Yan Zhou is a Research Fellow at the University of Oslo's Department of Informatics (Data and Knowledge Management), affiliated with the Faculty of Mathematics and Natural Sciences. His research focuses on applying data science and machine learning to environmental and energy systems, emphasizing reservoir operations, sustainable energy integration, and climate resilience. Key interests include optimizing hydropower systems, flood forecasting, and mitigating carbon emissions through data-driven approaches. His work spans machine learning applications for hydrological modeling, renewable energy forecasting, and multi-objective optimization frameworks. Recent studies address challenges in reservoir management, wind energy clustering, and urban sustainability through interdisciplinary methods. Notable contributions include hybrid deep learning models for algal bloom prediction and frameworks integrating pumped-storage systems with hydropower. Publications highlight innovations in flood risk assessment, carbon budget analysis, and climate-resilient infrastructure. Yan Zhou collaborates on projects combining physical hydrology with advanced analytics, aiming to enhance energy-water-food nexus synergies in dynamic environmental contexts. No specific awards or grants are listed, though his active research portfolio demonstrates significant contributions to environmental data science.
Halvard Arntzen is an Associate Professor at Molde University College, affiliated with the Faculty of Logistics and the Department of Logistics. He currently serves as the acting Dean for the Department of Logistics. His academic background includes a Cand. Scient. in Mathematics from the University of Oslo (1994) and extensive experience in logistics and operations research, including roles as a scientific assistant at the University of Oslo (1994–2000) and faculty member at Molde University College since 2000. Research Interests: Applied mathematics, statistical modeling, econometrics, operations research, planning, optimization, decision support systems, and sport analytics. His work spans topics such as stochastic vehicle routing, team sports outcome prediction, and heuristic algorithms for complex optimization problems. Teaching: Courses include Log 708 - Applied Statistics and Mat 210 - Statistics II , focusing on practical applications of statistical methods and regression analysis. Research Groups: Member of the Planning, Optimization and Decision Support research group. Publications: Over 15 peer-reviewed articles, including influential works on sport analytics, stochastic vehicle routing, and optimization algorithms. His research addresses real-world challenges in logistics, healthcare, and sports through data-driven methodologies.
Daniel Hagen is an Associate Professor at the Department of Engineering Sciences, University of Agder (UiA), Norway. He holds a Ph.D. in Engineering Sciences (2020), M.Sc. and B.Sc. in Mechatronics (2014 and 2012), and a Trade Certificate as Automation Mechanic (2009). His academic career is complemented by extensive industrial experience in roles such as Chief Scientist at Twilligent and Senior Technical Advisor at MotionTech, focusing on control systems, robotics, and energy-efficient mechatronics. Dr. Hagen's research interests include robotics, actuation systems, fluid power, and control systems. He leads projects in digital twins, autonomous robotics (e.g., UiAbot), and energy-efficient electro-hydraulic systems. His work emphasizes practical applications in offshore engineering and industrial automation. He teaches courses like Robotics and Instrumentation, Programming for Intelligent Robotics, and Mechatronic System Design. He is affiliated with research groups such as CAIR (Artificial Intelligence Research) and the Intelligent Mechatronics (iTron) group. Recent publications focus on real-time control systems, payload motion prediction, and digital twin frameworks for mechatronics. His research bridges academic innovation with industrial challenges, particularly in energy efficiency and autonomous systems.
Prof. Nadav Bar is a Professor at the Department of Chemical Engineering, NTNU, leading the Microbial Feedback Control (MFC) laboratory. His research focuses on integrating biotechnology with control theory to develop feedback systems for microbiological processes. He coordinates major EU projects including iCulture (7.8M€, 2023-2028) and AILEEN (1.5M€, 2023-2028), focusing on AI-driven microbial control and food safety. His lab specializes in real-time bioprocess control using advanced methods like Model Predictive Control (MPC) and Reinforcement Learning, alongside Digital Twin technologies for bioreactor monitoring. Key projects include optimizing food preservation via high-pressure processing (SafeFood, 2016-2019) and COPD diagnosis through systems biology (ERA SysMed-COPD, 2018-2022). His research spans microbial fermentation optimization, gene regulatory network analysis, and systems biology. Current PhD students include Fabienne Roessler and Yiwen Li. The lab actively recruits students in control engineering, biotechnology, and analytical chemistry. Notable contributions include developing noise reduction algorithms for gene expression data and advancing sensor fusion techniques in bioprocess monitoring. Research Themes: Feedback control of microorganisms, hybrid modeling with machine learning, optimal control of bio-processes, microbial sensor systems, and COPD systems biology. Lab infrastructure includes state-of-the-art bioreactors, autosamplers, and software tools like MATLAB-CASADI for real-time control. Key Publications: Recent works focus on robust bioprocess optimization using spent sulfite liquor (2024), advanced state estimation in bioreactors (2023), and high-pressure processing effects on Listeria monocytogenes transcriptomes (2021). Over 50 peer-reviewed articles demonstrate cross-disciplinary impact across biotechnology and control engineering.
