Fredrik Manne is a Professor at the Department of Informatics, University of Bergen, Norway. His work focuses on parallel and distributed computing, particularly in combinatorial scientific computing and self-stabilizing algorithms. He has contributed extensively to graph algorithms, including matching, coloring, and clustering, with applications in high-performance computing, numerical optimization, and wireless networks. His research involves designing algorithms for parallel architectures, including multi-core and GPU-based systems. He has co-authored numerous publications on topics such as spanning forests, vertex cover heuristics, and b-matching. His work often integrates theoretical and applied approaches, addressing challenges in sparse matrix computations and wireless mesh network communication. The recent articles highlight his exploration of graph neural networks for parallel coloring, efficient multithreaded matching algorithms, and GPU-accelerated clustering methods. These studies emphasize scalability, optimization, and real-world applications in computational science.
Professor Talal Rahman is a faculty member at the Western Norway University of Applied Sciences, where he works in the Department of Computer Science, Electrical Engineering and Mathematical Sciences. His office is located at Bergen KRONSTAD D305, and he can be reached at phone number +47 55 58 72 46. Professor Rahman's research spans several key areas in computational mathematics and scientific computing. His primary research interests include: Scientific Computing Numerical Analysis Numerical Methods for Partial Differential Equations Preconditioning Finite Element with Domain Decomposition Methods Variational Image Processing Artificial Intelligence and Machine Learning applications Professor Rahman's extensive publication record demonstrates a strong focus on domain decomposition methods, particularly Schwarz methods and their applications to multiscale problems. His recent work shows an increasing integration of machine learning techniques with traditional numerical methods, as evidenced by publications on neural network applications for environmental modeling and capelin migration patterns. His research also extends to biomedical applications, including computational analysis of biodegradable materials and bone tissue engineering scaffolds. He has made significant contributions to the development of adaptive preconditioners and parallel algorithms for solving complex numerical problems, with his work on the TV-Stokes model for image processing representing an important contribution to the field of variational image processing. Professor Rahman has supervised numerous research projects and students, though specific student names are not provided in the available information. His research appears to be supported by grants related to computational science and engineering, though specific grant details are not mentioned in the provided text. Based on his research areas, Professor Rahman likely collaborates with various research groups focused on computational science, with potential connections to biomedical engineering labs and environmental research teams studying the Barents Sea ecosystem.
André Brodtkorb is a Professor and Head of the Department of Information Technology at Oslo Metropolitan University. His research spans applied mathematics, numerical analysis, and computational science, focusing on physics simulations and GPU computing. He advocates for open and reproducible research and is actively involved in education and societal engagement through the Academy of Young Researchers (2024-2028). Research Interests: His work integrates applied mathematics and computer science to develop high-performance simulations for environmental phenomena, including ocean currents, volcanic ash dispersion, and coastal flooding. He specializes in GPU-accelerated parallel computing, finite-volume methods, and Python-based scientific programming. Publication Trends: Recent articles highlight advancements in GPU computing efficiency, ocean modeling, and inverse ash transport modeling for volcanic plume forecasting. His research bridges computational methods with real-world environmental challenges. Scientific Awards: Member of the Academy of Young Researchers (2024-2028) Contact Information: Office: Pilestredet 35, 0166 Oslo Phone: +47 456 19 070 (Mobile), +47 672 35 924 (Office) Email: andre.brodtkorb@oslomet.no
Sukalpa Chanda is an Associate Professor at the Department of Computer Science and Communication, Halden University College. His research focuses on Machine Learning with applications to Document Image Analysis, Computer Vision, and Video Image Analysis, including advanced methods like Zero-Shot Learning, Deep Learning, and Transformer Networks. PhD in Computer Science from NTNU Appointments: Postdoctoral Researcher at Uppsala University (2018-2019) and Groningen University (2016-2018) Research Interests: Chanda specializes in Zero-Shot and One-Shot Learning for document and image analysis, with applications in handwriting recognition, face generation, and biomedical imaging. His work bridges theoretical machine learning with practical implementations in cultural heritage preservation and healthcare diagnostics. Scientific Collaboration: He collaborates with institutions like Indian Institute of Technology (Pallakad/Patna) and leads the Hugin Munin Project under The Digital Society research priority area. His team includes Master’s students and research assistants working on Transformer Networks and generative models. Key Publications: Recent works include frameworks for zero-shot action recognition (T2L, 2025), Nordic manuscript writer identification (2023), and advanced medical image segmentation networks (PAANet, 2021). His research spans document analysis, deep metric learning, and biomedical applications.
