Mahmoud Sayed Mahmoud Eid is a PhD Research Fellow in Engineering Sciences at the University of Agder. His research focuses on fault diagnosis in electric powertrains using AI-driven methods. He holds a B.S. in Mechatronics Engineering from Helwan University (2013) and an M.S. in Advanced Mechanical and Robotics Engineering from Ritsumeikan University, Japan (2019). Research Focus: Develops computational models for early fault detection in electric motors and inverters under noisy operational conditions. His methodologies integrate electromagnetic theory with machine learning for industrial applications in transportation and energy systems. Publications: His 6 most recent articles (2022-2025) focus on AI-enhanced diagnostics for power electronics and motor systems, demonstrating consistent innovation in signal processing and classifier design for industrial reliability.
Narada Dilp Warakagoda is an Associate Professor at the University of Oslo, affiliated with the Section for Autonomous Systems and Sensor Technologies. His research focuses on Artificial Intelligence, Autonomous Systems, Computer Vision, Robotics, Machine Learning, and Deep Learning, with notable contributions to underwater acoustics, renewable energy forecasting, and unmanned vehicle control systems. He collaborates extensively with researchers across disciplines to advance applications in environmental monitoring, energy systems, and autonomous technologies. His work integrates advanced machine learning techniques with domain-specific challenges, such as synthetic aperture sonar analysis, spatio-temporal wind forecasting, and reinforcement learning for autonomous navigation. He has published in leading journals and conferences, addressing topics like probabilistic solar irradiance prediction, graph-based wind energy modeling, and deep learning for seabed munitions detection. Warakagoda’s research bridges theoretical advancements with practical implementations, emphasizing applications in robotics, environmental science, and defense technologies. His projects often involve interdisciplinary teams, reflecting his commitment to solving real-world problems through innovative AI-driven solutions.
Christian Fieseler is a Professor at the Department of Communication and Culture at BI Norwegian Business School and founder of the Nordic Centre for Internet and Society. He holds a PhD in Management and Economics from the University of St. Gallen (2008), followed by postdoctoral research at Harvard University’s Berkman Center and Stanford University. His expertise spans digital transformation, corporate social responsibility, and social media’s impact on organizational identity. Fieseler’s research investigates how individuals and organizations adapt to digital disruptions, particularly in gig economies and algorithmic labor markets. Education: PhD in Management and Economics (University of St. Gallen, 2008) Postdoctoral Research: Harvard University (Berkman Center), Stanford University Research Focus: Algorithmic management and labor exploitation in gig economies Artificial intelligence ethics and governance Social media’s role in corporate social responsibility Identity construction in digital labor platforms Key Contributions: Over 40 peer-reviewed articles, including influential works on algorithmic paranoia, NFT-driven creative labor, and deliberative governance in AI. His work bridges critical theory with practical policy frameworks, addressing issues like platform accountability and digital well-being. Grants & Collaborations: Major projects with the European Union and Norwegian Research Council on digital transformation and platform economies. Leadership roles in interdisciplinary initiatives like the Nordic Centre for Internet and Society. Labs/Teams: Leads the Nordic Centre for Internet and Society, fostering research on digital ethics and societal impacts of emerging technologies.
Kristian Muri Knausgård is a Lecturer at the Department of Engineering Sciences , University of Agder , Norway. He teaches courses in embedded systems, software development, and robotics. Current courses: MAS245 (Embedded Computer Systems), MAS417 (Software Development), MAS418 (Robotics Programming) Previous courses: MAS218 (Electrical Circuits), MAS234 (Embedded Systems) His research focuses on embedded systems , real-time systems , and technical cybernetics , with applications in artificial intelligence , computer vision , and systems engineering . He contributes to the university's research groups on Robotics and Automation and Systems Engineering and Modeling . Recent publications show a strong emphasis on: Autonomous systems (robotics, docking algorithms) Deep learning applications in marine ecology 3D reconstruction and computer vision techniques Fish detection/classification using neural networks Industrial automation for aquaponic systems His work bridges theoretical research with practical implementations in mechatronic systems and environmental monitoring.
