridges musicology, cognitive science, and interdisciplinary studies, focusing on rhythm, music production, and the intersection of music, media, and technology. University: University of Oslo Role: Professor Projects: TIME (NFR TOP RESEARCH), RITMO (NFR Center of Excellence) Danielsen’s work explores microrhythm , groove , and temporal perception in music, with a particular emphasis on popular and African-American music . She investigates how music cognition interacts with sonic features and body posture in performance, integrating psychological and acoustic analysis . Her recent projects include MusicLab Copenhagen , a dataset for interdisciplinary concert research, and studies on beta oscillations and pupil responses in groove perception. Danielsen collaborates across disciplines, examining how personality traits and genetic factors influence musical sensibility through twin studies. In music production , she analyzes how digital audio workstations transform rhythmic structures and investigates acoustic chamber design for sound experiments. Her research also addresses gender patterns in music mediation and the cultural implications of rhythmic aesthetics.

• Distributed Systems
• Cybersecurity
• Sports Technology
• +۷ مورد دیگر

Dag Johansen is a Professor in the Department of Informatics at UiT The Arctic University of Norway, Tromso campus. His work spans multiple research areas at the intersection of computer science, sports science, medicine, health technology, and nutrition science. He leads the interdisciplinary "Corpore Sano" research center and is actively involved in several research groups including the Cyber Security Group (CSG) and Crime Control and Security Law. Professor Johansen's research focuses on developing fundamental software solutions for secure and error-free data processing in heterogeneous distributed systems, ranging from lightweight "Internet of Things" devices and mobile phones to large-scale cloud solutions. His work particularly emphasizes applications in sports technology, edge computing, and compliance technology. His research interests include distributed systems, cybersecurity, sports technology, edge computing, data privacy, AI for sports analytics, multimedia forensics, and compliance technology. His recent publication trends show a strong focus on AI applications for sports video analysis, particularly in soccer and ice hockey, where his team has developed AI-based cropping systems for social media representations. He also has significant work in data privacy and GDPR compliance, especially regarding the "third country problem," as well as applications of AI in sustainable fishing practices. His 2024-2025 publications demonstrate continued work in self-healing microservices, lightweight encryption for video feeds, and virtual reality training environments. Professor Johansen is actively involved in mentoring students and research collaborators, as evidenced by his extensive publication record with numerous co-authors including doctoral students and postdoctoral researchers. His work has received funding through various research projects focused on data analytics, privacy technology, cybersecurity, and sports technology applications. He leads the interdisciplinary "Corpore Sano" center, which brings together researchers from computer science, sports science, medicine, health technology, and nutrition science. His work also involves collaboration with the "Njord" project focused on sustainable fishing through AI applications, and he's involved in developing the "Áika" distributed edge system for AI inference.

Milica Orlandicمشاهده پروفایل

دانشیار

• Hyperspectral Imaging
• Remote Sensing
• Digital Design
• +۷ مورد دیگر

Milica Orlandic is an Associate Professor in the Department of Electronic Systems at NTNU. She holds an MSc from the University of Montenegro (2009) and a PhD from NTNU (2015). Her research focuses on hyperspectral imaging, remote sensing, FPGA-based systems, and embedded computing for aerospace applications. She is actively involved in the HYPSO CubeSat mission, developing onboard processing systems for Earth observation. Education: MSc in Electrical Engineering, University of Montenegro (2009) PhD in Electronics, NTNU (2015) Research Interests: Her work spans hyperspectral data processing , including compression, anomaly detection, and onboard computing for satellites. She also explores reconfigurable hardware (FPGAs) for real-time signal processing, cyber-physical systems, and spaceborne sensor systems. Publications Trends: Recent work emphasizes lightweight machine learning for anomaly detection, FPGA acceleration of hyperspectral compression (CCSDS 123), and algorithm co-design for CubeSat missions. Key contributions include robust onboard processing frameworks for HYPSO-1 and adaptive hardware-software systems. Advising & Teams: She supervises a dynamic team of over 40 PhD and MSc students working on FPGA implementations, satellite systems, and hyperspectral algorithms. Notable collaborations include the HYPSO CubeSat project, which aims to deliver high-resolution Earth observation data with low latency. Labs & Infrastructure: Her research leverages NTNU’s facilities for embedded systems prototyping, FPGA development, and CubeSat payload testing. The HYPSO mission integrates her team’s hardware-software co-design innovations for space applications.

• Serious Games
• Healthcare Technology
• Gamification
• +۴ مورد دیگر

Simon McCallum is an Associate Professor at NTNU’s Faculty of Information Technology and Electrical Engineering. Born in New Zealand, he transitioned from commercial game development in Norway to academia in 2009. His research focuses on Serious Games, particularly in healthcare contexts, gamification, and virtual reality applications. He holds a PhD from the University of Otago (2007), where he studied Computer Science, Mathematics, Psychology, and Philosophy. McCallum has contributed to over 30 publications, including works on cognitive health gaming, software engineering education, and AR applications. He teaches courses such as IMT4307 (Serious Games Research) and IMT3603 (Game Programming). His key projects include the Smartkuber AR game for cognitive screening and the Traction Trebuchet historical engineering study. McCallum has collaborated with institutions like the University of Otago and has engaged in outreach through conferences and media appearances, promoting game technology in healthcare and education.