Amirhosein Taherkordi is an Associate Professor at the Department of Information Security and Communication Technology within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). His academic profile shows continuous research activity with publications spanning from 2011 through 2025, indicating an established career trajectory in computer science and networking research. Dr. Taherkordi's research interests focus on addressing fundamental challenges in distributed computing environments, particularly in resource-constrained scenarios. His work spans Internet of Things (IoT) systems, edge and fog computing architectures, network security protocols, and machine learning applications for network traffic analysis. He has made significant contributions to energy-efficient data collection protocols for wireless sensor networks, privacy-preserving techniques for industrial IoT systems, and communication-efficient approaches for federated learning in vehicular networks. His research consistently bridges theoretical innovation with practical implementation, addressing real-world challenges in smart transportation, environmental monitoring, and industrial automation systems. An analysis of Dr. Taherkordi's recent publication trends (2023-2025) reveals a strong emphasis on federated learning applications for vehicular networks (FedAGL, FedAPT), energy-efficient IoT data collection strategies (eU2U, ECMSH), and the integration of transfer learning with edge computing for transportation applications (TELEGAIT, FOGFLEET). His work increasingly addresses the critical tension between computational efficiency and accuracy in distributed systems, with growing applications in environmental monitoring (PmForecast) and circular economy frameworks. The interdisciplinary nature of his research spans computer science, electrical engineering, and environmental science domains. Dr. Taherkordi maintains an active collaborative research profile, working with international colleagues across multiple institutions as evidenced by his diverse publication venues including IEEE Transactions, ACM journals, and various conference proceedings. His research program appears to be well-established with consistent funding, though specific grant details aren't provided in the available text. He likely leads or contributes significantly to research groups focused on networking, IoT, and edge computing at NTNU, mentoring students in these emerging technology domains.
Silvia Lizeth Tapia Tarifa is an Associate Professor in the Department of Informatics at the University of Oslo, specializing in formal methods for parallel and distributed systems. She serves as one of the principal investigators for the NFR SJM (Smart Journey Mining) project, which runs until 2026, and actively participates in Digital Twins research with a focus on GDPR-compliant data management. Her academic affiliations include the Reliable Systems research group and the Analytical Systems and Reasoning (ASR) group at the Department of Informatics. Professor Tapia Tarifa's research spans formal methods, concurrency theory, and distributed systems with particular emphasis on self-adaptive systems, semantics of concurrent languages, compositional reasoning about distributed system behavior, and formal modeling of resource usage. Her work bridges theoretical computer science with practical applications in digital twins, GDPR compliance, and resource management in distributed environments. She has made significant contributions to the ABS language framework and active object models for parallel and distributed computing. Her publication record shows a consistent focus on formal verification techniques applied to emerging challenges in distributed computing. Recent work demonstrates increasing attention to digital twins technology, user journey modeling, and privacy-preserving systems. The research trajectory reveals evolution from foundational work on concurrent language semantics toward applied research in self-adaptive systems and GDPR-compliant architectures, while maintaining strong theoretical underpinnings in formal methods. Young Research Talent grant from Research Council of Norway (2017), the only computer science grant in that call Fellow at United Nations University, International Institute for Software Technology (2007) Active participation in formal methods community as general chair, PC chair, and committee member Professor Tapia Tarifa has supervised PhD and master's students while teaching graduate-level courses. She has led significant research initiatives including the Analysis and Complex System Research Program at SIRIUS Center (ended 2023) and the EU MSCA-ITN REMARO project on Reliable AI for Marine Robotics (ended 2024). Her current research portfolio includes multiple active grants focused on digital twins, user journey analysis, and privacy-preserving distributed systems. She collaborates extensively with researchers across Europe through various EU-funded projects including FP7 ENVISAGE, FP7 FET UpScale, and FP7 FET HATS. Her research activities are centered around the ABS language framework and its applications to distributed systems verification. She maintains active collaborations through the SIRIUS Center and participates in the international formal methods community through conference organization and program committees.
