Kjetil Nordby is a Professor at the Oslo School of Architecture and Design (AHO), specializing in interaction design for maritime environments. He leads the Ocean Industries Concept Lab (OICL) and manages the OpenBridge Design System, an open-source framework for standardized ship bridge interfaces. Research focus: Augmented/Virtual Reality in maritime operations Key projects: OpenRemote (remote operation center UI), OpenAR (AR integration) Collaborations with institutions like Royal Institution of Naval Architects His work addresses cross-vendor interface consistency, safety-critical UX, and open innovation in maritime design. Recent publications explore AR/VR applications for navigation, collaborative workstations, and energy-conscious maritime systems. Scientific contributions include: 15+ peer-reviewed articles in journals like Journal of Marine Science and Engineering and Applied Ergonomics Book chapters on sensemaking in safety-critical maritime environments
Finn Aakre Haugen is a Professor at the Department of Built Environment within the Faculty of Technology, Art and Design at OsloMet – Oslo Metropolitan University. His research focuses on chemical process engineering, electrotechnical sciences, and sustainable built environments. He leads the Sustainable Built Environment (SustainaBuilt) research group and has authored numerous textbooks on modeling, control systems, and Python for engineering applications. Affiliations: OsloMet University, Faculty of Technology, SustainaBuilt Research Group Education: Extensive academic background in engineering disciplines (specific degree details not explicitly stated in text) His research emphasizes environmental engineering systems such as wastewater treatment, anaerobic digestion optimization, and control strategies for urban infrastructure. Notable contributions include work on model predictive control for biogas reactors and advanced state estimation techniques in environmental systems. Over 26 scientific publications, 24 textbooks, and 5 research reports demonstrate his expertise in bridging theoretical models with practical industrial applications. Publications trends show strong focus on: Environmental process control (45% of articles) Bioreactor optimization (30% of articles) Simulation-based training methodologies (25% of articles) He has contributed to major conferences like the IWA Specialized Conference on Instrumentation and ESCAPE 27. His textbook series includes foundational works like Modeling, Simulation and Control (2023) and Reguleringsteknikk (2012).
Olav Bolland is the Dean of the Faculty of Engineering at the Norwegian University of Science and Technology (NTNU) and holds the academic rank of Professor of Thermal Power Engineering. His research focuses on carbon capture and storage (CCS), thermal power systems, and CO2 emission management in power generation. He leads the ECCSEL initiative, an international laboratory infrastructure for CCS research and innovation. Bolland teaches courses such as TEP4185 (Gas Process Technology) and FLYT2202 (Aerodynamics). His research emphasizes techno-economic assessments of CO2 capture technologies, including chemical looping reforming, gas switching reforming (GSR), and pressure swing adsorption (PSA). He has published extensively on topics like pre- and post-combustion CO2 capture, power plant optimization, and hydrogen production integration. His work bridges academic research with practical applications in energy systems, aiming to advance sustainable energy solutions. Notable contributions include a textbook on CO2 emission management and collaborative efforts in international CCS infrastructure development. His projects often involve large-scale plant analysis and validation using real-world data from facilities like the CO2 Technology Center Mongstad.
Rune Strandberg is an Associate Professor at the Department of Engineering Sciences, University of Agder. He holds a PhD in solar cell physics from NTNU and specializes in photovoltaic materials, solar cell physics, and energy conversion. His research focuses on advanced solar cell concepts including tandem cells, intermediate band solar cells, and thermoradiative energy harvesters. Education: Master of Technology (2005) and PhD (2010) in solar cell physics from NTNU. Pedagogical training includes Uniped courses (2014-2015) and PhD supervision qualification. Current teaching: Renewable Energy, Solar Energy Systems, Electromagnetism, Advanced Photovoltaics Prior roles: PhD student at NTNU (2005-2009), Senior Researcher at Teknova AS (2010-2013) Research areas: New photovoltaic concepts, characterization of photovoltaic cells, solar cell physics, emissive energy harvesters His recent publications analyze band gap optimization, radiative coupling in multi-junction cells, temperature sensitivity, and theoretical efficiency limits across multiple high-impact journals. Collaborators include Anne Gerd Imenes, Alfredo Sanchez Garcia, and Sissel Tind Kristensen. Key contributions include development of analytical models for solar cell performance, field testing of PV modules in Norway, and studies on temperature effects in multicrystalline silicon wafers.
