Molly Maleckar is a Research Professor at the Computational Physiology Department of Simula Research Laboratory , Oslo, Norway. Her work bridges computational modeling, cardiac electrophysiology, and biomedical applications, with a focus on arrhythmia mechanisms, fibrosis modeling, and machine learning integration in cardiac risk prediction. Research Interests include: Computational Cardiology Ion Channel Dynamics Machine Learning in Medicine Excitable Tissue Modeling Cardiac Fibrosis Analysis Biomedical Simulation Scientific Contributions span 15+ publications (2018-2024) addressing atrial fibrillation, calcium handling, and AI-driven ECG analysis. Key collaborative projects involve patient-specific ventricular modeling and educational initiatives like the Simula Summer School in Computational Physiology .
Snorre Aunet is a Professor at the Department of Electronic Systems at the Norwegian University of Science and Technology (NTNU), with additional roles as an Adjunct Professor at the Department of Informatics, University of Oslo. His academic career spans institutions in Norway, Germany, and the USA, including sabbaticals at Bielefeld University and University of California, San Diego. Education: Electronics Engineering (Trondheim Technical College, 1987), Informatics (UiO, 1993), and Physical Electronics (NTNU, 2002) Research Focus Dr. Aunet specializes in ultra-low-power mixed-signal integrated circuits and nanoscale CMOS technologies . His work includes innovative MEMS transducer designs for energy harvesting, digital IC development, and robust circuit solutions for emerging semiconductor challenges. Recent Publications His recent contributions include advancements in MEMS technology for sustainable energy systems, editorial leadership at NorCAS conferences, and policy discussions on the European Chips Act. These reflect expertise in both cutting-edge design and strategic technology development. Scientific Recognition 2019: Elected to Norwegian Academy of Technical Sciences (NTVA) 2015: Best Paper Award at Asia Symposium on Quality Electronic Design 2006: IEEE Senior Member (Electron Devices Society) 2003: Best Poster Award at Int'l Conf. on Evolvable Systems Leadership & Grants He has chaired multiple IEEE conferences and contributed to projects like Robust Ultra-Low-Power Circuits for Nano-Scale CMOS (NTNU/University of Paderborn). His career combines academic leadership with industry collaboration through Nordic Semiconductor and international research councils.
Helge Langseth is a Professor at the Department of Computer Technology and Informatics , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). His research focuses on Artificial Intelligence , Machine Learning , and Probabilistic Graphical Models , particularly Bayesian Networks and their applications in Decision Support Systems . Langseth's work addresses Explainable AI (XAI) , Reinforcement Learning , and Recommender Systems . He has contributed to Bayesian Optimization , Probabilistic Modeling , and Robotic Control in oceanic environments. His recent publications emphasize transparency , fairness , and scalability in AI systems, with applications spanning maritime trade, migraine diagnosis, and power grid management. He is affiliated with the Intelligent Systems Research Group at NTNU and actively mentors doctoral and master's students. Co-authored works with Yanzhe Bekkemoen , Sverre Herland , and Jørgen Hanssen reflect his role in advising the next generation of AI researchers.
Guangyu Cao is a Professor at the Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU). He holds leadership roles in multiple international organizations, including the European standards working group CEN TC156 WG18, REHVA's technical committee, and the editorial board of the Journal of Building Engineering (Impact Factor 5.318). His research focuses on indoor airflow dynamics, ventilation systems in healthcare settings, and airborne disease transmission mitigation, with a strong emphasis on surgical environments and school buildings. Education: PhD in Energy Engineering (2009), Helsinki University of Technology Senior Researcher at VTT Technical Research Center (2009-2014) Research Interests: Hospital ventilation optimization, thermal comfort in clinical settings, airborne infection control, and sustainable building environmental quality. He combines experimental studies with mathematical modeling to evaluate ventilation strategies, particularly in operating rooms and isolation wards. Projects: EU Marie Curie DTN HumanIC (2024–2027) EU H2020 iclimabuilt (2021–2025) NFR POSIred (2020-2024) Labs/Teams: Collaborates with St. Olav's Hospital on indoor environment projects and leads teams in Cold Climate HVAC and Healthy Building Europe initiatives.