Professor Dimitrios Kraniotis is affiliated with the Department of Built Environment at Oslo Metropolitan University's Faculty of Technology, Art and Design. His research focuses on sustainable building technologies, climate change impacts on built environments, and heritage conservation. He leads projects like the Net-Zero Future initiative addressing carbon footprint reduction. Research areas include moisture and heat transfer in timber systems, hygrothermal performance modeling, and adaptive reuse of heritage structures. He has published 47+ scientific papers and contributed to 37 dissemination reports, emphasizing data-driven approaches for climate resilience. Key research groups: Building Technology, Structural Engineering Research Group (SERG), Sustainable Built Environment (SustainaBuilt) Notable projects: Net-Zero Future Alliance (international collaboration), HYPERION EU project (cultural heritage climate impact) His work bridges computational modeling (CFD, Bayesian networks) with practical applications in material durability and energy efficiency. He collaborates with industry partners for real-world implementation of sustainable solutions.
Anniken Susanne Thoresen Karlsen is an Associate Professor in the Department of ICT and Science at the Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). She has extensive experience in research and teaching, with a focus on digital transformation, software-intensive systems, and emerging technologies. Karlsen holds leadership roles including deputy chair of the interdisciplinary NTNU research group SHAPE and active participation in multiple research centers. Her educational background includes: PhD in Information Science from the University of Bergen (UiB) Master's degree in Information Technology from Aalborg University (AAU) in Denmark Master of Science in Economics from the Norwegian School of Economics (NHH) Computer Engineering degree from Møre og Romsdal School of Engineering (MRIH) One-year practical-pedagogical education from NHH and UiB NTNU's UniPed program for employees Professor Karlsen's research spans a wide spectrum of digital transformation topics. Her work focuses on the integration of concepts from multiple domains to design and develop software-intensive systems. She explores modern software development methodologies, socio-technical systems thinking, and systems engineering approaches. A significant portion of her research addresses emerging technologies such as digital twins, virtual reality, and service robots as building blocks for innovative solutions. She also investigates the digital economy and sustainable development through digital transformation, with particular emphasis on business modeling and change management. Her recent publications demonstrate a strong focus on applying digital technologies to solve real-world problems across multiple sectors. There's a clear trend toward using digital twin technology in various contexts including urban planning, offshore wind farms, and healthcare. Her work in search and rescue operations shows consistent application of AI and knowledge management techniques. Karlsen's research bridges theoretical concepts with practical applications, particularly in sustainability-focused domains like renewable energy and healthcare. Professor Karlsen actively supervises students at all academic levels and has been involved in numerous research projects. She has held various academic leadership positions including vice-dean, head of department, and research group leader. Her collaborative work spans multiple sectors including healthcare, search and rescue, offshore, banking, food, and construction industries. She is affiliated with several research groups and centers: SHAPE - SamHandlingsArena for Prosjektledelse og Endring (2023-present), Deputy Chair The Norwegian Open AI Lab (NAIL) (2023-present) Green2050 - The Centre for Green Shift in the Built Environment (2022-present) Forskningsarena for bærekraftsanalyse (2020-present), Deputy Leader Forskningsgruppen for bærekraftig digital transformasjon, SDT (2017-present), Founder and former Leader
Ashkan Jahanbani Ghahfarokhi is an Associate Professor at the Norwegian University of Science and Technology (NTNU), Department of Geosciences (IGV), specializing in reservoir engineering. He leads the CEORS Gemini Centre, a strategic collaboration between NTNU and SINTEF on CO2 Enhanced Oil Recovery & Storage. His roles include Head of the Well and Reservoir Group (2021-2022), Project Manager for the NORHED II Program in Mozambique, and Guest Editor for the Energies journal's AI/ML in Oil & Gas Special Issue. Education: PhD (NTNU, 2015), MEng (University of Calgary, 2009), MSc/BSc (Petroleum University of Technology, Iran) Research focuses on subsurface reservoir modeling, CO2 storage, data-driven techniques, and production optimization. Key interests include: CO2-EOR/Storage system design Machine learning integration in reservoir simulation Wellbore-reservoir coupling dynamics Recent articles emphasize advanced numerical modeling of CO2 injection in depleted gas fields, AI-driven proxy models for reservoir optimization, and interfacial tension analysis for hydrogen storage. His work spans 40+ peer-reviewed papers and 20+ conference presentations. Scientific Awards: NTNU's Outstanding Academic Fellows Programme (2022-2026), DNVA Postdoctoral Scholarship (2015-2017) Supervised 20+ master’s students and 4 PhD candidates, including Jinjie Mao (CO2 storage optimization), Cuthbert Ng (data-driven reservoir modeling), and Behnam Tavagh Mohammadi (CO2 field storage modeling). Active in teaching reservoir simulation, CO2 storage engineering, and petroleum geoscience courses at NTNU.