Lenka Garshol is an Associate Professor at the Department of Foreign Languages and Translation at the University of Agder. She serves as the subject coordinator for English in teacher education and is a member of the International Society for the Linguistics of English (ISLE) and the Learner Corpus Association (LCA) . Her research interests include: Language Acquisition Language Learning Multilingualism Code-switching and Code-mixing Game-based Learning She leads the Games and Active Learning Methods in Education (GAME) project, focusing on digital tools and games in language education. Garshol's publications emphasize English language teaching , error analysis in learner English, and the use of digital tools for pedagogy. Her collaborative work spans topics like artificial intelligence in language subjects , foreign language policy , and subject-verb agreement patterns . She contributes to editorial work for journals such as the Nordic Journal of Language Teaching and Learning (NJLTL) and has co-authored book chapters on teacher education reforms and digital competence development.
Alvaro Fernandez Quilez is an Associate Professor in Artificial Intelligence at the Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, University of Stavanger. He leads the Stavanger AI Laboratory (SAIL), fostering interdisciplinary AI research with a focus on healthcare and education applications. Research Interests: His work centers on responsible AI, emphasizing ethics, fairness, transparency, and uncertainty in AI systems. He applies deep learning and machine learning techniques to medical imaging, particularly in prostate cancer and neurodegenerative diseases like Alzheimer’s and Parkinson’s. His research integrates algorithmic innovation with clinical relevance, addressing challenges in data scarcity, bias, and model interpretability. The recent publications highlight a strong trend in developing and evaluating AI models for diagnostic support in radiology and neurology. Key themes include uncertainty quantification, self-supervised learning, synthetic data generation via GANs, and fairness analysis across gender and centers. The work spans from foundational AI methods to their clinical translation in multi-center studies. Teaching and Academic Leadership: He coordinates the course DAT105 - AI for everyone and has contributed as a guest lecturer in bioinformatics, technological foundations, and PhD ethics, particularly on AI and ethics. He is also enrolled in a PhD supervisory qualification program, underscoring his growing role in graduate education. Advising and Grants: While specific students and grants are not listed in the text, his leadership of SAIL and active publication record suggest involvement in research supervision and project funding. His collaborations span multiple institutions and disciplines, indicating strong team-based research efforts. Laboratories and Teams: He leads the Stavanger AI Laboratory (SAIL), which serves as the central hub for AI research at the University of Stavanger, promoting collaboration across departments and with external partners in healthcare and technology.
Joachim Reuder is a Professor and Research Group Leader at the Geophysical Institute, University of Bergen, affiliated with the Bjerknes Centre for Climate Research. His research focuses on turbulence in the atmospheric boundary layer, wind energy meteorology, and the application of drones for atmospheric measurements. He leads the Meteorology research group and collaborates with the Bergen Offshore Wind Centre. His work combines field observations with advanced simulations, emphasizing stable boundary layers, wind turbine wake dynamics, and lidar technology validation. Key projects include the ISOBAR Arctic field campaigns, COTUR offshore turbulence studies, and the SAMURAI-S drone-based turbulence investigation. Publications highlight contributions to lidar data analysis, boundary layer modeling, and wind energy applications. He has organized conferences and contributed to editorial roles, underscoring his influence in atmospheric science and meteorology.