Thomas Erich Zinner is Professor at the Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), a position held since August 2019. Previously, he served as visiting professor and head of the FG INET research group at TU Berlin, and led the 'Next Generation Networks' research group at the University of Würzburg's Communication Networks chair. His educational background includes a diploma (2006) and Ph.D. (2012), both from the University of Würzburg. His research spans network architecture performance evaluation with emphasis on SDN/NFV and QoE-centric management approaches for emerging networks. Zinner's recent publications reveal strong trends toward intelligent 6G architectures integrating AI in the user plane, QoE-aware 5G resource allocation, and autonomic management of softwarized networks. His work combines theoretical modeling, simulation frameworks like OMNeT++, and practical implementations focused on real-world applicability in beyond-5G systems. He leads the Networking Research Group at NTNU working on the TeraFlow project, developing secure cloud-native SDN controllers for autonomic traffic management at massive scale. This initiative addresses critical challenges in next-generation network infrastructure through innovative controller architectures and flow management techniques.
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.
Sebastian Georg Zieglmeier is a Research Fellow at the University of Oslo's Faculty of Mathematics and Natural Sciences, affiliated with the Section for Autonomous Systems and Sensor Technologies. His research focuses on advanced control systems, energy systems, and machine learning applications such as reinforcement learning and deep learning. He explores modeling techniques for autonomous power systems and data-driven control strategies. His recent publications emphasize RNA engineering, therapeutic applications, and immunogenicity reduction in vaccines. Key areas include RNA purification, lipid-based formulations, and cancer-targeted agents. Zieglmeier's work bridges biotechnology and engineering, with contributions to molecular diagnostics and vaccine development. While no specific grants or awards are noted, his research aligns with cutting-edge technologies in mRNA therapeutics and autonomous systems. He is based at the University of Oslo's Institute of Transport Economics (ITS) and contributes to interdisciplinary projects at the Faculty of Mathematics and Natural Sciences.
Arvid Aakre is an Associate Professor and Head of the Road, Railway and Transport Research Group at the Department of Civil and Environmental Engineering , NTNU. His research focuses on traffic flow theory, traffic regulation systems, road network design, and Intelligent Transport Systems (ITS). He has expertise in traffic simulation modeling, driver behavior analysis, and data-driven transportation solutions. Key areas of research include traffic signal optimization, ITS terminology standardization, and infrastructure planning for efficient traffic flow. He has published extensively on topics ranging from speed modeling of heavy vehicles to traffic capacity analysis in roundabouts and 2+1 road configurations. Aakre is actively involved in software development for traffic data collection (e.g., GPSLOG) and terminology tools for transport informatics. His work bridges academic research with practical applications, such as advising on national road regulations and contributing to transportation policy in Norway. He teaches courses in transportation engineering, traffic modeling, and ITS, reflecting his commitment to both academic and applied aspects of the field. His interdisciplinary approach spans civil engineering, human factors, and computational modeling.
Achim Kohler is a Professor in the Department of Physics at the Faculty of Science and Technology, Norwegian University of Life Sciences (NMBU). He leads the BioSpec research group under RealTek, focusing on vibrational spectroscopy of biological materials. His work integrates physics, data analysis, and measurement technology for high-throughput characterization of microorganisms and other biological systems. His research interests lie at the intersection of physics, spectroscopy, and data science . The BioSpec group pioneers in modeling scattering and absorption in infrared spectroscopy and develops advanced multivariate analysis methods for interpreting complex spectral data. Their work enables automated, label-free screening of biological samples with applications in microbiology, medicine, and environmental monitoring. The recent publications reflect a strong trend toward automated, data-driven analysis of biological systems using vibrational spectroscopy . Key fields include infrared and Raman spectroscopy, multivariate calibration, machine learning for spectral classification, and non-destructive testing of cells and tissues. The research spans fundamental optical modeling to applied diagnostics and process monitoring. The BioSpec group has achieved global recognition as one of the leading teams in multivariate analysis of vibrational spectroscopic data. Professor Kohler has led multiple Norwegian and European research projects, contributing significantly to the advancement of spectroscopic techniques in life sciences. He supervises research within the BioSpec group, mentoring students and researchers in interdisciplinary projects that combine experimental spectroscopy with computational modeling. While specific grant names are not listed, his leadership of national and international projects indicates substantial external funding. The group actively develops new methodologies for real-time, high-throughput characterization of biological materials. The BioSpec group is a multidisciplinary team conducting cutting-edge research in vibrational spectroscopy. They focus on both theoretical modeling and practical applications, including automated screening platforms and advanced chemometric tools. More information can be found at: https://www.nmbu.no/en/faculty/realtek/research/groups/biospectroscopy .