Umit Cali is a Professor and Chair in Digital Engineering for Future Technologies at the University of York, UK, and holds a part-time Professor role in Energy Informatics at NTNU (Norwegian University of Science and Technology). He specializes in energy systems, blockchain, IT law, and data science, with over 20 years of experience in academia and industry. His research focuses on energy informatics, cybersecurity, and renewable energy integration. Education: PhD in Electrical Engineering and Computer Science (University of Kassel, Germany) and LL.M in IT and IP Law (University of Goettingen, Germany). Research Interests: Blockchain applications in energy systems, AI-driven energy management, cybersecurity for critical infrastructure, legal frameworks for digital technologies, and sustainable energy policies. Recent work emphasizes digital twin technology for energy systems optimization, decentralized energy markets, and ethical AI deployment. His publications span energy storage, smart grid cybersecurity, and policy analysis for renewable energy adoption. Professional Experience: Previously worked at IBM, Fraunhofer Institute, EnBW, and as an assistant professor at multiple universities. Serves as Vice Chair of the IEEE Blockchain in Energy Standards Working Group (P2418.5).
Nand Kishor is a Full Professor at the Department of Engineering, Østfold University College. His academic journey includes roles as Professor (2018–2021) and Associate Professor (2012–2018) at Motilal Nehru National Institute of Technology (MNNIT), Allahabad, India. He also served as an Associate Professor at the University of Agder, Norway (2017–2018) and a Marie Curie Experienced Researcher at Aalto University, Finland (2012–2013). Dr. Kishor holds a PhD in Power Systems (2006) from an Indian university, with research focusing on hydro turbine dynamics using neural networks and fuzzy techniques. His current research interests span renewable energy integration, smart grids, dynamic analysis, and ICT applications in power systems. He has supervised 11 PhD students, including 4 co-supervised students from MNNIT Allahabad. He has authored/co-authored three books: Modeling and Dynamic Behavior of Hydropower Plants , ICT for Electric Vehicle Integration with the Smart Grid , and Synchrophasor Technology: Real-time Operation of Power Networks . He serves as an editor for journals like IET Renewable Power Generation and IEEE Systems Journal . Key projects include the EU-funded PERSIST (Leading WP3) and Indo-EU SUSTENANCE projects. His work emphasizes interdisciplinary approaches to energy transition and grid resilience, with a focus on cyber-physical security and renewable integration.
Moon Keun Kim is a Professor in the Department of Building and Energy Engineering at Oslo Metropolitan University, Faculty of Technology, Art and Design. He is actively engaged in research on sustainable built environments, energy efficiency in buildings, and the application of artificial intelligence for energy prediction and HVAC optimization. He is affiliated with the Sustainable Built Environment (SustainaBuilt) research group. His research interests span building energy systems , indoor air quality , ventilation technologies , and climate-adaptive building design . He applies advanced data-driven methods such as LSTM and artificial neural networks to model and predict energy consumption in residential, commercial, and campus buildings. His work also extends to integrated energy systems, radiant cooling, hydrogen blending, and carbon trading mechanisms for urban energy planning. The recent publications demonstrate a strong trend toward using artificial intelligence and machine learning for optimizing building energy performance. Topics include data normalization in neural networks, multi-objective optimization of energy systems, and comprehensive reviews on AI in HVAC. His work bridges engineering fundamentals with sustainability goals, particularly in Nordic and climate-sensitive environments. Handbook of Ventilation Technology for the Built Environment Design, control and testing (2022) Research trends about the indoor environment in Norway (2023) International cooperation for BESS technology market entry (2024) Kim has authored a textbook and numerous scientific and dissemination publications, indicating active knowledge transfer. While no formal students or grants are listed, his collaborative publications suggest involvement in research teams and projects. He frequently presents at international conferences such as EKC and KICT symposia, contributing to global discourse on sustainable building technologies. He is associated with the Sustainable Built Environment (SustainaBuilt) research group, which focuses on advancing energy-efficient and environmentally responsible building systems through interdisciplinary research and innovation.