Anne C. Elster is a Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. She is the founder and director of the HPC-Lab, a leading research group in heterogeneous and parallel computing. She also maintains a long-standing affiliation with the Oden Institute at the University of Texas at Austin as a Senior Visiting Scientist until Summer 2025. Research Interests: Her work spans high-performance computing (HPC), GPU computing, parallel algorithms, auto-tuning, performance optimization, and machine learning applications in scientific computing. She leads research in heterogeneous architectures and has contributed significantly to compiler and runtime systems for GPUs and accelerators. Publications Trends: Her recent publications (2021–2024) focus on GPU acceleration, auto-tuning frameworks (e.g., BAT, LS-CAT), performance modeling (Roofline), machine learning integration in HPC, and applications in geophysical and scientific computing. There is a strong emphasis on empirical evaluation, benchmarking, and practical optimization techniques. Scientific Awards and Recognition: IEEE Senior Member (2000) IEEE Computer Society Distinguished Contributor Charter member, NTNU's Board (2021) Distinguished Speaker, IEEE Computer Society (2019–2022) Advising and Grants: She has advised over 100 master’s students and several PhD students. She has led major funded projects including the RCN SFI Centre for Geophysical Forecasting, EU H2020 CloudLightning and TICOH, and NFR FRINATEK on Computational Microscopy. She has served on numerous international program committees and evaluation boards. Labs and Teams: She leads the HPC-Lab at NTNU, which includes postdocs, PhDs, and master’s students, and collaborates with international researchers. The lab is a hub for innovation in GPU computing, auto-tuning, and HPC applications.
Alexander Wold is an Associate Professor at the University of Oslo, affiliated with the Research Group for Robotics and Intelligent Systems within the Faculty of Mathematics and Natural Sciences. His work focuses on reconfigurable computing, embedded systems, and robotics, with notable contributions to FPGA design, real-time systems, and educational technology. He holds a position at the Institute of Informatics (IFI) and can be contacted at alexawo@ifi.uio.no . Research interests include optimizing hardware-software co-design, thermal management in 3D-IC systems, and developing open-source tools like EasyPR for pattern recognition. His publications span topics such as remote cloud labs for reconfigurable logic education, network traffic management in industrial Ethernet, and constraint programming for module placement in FPGAs. Dr. Wold’s articles reflect a strong emphasis on practical applications of robotics and intelligent systems, with a focus on safety-critical industrial systems and autonomic computing. He has contributed to multi-core system design, thermal-aware FPGA architectures, and self-aware systems.
Iver Brynild Neumann is a Professor II at the Faculty of Social Sciences , University of Agder. His work bridges international relations, political sociology, and diplomatic theory, focusing on historical and non-sedentarist approaches to global politics. Research Themes : International political sociology, state formation, nomadic military prowess, small state agency, and visual/ethnographic diplomatic methods. Key Publications : Contributions to the Oxford Handbook of International Political Sociology , analyses of Bronze Age inter-polity systems, and critiques of Eurocentrism in diplomatic studies. Recent Focus : The implications of the New Strategic Situation for Norway, the role of prehistoric systems in international relations, and the interplay of gifts and power in diplomatic exchanges. Notable Collaborations : Co-authored works with Einar Wigen, Håkon Glørstad, and Ole Jacob Sending, exploring nomadic traditions and historical continuities in Eurasian geopolitics. Neumann’s articles emphasize interdisciplinary methodologies, including discourse analysis, ethnography, and visual semiotics, challenging conventional frameworks in international relations.
Malin Johansson is an Associate Professor and Group Leader of the Earth Observation group at the Department of Physics and Technology, UiT The Arctic University of Norway . Her research connects remote sensing with numerical modeling and in-situ data to advance understanding of Arctic sea ice properties , environmental monitoring (oil spills, harmful algae blooms), and multi-frequency SAR observations . She teaches courses like FYS-1009 (Climate and Environmental Monitoring), FYS-3001 (Physics of Remote Sensing), and FYS-3023 (Applied Remote Sensing). Research Focus : Arctic sea ice dynamics using SAR and optical imagery Environmental risk assessment (oil spills, algae blooms) Climate-ice-ocean interactions Machine learning for SAR data analysis Article Trends : Her recent work emphasizes multi-decadal SAR analysis of Arctic sea ice types, Lagrangian drift prediction for environmental hazards, cross-polar comparisons of ice classification methods, and incidence angle modeling for SAR calibration. Students : Truls Karlsen (Multi-Frequency Sea Ice Observations) Jakub Petříček (Permafrost Remote Sensing) Labs/Teams : Leads the Earth Observation group at UiT, collaborating with international institutions on projects like INTERAAC (Norway-China) and MOSAiC expeditions.