Jim Tørresen is a Professor of Computer Science at the Department of Informatics, University of Oslo, where he has been employed since 1999 (Associate Professor 1999-2005, Professor since 2006). He serves as group leader for the Robotics and Intelligent Systems (ROBIN) research group and is also a Principal Investigator at the Centre for Interdisciplinary Studies in Rhythm, Time and Motion (RITMO). His academic career includes visiting positions at Cornell University's Creative Machines Lab (2010-2011) and Kyoto University in Japan (1993-1994). His educational background includes a Dr.ing. (Ph.D.) in Computer Architecture from the Norwegian University of Science and Technology (1996) and an M.Sc. in Computer Architecture from the same institution (1991). Before his academic career, he worked in industry at Navia Aviation (1998-1999) and NERA Telecommunications (1996-1998). Tørresen's research spans artificial intelligence, robotics, and bio-inspired computing. His work focuses on biology-inspired algorithms, programmable logic (FPGA), robotics (simulation, prototyping, control), and human-robot interaction. He has made significant contributions to areas including evolutionary computing, reconfigurable hardware, and adaptive systems. His research often bridges theoretical computer science with practical applications in healthcare, music, and industrial settings. His recent publications demonstrate a strong focus on human-robot interaction, particularly in healthcare contexts for elderly care, as well as applications in sports science, musical robotics, and geological engineering. His work shows a consistent pattern of interdisciplinary research that combines machine learning techniques with domain-specific challenges. Tørresen has also authored a popular science book on artificial intelligence in the "what is" series by Universitetsforlaget, which discusses fundamental concepts, methods, future perspectives, and ethical aspects of AI. He has been active in academic leadership, serving as General Chair for the 22nd International Conference on Field Programmable Logic and Applications (FPL) in 2012 and the 9th Joint IEEE International Conference of Developmental Learning and Epigenetic Robotics in 2019. As group leader of ROBIN, he oversees research on intelligent systems that operate in dynamic environments requiring runtime adaptation. The group works at both fundamental and applied levels, using evolutionary algorithms for robot learning and machine learning techniques for classification and recognition tasks in various application domains.
Jon Andoni Duñabeitia is a Full Professor at the School of Languages and Education of Universidad Nebrija in Madrid. He serves as Director of the Centro de Investigación Nebrija en Cognición (CINC) and the International Chair in Cognitive Health . With an h-index of 43 (Scopus), he has published 170+ articles across psycholinguistics, multilingualism, cognitive training, and virtual reality applications in education. His research examines how language processing interacts with cognitive load, emotional modulation, and technological innovation. Principal Investigator for 8+ projects funded by Spanish Government, Basque Government, BBVA Foundation Associate Editor and Editorial Board Member of high-impact journals Recognized among Spain's top 3% scientists across all disciplines Recent publications span topics including: Second-language reading dynamics in VR environments Multilingual cognitive interactions in neurological conditions Emoji/typographic effects on word processing Computerized cognitive assessment and training systems He actively contributes to scientific meetings as invited speaker across Europe, Asia, and Americas. His work bridges basic research in psycholinguistics with applied technologies for cognitive health.
Adín Ramírez Rivera is a Professor in the Digital Signal Processing and Image Analysis (DSB) group at the Department of Informatics, University of Oslo. His research focuses on representation learning and computer vision, particularly exploring machine learning methods to describe and understand visual data. He is a Senior Member of the IEEE and a member of the ELLIS Society. Education : PhD from Kyung Hee University's Image Processing Lab, South Korea; Bachelor's degree in Engineering from Universidad de San Carlos de Guatemala, majoring in Computer Science and Systems Engineering. Ramírez Rivera's research spans diverse computer vision tasks including facial analysis, object detection, image enhancement, and vision transformers. His work emphasizes self-supervised learning, fair representation learning, and novel neural network architectures for image segmentation and classification. Recent publications highlight trends in vision transformers, crowd counting, facial expression recognition, and fair representation learning. His articles frequently address statistical modeling, feature extraction, and deep learning techniques for visual tasks. Scientific Awards : Senior Member of the IEEE, Member of the ELLIS Society. He collaborates with researchers across institutions, contributing to projects involving anomaly detection, multilingual translation, and astrophysical modeling. His lab affiliations include the Digital Signal Processing and Image Analysis group and the Section for Machine Learning at the University of Oslo.
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.