Lars O. Nord is a Professor in the Department of Energy and Process Engineering at NTNU, specializing in thermal energy systems, CO2 capture technologies, and dynamic process modeling. He holds a PhD from NTNU (2010) and a Master's from Virginia Tech (2001). His research focuses on power cycles, turbomachinery optimization, and decarbonization strategies for energy systems. Nord has led projects such as DEXPAND and InnCapPlant, addressing CO2 capture under variable loads and expander efficiency in renewable systems. Current roles: Head of the Thermal Energy research group and teaches courses like Engineering Thermodynamics. Research highlights include thermal energy storage integration, moving bed adsorption processes, and offshore hybrid energy systems. His work spans over 80 publications, emphasizing CO2 capture dynamics, turbine design, and control strategies for flexible power plants. Notable collaborations include SINTEF and Aker Solutions. Nord advises multiple PhD candidates and has mentored alumni now leading roles in industry and academia.
Katrine Eldegard is a Professor at the Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), Department of Ecology and Natural Resource Management (INA). She leads BatLab Norway and contributes extensively to national and international conservation science policy. Institution: Norwegian University of Life Sciences School: Faculty of Environmental Sciences and Natural Resource Management Department: Department of Ecology and Natural Resource Management Position: Professor Her research centers on understanding how human activities and land use affect natural ecosystems and species across taxa and spatial scales. She specializes in the behavioral, population, and community-level responses of mammals, birds, and insects to anthropogenic pressures such as energy infrastructure, transport networks, and forestry. A major focus is on bat ecology and conservation, pollination dynamics, and biodiversity monitoring in boreal and agricultural landscapes. Her recent publications reveal strong trends in climate change impacts on bat morphology and distribution, pollinator-plant interactions under environmental change, and the ecological consequences of infrastructure development. These works integrate field ecology with advanced modeling and policy-relevant assessments. She has played leading roles in key scientific committees: Chair, Mammal Committee, Norwegian Red List for Species (2021) Chair, Mammal Committee, Norwegian Alien Species List (2023) Member, Norwegian Scientific Committee for Food and Environment (VKM), CITES Expert Panel Norway’s representative, UNEP/Eurobats Advisory Committee Eldegard has supervised numerous research projects and collaborated with government agencies and private partners on applied ecology. She teaches courses including NATF200 Vern og forvaltning av norsk natur and the upcoming NATF300 Conservation Science. Her work is supported by extensive fieldwork, interdisciplinary collaboration, and integration of ecological theory with practical conservation. She leads BatLab Norway, a research group dedicated to advancing knowledge on bat ecology, behavior, and conservation through innovative methods including telemetry, acoustic monitoring, and landscape analysis.
Gabriele de Seta is a researcher at the University of Bergen's Department of Linguistic, Literary and Aesthetic Studies, leading the ALGOFOLK project funded by the Trond Mohn Foundation (2024-2028). A sociologist by training with a PhD from Hong Kong Polytechnic University, he previously served as postdoctoral fellow at Academia Sinica (Taipei) and on the ERC-funded Machine Vision in Everyday Life project. Department of Ethnology, Academia Sinica (postdoctoral fellow) Department of Linguistic, Literary and Aesthetic Studies, University of Bergen (researcher) His research focuses on algorithmic folklore, synthetic ethnography, and Chinese digital culture. He examines how digital technologies and creative practices mutually shape each other, particularly in China's sociotechnical landscape. Key areas include platform governance, machine vision, algorithmic accountability, and digital vernaculars. Recent publications analyze TikTok/Douyin's infrastructural role, deepfakes, QR codes as globalized gateways, and Chinese internet vulgarity. His work combines theoretical frameworks from STS, media studies, and digital anthropology with fieldwork on Chinese digital practices. Trond Mohn Foundation Starting Grant ERC-funded research As educator, he designed the Machine Vision module for Bergen's Critical Approaches to Technology and Society course and taught Key Theories in Digital Culture. He supervises multiple MA/PhD students including co-supervision of Hanna Lauvli. Through his blog and podcast, he promotes public engagement with digital research.
