Tom Roar Eikebrokk is a Professor at the Department of Information Systems , University of Agder , Norway. He contributes to the Center for Digital Transformation (CeDiT) research group and has over two decades of academic and practical experience in digitalization, business process management, and collaborative innovation. Research Focus : Digital transformation, co-creation frameworks, e-health innovation, IT service management (ITIL), and remote work dynamics. Methodologies : Empirical studies, mixed-method research, case analysis, and Delphi studies. Recent Publications (2024–2025) explore reciprocal relationships between BPM and digitalization, generative AI for sustainable co-creation, and open innovation workspaces in specialized industries. His 2018–2021 work on co-creation in SME networks, worklife ergonomics in digital environments, and robotic process automation impacts remains influential. Collaborative Networks : Frequently co-authors with Dag Håkon Olsen , Niels Frederik Garmann-Johnsen , and Jon Iden , focusing on cross-municipal healthcare systems, digital governance, and IT competence frameworks.
Zhixin Yu is a Professor of Natural Gas Technology at the Department of Energy and Petroleum Technology, Faculty of Science and Technology, University of Southeastern Norway. His research is centered on advanced energy technologies with a strong focus on CO 2 utilization, hydrogen production, electrocatalysis, and next-generation battery systems. He actively collaborates with a broad network of researchers in materials science and chemical engineering. His research interests span CO 2 capture and conversion , green hydrogen production via electrocatalysis and photocatalysis , design of single-atom and nanostructured catalysts (including MOFs, carbon-based materials) , and advanced energy storage systems such as lithium-ion, lithium-sulfur, and lithium-ion capacitors . His work integrates experimental synthesis with theoretical modeling, particularly density functional theory (DFT), to understand and optimize catalytic and electrochemical processes. The recent publications (2021–2025) reveal a strong thematic trend toward electrocatalytic CO 2 reduction to value-added chemicals , hydrogenation reactions using sustainable catalysts , and interface engineering in battery materials to improve stability and performance . The keywords consistently include catalysis, energy materials, CO 2 utilization, and electrochemistry, highlighting his role at the forefront of sustainable energy technology development. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: While specific students or grant details are not listed, Professor Yu appears to lead or significantly contribute to a research group focused on energy materials. His extensive co-authorship with early-career researchers (e.g., Song Lu, Obinna Egwu Eleri, Frederik Thorbjørn Huld) suggests active mentorship and advising. The volume and quality of publications indicate successful acquisition of research funding, likely from national and international sources supporting sustainable energy and materials science. Labs and Teams: No specific lab or research team name is provided. However, his frequent collaboration with colleagues such as Fengliu Lou, Song Lu, and Kun Guo indicates a well-integrated research group within the Department of Energy and Petroleum Technology, likely focused on catalytic materials and energy storage devices.
Professor Ole-Christoffer Granmo is a distinguished academic at the University of Agder, Norway, where he serves as Professor in the Department of Information and Communication Technology. He is the Founding Director of the Centre for Artificial Intelligence Research (CAIR) at the University of Agder, leading cutting-edge research in artificial intelligence and machine learning. Dr. Granmo obtained his master's degree in 1999 and his PhD in 2004, both from the University of Oslo. His academic journey has been marked by significant contributions to the field of AI, most notably the creation of the Tsetlin machine in 2018, for which he received the AI research paper of the decade award from the Norwegian Artificial Intelligence Consortium (NORA) in 2022. Professor Granmo's research primarily focuses on logical and causal world modeling across multiple modalities including images, sound, and natural language. His work spans logical auto-encoding, convolution, regression, transformer architectures, and reinforcement learning, all with the overarching goal of creating ultra-low-power artificial general intelligence through transparent logical learning and reasoning. His publications reveal a strong emphasis on interpretable AI systems, hardware implementations, and applications across diverse domains including cybersecurity, healthcare, social media analysis, and bioinformatics. AI Research Paper of the Decade (2022) - Norwegian Artificial Intelligence Consortium (NORA) Eight paper awards in machine learning Professor Granmo has coordinated over seven research projects and mentored 55+ master's students and nine PhD students. His leadership extends to co-founding the Norwegian Artificial Intelligence Consortium (NORA) and establishing two companies: Anzyz Technologies AS and Tsense Intelligent Healthcare AS. As an advisor at Literal Labs, he actively bridges academic research with practical industry applications, demonstrating his commitment to translating theoretical innovations into real-world solutions that address complex challenges across multiple sectors.