Guoyuan Li is a Professor at the Department of Ocean Operations and Civil Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU), Ålesund Campus. His work bridges digitalization , artificial intelligence , and maritime engineering , focusing on ship maneuvering, robotics, and human-machine interaction. Ph.D. in Computer Science, University of Hamburg (2013) M.S. & B.S. in Computer Science, Chongqing University (2009 & 2006) Research Interests: Digital twin systems for ships, adaptive locomotion control in bio-inspired robotics, trajectory prediction for marine vessels, and human visual attention analysis in maritime operations. He integrates machine learning and physics-based models to enhance safety and efficiency in marine environments. Publications highlight trends in ship motion prediction , collision avoidance , and environmental disturbance modeling , with applications in digital twin technology and remote control centers . His work spans IEEE and Springer journals. Awards include multiple Best Paper Awards at IEEE conferences (2024-2014). He serves as Associate Editor for IEEE Journal of Oceanic Engineering and IEEE Transactions on Intelligent Transportation Systems . Projects include EU’s RoboSapiens (robot adaptation), Digital Twin for Green Ship Operations (Norway), and AuReCo (remote control systems). He collaborates with the Intelligent Systems Lab at NTNU.
Woldaregay, Ashenafi Zebene is a researcher at UiT The Arctic University of Norway, affiliated with the Faculty of Health Sciences and the Department of Clinical Medicine. His work bridges digital health, artificial intelligence, and clinical applications, particularly in diabetes management and infectious disease surveillance. He is a key contributor to the EDMON (Electronic Disease Surveillance and Monitoring Network) project, which leverages self-recorded health data from people with Type 1 diabetes for early outbreak detection. PhD in Digital Health / Health Informatics (2021) Master’s in Electronic Disease Surveillance (2016) His research focuses on applying machine learning, data science, and AI to solve pressing healthcare challenges. Key areas include blood glucose pattern analysis in diabetes, anomaly detection, mHealth adoption, wearable technology, and syndromic surveillance. He has developed models for infection detection, surgical risk prediction, and deidentification of clinical text. His work emphasizes real-world applicability, patient engagement, and data privacy. The most recent articles show a strong trend toward transformer models, synthetic data, and instruction-guided NLP in clinical contexts, alongside continued work in mHealth usability, caregiver support, and personalized health monitoring. His publications span journals in health informatics, medical informatics, and digital health, reflecting interdisciplinary collaboration. Scientific contributions include: Development of the EDMON system for real-time infection monitoring Creation of datasets for wearables and mHealth motivation factors Systematic reviews on reinforcement learning in diabetes and AI in healthcare security Innovation in cluster detection algorithms (K-CUSUM) for outbreak detection While direct information on grants and advising is not provided, his role as a doctoral candidate and co-author on numerous student-led studies suggests involvement in research mentoring. He collaborates extensively with researchers such as Gunnar Hartvigsen, Eirik Årsand, and Karl Øyvind Mikalsen, indicating membership in a large, active digital health research group. He is involved in a multidisciplinary research lab focused on digital health innovation, mHealth systems, and AI-driven clinical decision support. The team works on real-time monitoring, data privacy, and user-centered design of health technologies, with applications in chronic disease management and public health surveillance.
Hanno Langweg serves as Associate Professor in the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU), based at the Gjøvik campus. His academic activities are deeply integrated with the COINS Research School of Computer and Information Security, a national consortium led by NTNU that unites Norway's major universities in cybersecurity research and doctoral training. Langweg's research centers on practical security vulnerabilities and compliance frameworks. His work extensively analyzes difficult-to-detect code patterns for SQL injection and cross-site scripting, develops forensic readiness methodologies, and bridges legal requirements like GDPR with technical security implementations. Recent publications demonstrate consistent focus on automated vulnerability injection, stakeholder-driven security requirements, and forensic evidence collection systems. His publication trends reveal sustained contributions across three interconnected domains: vulnerability pattern analysis (particularly in PHP and browser contexts), forensic readiness tooling (including kernel tracing and event reconstruction), and regulatory compliance engineering (GDPR, Common Criteria). This triad reflects both technical depth in code-level security and strategic understanding of legal-technical intersections. Langweg actively contributes to the academic community through conference organization (Sicherheit series) and teaching courses including Software Security (IIKG2001) and Information Security Summer School (IMT6003). His collaborations span multiple institutions with frequent co-authorship patterns involving Schuckert, Katt, and Liao. Within the COINS Research School framework, Langweg supports Norway's cybersecurity research ecosystem that now exceeds 100 doctoral students. His work directly enables the school's mission of integrating national research groups, enhancing student mobility, and establishing international recognition in information security.