Filippo Maria Bianchi is an Associate Professor in the Department of Mathematics and Statistics at UiT The Arctic University of Norway, where he conducts research at the intersection of machine learning, dynamical systems, and complex networks. He is also a Senior Researcher at NORCE Norwegian Research Centre and actively contributes to the IEEE Task Force on Learning for Structured Data and the ELLIS Society. Department: Department of Mathematics and Statistics School: Faculty of Science and Technology University: UiT The Arctic University of Norway Adjunct Position: Senior Researcher, NORCE Education: Bachelor’s in Computer Engineering, Sapienza University of Rome Master’s in Artificial Intelligence & Robotics, Sapienza University of Rome (cum laude, 2012) PhD in Machine Learning, Sapienza University of Rome His research focuses on graph machine learning, time series analysis, reservoir computing, and probabilistic forecasting , with applications in energy analytics and remote sensing. He has led and contributed to numerous projects involving Arctic power grids, satellite-based environmental monitoring, and deep learning for sustainability. The recent publications reflect a strong trend in graph neural networks —particularly pooling mechanisms, spatiotemporal modeling, and explainability—alongside applications in energy forecasting, avalanche detection, and remote sensing . His work combines theoretical innovation with real-world impact, especially in Arctic and remote environments. Scientific Affiliations and Leadership: Vice-Chair, IEEE Task Force on Learning for Structured Data Member, ELLIS Society Co-founder, Northernmost Graph Machine Learning group Member, IEEE Task Force on Reservoir Computing Visiting Professor, Politecnico di Milano (2024–2025) He actively mentors students and collaborates on interdisciplinary research. He has led projects in power grid reliability, solar fault detection, and unsupervised change detection in satellite imagery . His work is supported by open-source implementations and reproducible research practices. Laboratories and Research Groups: Northernmost Graph Machine Learning group (co-founder) ARC Research Group, UiT Graph Machine Learning Group, Lugano
Roy Johnsen is a Professor in the Department of Mechanical and Industrial Engineering at the Norwegian University of Science and Technology (NTNU), specializing in corrosion and surface technology. With a Dr.ing. degree from NTH (1984), he has extensive industry experience from Statoil Research Centre (1985-1991) and CorrOcean (1991-2004), where he expanded the company globally. His current research focuses on hydrogen embrittlement, corrosion protection, and integrity management in offshore systems, with collaborations across Europe, Asia, and the Americas.
Dieu Tien Bui is a Full Professor in the Department of Business and IT at the University of South-Eastern Norway (USN) School of Business. His research focuses on Geospatial Artificial Intelligence Machine Learning GIS and Remote Sensing Natural Hazard Modeling Environmental Problems (landslides, floods, soil salinity, biomass) . He has contributed to over 15 recent publications in journals like Science of the Total Environment , Remote Sensing , and Geomorphology , emphasizing hybrid AI models for landslide and flood susceptibility. His work spans Vietnam, India, China, and Iran with applications in climate change adaptation and disaster management. Scientific Awards: Global Highly Cited Researcher PhD Supervision: He has supervised 8 PhD students at institutions including USN, NTNU, and Vietnamese universities.
Professor Kenneth Ruud is a leading expert in theoretical and computational chemistry at UiT The Arctic University of Norway. He serves as Director General of the Norwegian Defence Research Establishment and leads the Hylleraas Centre for Quantum Molecular Sciences. His research focuses on relativistic quantum chemistry, developing advanced ab initio methods for molecular property calculations, and integrating QM/MM and continuum solvent models. Education: PhD from University of Oslo (1998, supervised by Trygve Helgaker) Postdoc: University of San Diego with Peter Taylor (1998-2000) His work spans relativistic effects in molecular properties, vibronic coupling, and X-ray spectroscopy. He contributes to software development through programs like Dalton, Dirac, ReSpect, and OpenRSP. Recent publications highlight applications in spin-vibronic dynamics, heavy metal L/M-edge XAS, and topological materials. Key scientific contributions include relativistic DFT for nuclear spin-rotation constants, polarizable embedding models for vibrational spectra, and quantum dynamics frameworks. Awards recognize his impact through the Dirac Medal (2008) and multiple academy memberships. Elected to Norwegian Academy of Science and Letters Fellow of American Association for the Advancement of Science (AAAS) Foreign member of Finnish Academy of Science and Letters He actively participates in open science initiatives and serves on boards including Norges Forskningsråd and CAROS center for subsea robotics. Current projects involve quantum molecular science in extreme environments and computational protocol development.