Arnoldo Frigessi is Professor of Statistics at the University of Oslo, where he leads the Oslo Center for Biostatistics and Epidemiology and serves as director of BigInsight—a Centre of Excellence for Research-Based Innovation. This consortium unites industry, business, public actors, and academia to develop model-based machine learning methodologies for big data, with strong emphasis on health applications. His research centers on statistical methodology driven by real-world scientific challenges, specializing in stochastic models for complex dependence structures and computationally intensive inference algorithms. Core application domains include: Genomics and personalized cancer therapy (particularly breast and lung cancer) Infectious disease modeling (including pandemic response) eHealth, sensor data analysis, and recommender systems Personalized marketing and viral diffusion dynamics Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in cancer systems biology , where he integrates multi-omics, single-cell transcriptomics, and computational modeling to decode tumor evolution under therapy. Parallel work advances infectious disease epidemiology through time-varying reproduction number estimation and mobility-based transmission modeling, while methodological innovations span synthetic data generation (TVineSynth), causal inference via target trial emulation, and Bayesian ranking models for recommender systems. Scientific Awards: No specific awards mentioned in source materials Frigessi actively supervises graduate students, including a Department of Informatics project on "Utilizing covariate information in recommender systems." His leadership of BigInsight—funded as a Research-Based Innovation Centre by the Research Council of Norway—secures major grants supporting interdisciplinary collaborations with industrial partners (e.g., Telenor, DNB) and public health institutions. Current projects integrate real-world clinical data with mechanistic models for treatment optimization. He directs BigInsight's multidisciplinary team of statisticians, computer scientists, and domain experts, while leading the Oslo Center for Biostatistics and Epidemiology's efforts in developing statistical frameworks for complex health data. These initiatives drive Norway's national strategy for data-driven health innovation.
Meng Jiang is an Assistant Professor in the Department of Energy and Process Engineering at the Norwegian University of Science and Technology (NTNU). He is affiliated with NTNU’s Industrial Ecology Programme and collaborates closely with Prof. Edgar Hertwich. His research focuses on input-output analysis, material flow analysis, and modeling resource efficiency and circular economy strategies, particularly in chemical systems. He holds a Ph.D. in Chemical Engineering (Industrial Ecology) from Tsinghua University, China, and has additional academic training at the University of Washington (Seattle) and professional experience at the International Institute for Applied Systems Analysis (IIASA) and the United Nations Industrial Development Organization (UNIDO). Research interests include resource management, material flow analysis, and the intersection of chemical systems with sustainability. His work emphasizes China’s material and carbon footprints, circular economy transitions, and socio-economic-environmental interactions. He has contributed to frameworks like the Planetary Pressure-Adjusted Human Development Index and studies on regional disparities in resource use. Publications highlight contributions to understanding China’s fossil-based chemical production, machinery carbon footprints, and urban low-carbon transitions. Collaborations span global institutions, including presentations at the International Conference on Industrial Ecology (ISIE) and Gordon Research Conferences. Teaching includes courses on input-output analysis and environmental trade impacts. His work bridges academic research with practical policy implications, addressing global sustainability challenges through data-driven methodologies.
Daniel Groos is a Researcher at the Department of Computer Science, NTNU, specializing in the development of machine learning models for medical and sports-related motion analysis. His work focuses on applying deep learning techniques to video-based movement analysis for early detection of cerebral palsy in infants and performance analysis in elite sports. Education: PhD in Medical Technology (NTNU, 2018-2022), MSc in Computer Science with specialization in AI (NTNU, 2013-2018). Research interests include interdisciplinary collaborations with St. Olavs Hospital and Norwegian Open AI Lab. Key topics are deep learning applications in healthcare, computer vision for movement analysis, and sports biomechanics. Publications emphasize automated clinical analysis, video-based diagnostics, and human pose estimation. Notable projects include a deep learning method for cerebral palsy prediction and motion tracking systems for elite ski jumpers. Collaborations with institutions like the Centre for Elite Sports Research and Olympiatoppen highlight his work in sports performance analysis. No formal scientific awards listed but active in academic outreach with lectures at European conferences on childhood disability and movement analysis.
Alma Leora Culén is a Professor in the Design of Information Systems at the Department of Informatics, University of Oslo, part of the Faculty of Mathematics and Natural Sciences. She holds a prominent role in advancing sustainable interaction design, research through design methodologies, and transformative design practices focused on societal transitions. Her academic journey includes contributions to HCI education reform, emphasizing ethical and sustainable design principles. Culén has pioneered projects like 'Plurishop' exploring sustainable smartphone alternatives and 'Transition Design' approaches for mitigating democratic erosion. She actively engages in interdisciplinary collaborations through research groups such as DESIGN and Design4Dem. Key research interests span sustainable technology, participatory design processes, and leveraging AI for complex design mediation. Her work often intersects with societal challenges like climate action, youth engagement in socio-technical systems, and rural sustainability initiatives. Culén's pedagogical innovations include intensive design courses and speculative installations for civic education. She leads projects funded by the Norwegian Research Council, focusing on sustainable consumption patterns and MaaS solutions for rural areas. Notable collaborations include international design research societies and EU-funded sustainability initiatives. Her lab activities emphasize participatory prototyping and transition design frameworks, with a focus on vulnerable populations such as chronically ill youth and elderly users. Culén's work bridges academic research with real-world impact through partnerships with libraries, municipalities, and tech industries.
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.
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.