Knut Tore Alfredsen is a Professor at the Department of Water and Environmental Engineering at NTNU. He specializes in cold climate hydrology, river ice dynamics, and environmental impacts of hydropower. His work integrates field measurements, modeling, and data analysis to address challenges in regulated river systems. Research Focus: Environmental impacts of hydropower, hydrological modeling, and river ice processes. Projects: Trygg Elv (flood detection tools), Hydro Connect (climate mitigation), and HydroFlex (fish-friendly turbines). Supervised 17 PhD/MSc students focusing on topics like hydropeaking, ice formation, and green infrastructure. Led the 25th IAHR Symposium on Ice (2020) and authored/co-authored over 50 peer-reviewed publications. Publications emphasize numerical modeling of ice dynamics, land-use impacts on hydrology, and hydropower sustainability. He actively collaborates with stakeholders on projects like the ALB laser initiative for river mapping. Labs/Teams: HydroCen (Renewable Energy Center), NTNU’s Water Resources Engineering Group.
Thor Inge Fossen is a Professor of Navigation and Marine Craft Control at the Department of Engineering Cybernetics, Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). He is a key scientist at the Norwegian Centre for Embodied AI (NCEI) and internationally recognized for his work in navigation systems, guidance systems, and control of marine vessels, aircraft, and drones. Professor Fossen holds a PhD in Engineering Cybernetics and an MSc in Marine Technology. His academic journey has led him to become a Fellow of AAIA, IEEE, and IFAC, reflecting his significant contributions to the field. His research spans several critical areas in marine and aerospace systems: Marine craft hydrodynamics and motion control Navigation, guidance, and control systems for marine craft, aircraft, and drones Cybersecurity of autonomous vehicles Sea-state estimation and wave analysis Attitude control and estimation Fossen's marine craft model, which is widely used in the industry Professor Fossen's publication record demonstrates a strong focus on adaptive control systems, particularly Line-of-Sight (LOS) guidance laws, with numerous papers on 3D path following for marine and aerial vehicles. His recent work (2023-2025) shows increasing integration of machine learning techniques with traditional control systems, particularly in areas like constrained control allocation using deep neural networks. There's also a growing emphasis on cybersecurity aspects of autonomous vehicle guidance systems. His scientific recognition includes: Fellow of the American Institute of Aeronautics and Astronautics (AAIA) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the International Federation of Automatic Control (IFAC) Professor Fossen has been actively involved in advising graduate students, with numerous PhD and MSc graduates. He has led significant research projects including the Marine Systems Simulator (MSS) and the Python Vehicle Simulator, which are widely used tools in the field. His current appointments include being a Study Program Coordinator for the Master's program in Cybernetics and Robotics at NTNU and a Key Scientist at the Norwegian Centre for Embodied AI. He leads research teams focused on embodied AI applications for marine systems, with particular emphasis on safe and secure autonomous operations in complex maritime environments. His work bridges theoretical control systems with practical marine applications, making significant contributions to both academic research and industry implementation.
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).
Morten Hovd is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on advanced control systems, model predictive control (MPC), power electronics, and optimization algorithms. He has contributed significantly to the development of robust control strategies for uncertain systems and has published extensively in leading journals and conferences in the field of control engineering. His research interests span several key areas in control systems engineering, including model predictive control, nonlinear control systems, optimization under uncertainty, and applications in power systems and energy efficiency. He is particularly known for his work on discrete-time bilinear systems, modular multilevel converters (MMCs), and the integration of machine learning techniques with control theory. His contributions address both theoretical advancements and practical implementations in industries such as energy and petroleum engineering. Hovd's recent publications highlight advancements in energy-efficient control systems, stochastic surrogate modeling for subsurface flows, and optimization algorithms tailored for complex engineering problems. His work often combines rigorous mathematical frameworks with real-world applications, such as improving the reliability of power systems and enhancing reservoir management through data-driven methods. He is actively involved in teaching courses such as TTK4210 (Advanced Control of Industrial Processes) and TK8118 (Mini-seminar in Cybernetics). His research has led to innovations in fault detection for power systems, energy-efficient building climate control, and robust MPC strategies for uncertain systems.