Jianhua Zhang is a Professor of Computer Science and founding deputy head of the AI Lab at the Department of Computer Science, OsloMet - Oslo Metropolitan University, Norway. He holds affiliations with the Faculty of Technology, Art and Design. His career includes roles as Scientific Director at Vekia (France), Head of Machine Learning Lab, and Professorships at East China University of Science and Technology and Beijing University of Technology. He has held visiting positions at TU Berlin, TU Dresden, and the University of Catania. Educations: PhD in Electrical Engineering and Information Sciences (Ruhr University Bochum, 2005), Postdoctoral Research at the University of Sheffield (2005-2006). Research focuses on artificial intelligence, computational intelligence, cognitive human-machine systems, neuroergonomics, affective computing, and AI-driven neuroergonomics. Applications span engineering, biomedicine, finance, and business. He has led over 20 large-scale projects and published extensively (4 books, 13 chapters, ~200 papers). Leadership roles include Chair of IFAC Technical Committee on Human-Machine Systems (2017-2023), Vice Chair of IEEE Norway Section, and editorial roles at journals like Frontiers in Neuroscience and Cognitive Neurodynamics . He organized major conferences like IFAC HMS2025 (Beijing) and ICMLT 2024 (Oslo). Awards: Stanford/Elsevier Top 2% Scientists (2023/2024), Senior Research Fellowship (CSC, 2012), Max Planck Fellowship (2011), Shanghai Pujiang Talent (2007), DAAD Scholarship (2002-2004). Grants and advising: PI for 20+ projects, advising PhD students in AI, machine learning, and control systems. Teaching includes courses on computational intelligence, IoT, and fuzzy systems at both undergraduate and graduate levels. Labs/Teams: AI Lab at OsloMet, Machine Learning Lab (Vekia), and collaborations with institutions globally. Current work emphasizes AI ethics, neuroergonomics in smart cities, and adaptive human-machine systems.
Børge Rokseth is an Associate Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His work focuses on maritime systems, autonomous vessel control, and safety verification. He actively supervises Master's students and contributes to research on risk-informed control systems, hybrid power systems, and systems-theoretic process analysis (STPA). Research Interests: Rokseth's research spans autonomous ship systems, dynamic risk assessment, and safety verification. He explores risk-based decision-making for maritime autonomy, hazard identification in hybrid propulsion systems, and control function allocation in dynamic positioning. His work integrates systems theory, machine learning, and regulatory compliance (e.g., COLREGS) to enhance safety and environmental performance in marine operations. Publications: His recent work includes probabilistic trajectory prediction frameworks for autonomous ships, STPA-based safety analyses, and studies on decarbonization barriers in the maritime industry. These publications emphasize risk modeling, systems-theoretic approaches, and simulation-based verification. Teaching: Rokseth teaches courses such as TTK4130 - Modelling and Simulation, contributing to the education of future engineers and researchers in cybernetics and maritime systems.
Staal A. Vinterbo is a Professor at the Department of Information Security and Communication Technology within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). His research focuses on privacy-preserving technologies, cryptography, and their intersections with machine learning, bioinformatics, and medical informatics. He has contributed to advancements in differential privacy, data anonymization, and secure computational methods.
Ronny Scherer is Center Director and Professor at CEMO (Center for Educational Measurement) and Deputy Director at CREATE (Center for Research on Equality in Education) at the University of Oslo's Faculty of Educational Sciences. His work bridges educational measurement, assessment, and evaluation with a focus on research syntheses and complex sampling surveys. Dr. Scherer's research spans two interconnected domains: substantive areas including digital divides, equity and equality in education, and measurement of complex cognitive skills (such as complex problem solving, adaptability, computational thinking, and executive functioning); and methodological areas focusing on advanced meta-analytic techniques, multilevel structural equation modeling, and spatial analysis of complex survey data. His work frequently utilizes international large-scale assessment data from PISA, ICILS, TIMSS, PIRLS, PIAAC, and TALIS. His publication record demonstrates a clear trajectory toward increasingly sophisticated meta-analytic approaches, with recent work focusing on second-order meta-analyses, AI-assisted screening methods, and advanced techniques for handling complex survey data. His research consistently addresses critical educational challenges related to equity, digital literacy, and measurement of 21st century skills. Dr. Scherer has secured significant research funding for projects including ARISE (Academic resilience in mathematics and science among vulnerable students), DiDiRes (Digital inequalities in education), and ADAPT21 (Educational assessments of the 21st century: Measuring and understanding students' adaptability in complex problem solving situations). Co-director of CREATE (Centre for Research on Equality in Education) since 2023 Professor of Educational Assessment and Measurement at CEMO since 2019 Extensive experience with international large-scale assessments including ICILS, TALIS, and PIAAC As an educator, Dr. Scherer teaches advanced courses in measurement models, multilevel models, meta-analysis, and equity in education. He actively supervises graduate students interested in his research areas and has developed numerous workshops on structural equation modeling and meta-analytic methods for international audiences.