Egil Øvrelid is an Associate Professor at the Department of Informatics (IFI), University of Oslo, affiliated with the Digital Innovation (DIN) research group. His work focuses on digital infrastructures, healthcare IT systems, and sociotechnical interplay in organizational innovation. He explores topics such as digital transformation, process innovation, and platform ecosystems in healthcare and higher education contexts. His research emphasizes the alignment of digital strategies with organizational practices, particularly in large-scale infrastructures. Key areas include lightweight IT solutions for process innovation, architectural transformation in incumbent organizations, and governance mechanisms in collaborative platforms. He has contributed to frameworks like 'dual digitalization' and 'adaptive mirroring' in healthcare IT architectures. Notable projects include the TSD platform for sensitive data research and studies on national digital ecosystems like Norway’s one-citizen-one-health-record initiative. His work bridges theory and practice, often collaborating with healthcare and educational institutions to address real-world challenges in digital innovation. Research Groups: Digital Innovation (DIN) Key Themes: Sociotechnical systems, digital transformation, healthcare IT, process innovation, platform ecosystems
Shuai Ren is a Research Fellow at the Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU). His research focuses on high-temperature heat pumps, heat and mass transfer, and computational fluid dynamics, with applications in industrial refrigeration and food processing. He is part of the ENOUGH project, exploring sustainable energy solutions. Key research interests include absorption-compression heat pump systems, CO2-based refrigeration technologies, and optimization of thermal systems for industrial use. His work addresses energy efficiency and sustainable energy integration in sectors like dairy processing and food manufacturing. Recent publications emphasize experimental and numerical studies of heat pump performance, including oil-free systems and ammonia-water mixtures. He has presented at international conferences such as the IIR Gustav Lorentzen Conference and the International Congress of Refrigeration. Shuai Ren advised the 2023 Master's thesis 'Utilization of surplus heat from the organic dairy at Røros.' His research collaborations involve institutions like the International Institute of Refrigeration and projects targeting low-carbon energy solutions.
Richard Wood is an Adjunct Professor at the Industrial Ecology Programme, Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU), and a part-time Professor at the School of Environment, University of Newcastle, Australia. He leads research consultancy XIO-SA, focusing on environmental-economic analysis for public good. His expertise includes sustainability science, systems approaches, and quantitative modeling techniques like carbon footprinting, life-cycle assessment, and input-output analysis. **Research Interests**: Globalization and environmental impacts of trade Sustainable consumption and production Circular economy and material efficiency Environmental footprints and policy design EXIOBASE multi-regional input-output modeling **Awards**: Recognized as a Web of Science Highly Cited Researcher (2019–2022), and Co-Editor in Chief of the Journal of Industrial Ecology. His work has produced over 100 publications, including foundational contributions to EXIOBASE, a global environmental input-output database. **Grants & Labs**: Key developer of EXIOBASE, widely used for environmental footprint quantification. Advises on methodologies for consumption-based carbon accounting and sustainable investment strategies. Active in global policy initiatives addressing climate change and biodiversity loss.
Ida Scheel is an Associate Professor in Statistics and Data Science at the University of Oslo , Department of Mathematics. She specializes in Bayesian hierarchical modeling, recommendation systems, and stochastic processes on networks. Her research interests include: Bayesian statistics and model diagnostics Data science applications in environmental and health domains Network-based machine learning Uncertainty quantification in predictive modeling Recent publication trends show a focus on Bayesian model validation, machine learning for product adoption prediction, and real-estate analytics. She contributes to interdisciplinary projects like BigInsight and CELS . Scientific awards : Sverdrup Prize for Young Researchers (2011) Advising : Supervised 8 PhD students (main/co-supervisor) in areas spanning Bayesian causal effects, neural network survival analysis, and model conflict detection. Key grants include participation in the Data Science@UiO and Integreat projects. Labs/teams : Active member of the Center for Computational Inference in Evolutionary Life Science (CELS) and the BigInsight center.
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.
Andres Soler is a Lecturer at NTNU's Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering. His research focuses on EEG signal processing for applications in brain-computer interfaces (BCI), stress/health monitoring, and low-density electrode systems. He has published extensively on topics including EEG source imaging, artifact removal, and optimized channel selection techniques. His work bridges biomedical engineering and machine learning, with notable contributions to driver alcohol detection systems and motor imagery classification for neurorehabilitation. Teaching roles include serving as Guest Lecturer for Biomedical Instrumentation and Control (TTK4270) and Adaptive Data Analysis (TTK7), while acting as main lecturer for Industrial Electrotechnics (TTK4240). His research group collaborates internationally on projects like FlexEEG and has presented at conferences such as IEEE EMBC and Brain Informatics. Key research directions include advancing EEG-based systems for clinical and automotive applications, developing algorithms for real-time brain activity decoding, and optimizing EEG hardware configurations for cost-effective implementations. Current trends show focus on enhancing signal quality through artifact mitigation strategies and improving BCI communication systems for locked-in patients.