Rahmi Lale is an Adjunct Associate Professor and Senior Researcher at the Department of Biotechnology and Food Science, Faculty of Natural Sciences, Norwegian University of Science and Technology (NTNU). His research spans synthetic biology, biophysics, and computational biology, focusing on transcriptional and translational control in microorganisms. He employs advanced methods like microfluidics, single-cell arrays, and high-throughput screening across diverse organisms including Thermus thermophilus , Vibrio natriegens , and Pseudomonas putida . He can be reached at rahmi.lale@ntnu.no. Research Interests: Biophysics, Computational Biology, Synthetic Biology, Metagenomics, Microfluidics, Bacterial Genetics Key Projects: Horizon Europe (MetaExplore, Sustainable Exploration), RCN (SafePhaeO3), U.S. NSF (CIRCLE Center), Innovation Fund Denmark (MuscleFuel), EuroHPC (AI-based DNA Design) Scientific Awards: Kavli Prize in Nanoscience (2018), iGEM Gold Medal (2014) Teaching: Advanced Biotechnology (BT3220), Bioinformatics and Statistics (TBT4507), Bioinformatics and Biopolymeric Materials (TBT4508) Lale's recent publications focus on synthetic gene regulation, AI-driven DNA design, and extremophile bioproduction. His lab website ( Lale Lab ) details ongoing work in interdisciplinary data-driven approaches.
Julia Debik is an Associate Professor at the Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), with a dual research role at the Musculoskeletal Research Group and CIMORe. She holds a PhD in Medical Technology and a Master of Science in Industrial Mathematics from NTNU, combining computational expertise with biomedical research. Academic Roles: Associate Professor (Onsager Fellow - AI and Health) Departments: Public Health and Nursing, Circulation and Medical Imaging Collaborations: National Network for Breast Cancer Research, Nordic Metabolomics Society, Metabolomics Society Research Focus: Integrating machine learning with metabolomics to uncover breast cancer risk factors, treatment response, and prognostic signatures. Her work emphasizes NMR-based metabolic profiling, biobank utilization (HUNT2, UK Biobank), and sample handling standardization (freeze-thaw cycles, delayed centrifugation). Publication Trends: Over 15 recent articles focus on metabolomic-biomarker discovery for breast cancer, circadian exercise effects, and technical validation of NMR platforms. Key subfields include lipoprotein associations, multi-omics integration, and AI-driven analysis of spatial -omics data. Scientific Contributions: Onsager Fellowship in AI and Health Key collaborator in HUNT biobank studies International presentations at Metabolomics Society conferences Developer of machine learning frameworks for multi-cancer profiling Outreach Activities: Regularly participates in public science events like Researchers' Night, delivers lectures on AI and molecular epidemiology, and contributes to popular science communication through initiatives like 'Tumorteltet'.
Eirik Østerud is a Professor at the Department of Private Law, Faculty of Law, University of Oslo, where he also serves as Acting Dean of Research. His academic career has been deeply rooted in competition law, with a focus on EU/EEA competition regulations and their application in Norwegian legal contexts. Born in 1978, Østerud earned his Cand. jur. (Norwegian law degree) in 2005 and completed his Ph.D. in 2010 from the University of Oslo's Faculty of Law. His doctoral thesis, 'Identifying Exclusionary Abuses under Article 82 EC – the Spectrum of Tests,' examined the prohibition on abuse of market dominance in EU competition law. Prior to his permanent academic appointment, he worked at BA-HR law firm from 2010 to 2013. Professor Østerud's research centers on competition law, with particular expertise in abuse of dominance, merger control, and the intersection of competition law with digital markets. His work addresses critical issues such as the application of competition rules to digital platforms, liability for competition law infringements, and the evolving regulatory landscape in Norway and the EEA. He has made significant contributions to understanding how traditional competition law principles apply to modern economic challenges, especially in data-driven markets. His publication record demonstrates a consistent focus on practical applications of competition law, with numerous articles analyzing Norwegian competition authorities' approaches, Supreme Court decisions, and the harmonization of Norwegian competition law with EU/EEA frameworks. Recent work increasingly addresses digital economy challenges, reflecting the evolving nature of competition law in the 21st century. Østerud is actively involved in the academic community through his participation in research groups including the Labor Law Group and the Market, Innovation and Competition (MIK) group at the University of Oslo. His work bridges theoretical competition law principles with practical enforcement considerations, making him a key contributor to Norway's competition law scholarship and practice.
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