Marianne Bakke Johnsen is an Associate Professor at Oslo Metropolitan University's Faculty of Health Sciences, specifically within the Department of Rehabilitation Science and Health Technology. Her work bridges physiotherapy, musculoskeletal health, and advanced health technology while incorporating genetics and genomics into her research framework. Faculty of Health Sciences Department of Rehabilitation Science and Health Technology Research focus areas: physiotherapy, musculoskeletal disorders, digital health solutions Her research interests center on musculoskeletal health technology and digital interventions for spinal disorders, particularly scoliosis treatment. She leads projects like the HIPS trial comparing self-management approaches for hip pain and HEYoung intervention study for adolescent persistent pain. Her work emphasizes wearable sensors, AI-driven prediction models, and patient-centered care. Recent publications demonstrate expertise in: Digital health for adolescent scoliosis Clinical outcomes in orthopedic treatments Genetic links between brain disorders and pain Validation of assessment tools in physiotherapy Population health in musculoskeletal disorders Her projects frequently involve interdisciplinary collaboration across Scandinavia, utilizing machine learning, systematic reviews, and randomized controlled trial methodologies to improve conservative treatment outcomes.
Solveig Sand-Hanssen Hofvind is a Professor at UiT The Arctic University of Norway , specializing in Radiography and breast cancer screening research. She is affiliated with the Radiography BSc program and contributes to the Acute and Critically Ill research group . Research Focus : Artificial intelligence in mammography, epidemiology of breast cancer, personalized screening programs, and public health implications of AI in healthcare. Key Themes : Her recent publications analyze AI-driven cancer detection, mammographic density as a risk factor, immigrant screening accessibility, and pandemic-induced screening delays. Collaborations : Frequently collaborates with researchers in European Radiology , BreastScreen Norway , and international cancer screening initiatives like CanScreen5 . Scientific Awards : 2023: Hold Pustens article prize for research on radiography profession development in Norway. Recent Article Trends : Her 2023-2025 work emphasizes AI integration in mammography, with studies on algorithm performance, patient attitudes, and radiologist workflows. Epidemiological studies focus on menopause timing, weight changes, and aspirin use as breast cancer risk factors. Public health research includes immigrant screening disparities and pandemic impacts on healthcare.
Haidar Hosamo is an Associate Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, affiliated with the Department of Built Environment. His work focuses on building sustainability, energy optimization, and digital twin technologies. Research areas: BIM, occupant modeling, predictive maintenance Key tools: Machine learning, AI-driven sensitivity analysis Recent publications address machine learning for floating solar arrays, dynamic LCA uncertainty, 5D BIM adoption, and knowledge graph integration in heritage conservation. His work combines data science with civil engineering to enhance building performance and occupant comfort.
Mathias Hudoba de Badyn is an Associate Professor in the Department of Technology Systems (ITS) at the University of Oslo. He previously served as a postdoc at the Automatic Control Lab at ETH Zürich (2019-2023) working with John Lygeros and Roy Smith. His academic journey includes a PhD in Aeronautics and Astronautics from the University of Washington under Mehran Mesbahi, where he focused on distributed control theory. His research interests center on control and estimation of networked dynamical systems with emphasis on sustainability applications. Using algebraic graph theory, he investigates how network structure affects distributed control, estimation, and optimization algorithms. His primary application domains include smart buildings and energy hubs interconnected with the electric grid for demand-side management and peak shaving, as well as aerospace applications involving multi-vehicle systems from UAV swarms to distributed satellite systems. His recent publication trends show strong focus on distributed control algorithms applied to energy systems and aerospace applications, with increasing integration of machine learning techniques while maintaining theoretical rigor. His work spans from fundamental network theory to practical implementations in building control and spacecraft systems. Work package leader in CENSSS (Centre for Space Sensors and Systems) Involved with FME Solar Centre for Environmentally Friendly Research Active supervisor for numerous PhD and Master's students His research group has secured projects related to smart buildings, energy systems, and aerospace control, with recent work focusing on virtual power plants, spacecraft GNC, and AI validation in maritime automation. He maintains strong collaborations with institutions including ETH Zürich, University of Washington, and various industry partners.
Professor Aristidis Kaloudis is affiliated with the Norwegian University of Science and Technology (NTNU) as a faculty member in the Department of Information Security and Communication Technology. His teaching portfolio spans courses such as Digital Business Models , Prognostic Models , Quantitative Methods , and Broad Innovation Policy at bachelor’s, master’s, and PhD levels. 2024: IIK5000 Digital Law and Business 2023–2022: SMF3081 Prognostic Models for Industrial Markets His research focuses on Digital Innovation Policy , Regulatory Impacts on Cybersecurity , Lean Production Systems , and Decision Analysis in Organizational Contexts . Recent work examines AI-driven financial volatility prediction and cross-border cyber capacity building. Scientific contributions include: 2024: AI/ML analysis of volatility indices 2023: Systemism model for innovation systems 2022: Triad relationships in lean manufacturing 2018: Vaccine development cost modeling 2007: EU sectoral R&D heterogeneity