Maben Rabi is a Professor at the Department of Information Technology and Communication, University of Southeast Norway. His work focuses on cyber-physical systems, networked control, and hybrid systems with applications in intelligent transportation and robotics. Current projects: CriSp (Research Council) and SafeSmart (KK Foundation). Research keywords: Cyber-Physical Systems, Networked Control, Hybrid Systems, Digital Society, DigiTech. Research Trends : His recent publications emphasize road friction estimation for autonomous vehicles, nonlinear control in relay feedback systems, probabilistic sampling for networked estimation, and packet loss modeling in vehicular networks. These works bridge control theory, cyber-physical systems, and transportation safety. Teaching : Courses include Technology Project (ITD25018) and Digital Control and Cyber-Physical Systems (ITD30019) .
Phuong H. Ha is a Professor at the Department of Computer Science, The Arctic University of Norway (UiT), located in Tromsø, Norway. He leads the Arctic Green Computing Group and is affiliated with the Faculty of Science and Technology. His research focuses on energy-efficient computing systems, machine learning, parallel programming, and cyber-physical systems. He holds a Ph.D. from Chalmers University of Technology (Sweden). Key roles include leading EU-funded projects such as EXCESS (FP7) and TAILOR (H2020), as well as national projects like PREAPP and eX3. He has pioneered research in energy informatics, edge intelligence, and parallel programming systems. His work emphasizes sustainable energy solutions and efficient resource management in distributed systems. Teaching responsibilities include courses on Green Computing, Operating Systems, and Parallel Programming. He has developed innovative frameworks for energy harvesting in wireless devices and contributed to Arctic observational systems through projects like the Arctic Observatory (DAO). Research highlights include over 30 peer-reviewed publications in top venues like IEEE Transactions on Parallel and Distributed Systems and Journal of Parallel and Distributed Computing. His work bridges theoretical foundations with practical applications in smart grids, IoT, and environmental monitoring.
Sufayan Ikabal Mulani is a Doctoral Research Fellow at the University of Oslo, affiliated with the Research Group for Digital Signal Processing and Image Analysis within the Faculty of Mathematics and Natural Sciences. His email is sufayanm@ifi.uio.no . Research interests focus on advanced imaging techniques, including digital signal processing and photoacoustic imaging. His work emphasizes improving imaging quality through novel algorithms like coherence factor beamforming and real-time signal processing. Recent contributions address challenges in LED-based photoacoustic systems and interference mitigation in microscopy. No formal student advisees or awards are listed. His affiliation with the research group highlights expertise in biomedical imaging and engineering applications.
Prof. Anne C. Elster is a Professor of Computer Science at NTNU's Department of Computer & Information Science (IDI), leading the HPC-Lab. She specializes in High-Performance Computing (HPC), GPU acceleration, and heterogeneous systems. Her work spans HPC applications in medical imaging, seismic processing, and oil & gas simulations, with collaborations at CERN, NVIDIA, and Schlumberger. Elster holds a PhD in Electrical Engineering from Cornell University (1994) and is an IEEE Senior Member since 2000. She has supervised over 70 master students and numerous PhD candidates, emphasizing GPU computing. Her teaching includes courses like Parallel Computing and Compilers , with a focus on programming and problem-based learning. She leads EU projects like CLOUDLIGHTNING (2015–2018) and has organized major conferences (e.g., ISC, SC, PARA). Her HPC-Lab is a CUDA Research and Teaching Center, and she advocates for HPC infrastructure investments in Norway through policy engagement.
Jørgen Røysland Aarnes is a Research Fellow at the Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU). His research spans fluid dynamics, computational methods, and philosophy of science, with particular focus on turbulence, multiphase flows, and structural realism in Ernst Cassirer's philosophy. He completed his PhD in 2018 with a thesis on particle-laden flows impinging on cylinders. Research Interests: Computational fluid dynamics (CFD), including high-order methods and overset grids Turbulent flows and free-surface vortex structures Particle-laden flow dynamics and impaction mechanisms Philosophy of science, structural realism, and symbolic forms Recent Contributions: His 2025 work on free-surface vortex patterns and 2024 philosophical papers on Cassirer's symbolic forms demonstrate interdisciplinary expertise. He has developed computational tools like the Pencil Code for fluid simulations. Awards: No scientific awards explicitly listed, though his work has been presented at major conferences including the European Turbulence Conference and European Geosciences Union meetings. Advising & Grants: No explicit grants or advisees listed, though his PhD supervision experience is implied through his postdoctoral role. Labs/Teams: Collaborates with the Pencil Code Collaboration and NTNU's energy engineering research groups.
