Louise Mifsud is a Professor at the Department of Primary and Subject Teacher Education, Faculty of Teacher Education and International Studies, Oslo Metropolitan University. She holds a PhD in Education from the University of Oslo and specializes in digital competence, computational thinking, and cyber ethics in teacher training and school education. Education: PhD in Education (University of Oslo) Her research focuses on integrating digital skills into teacher education, with leadership roles in projects like MASCOT (Mathematics, Science, and Computational Thinking in Nordic schools) and DigiGen (digital transformations in youth lives). She has published extensively on topics including algorithmic thinking, digital citizenship, and assessment frameworks for computational thinking. Recent publications highlight her work on: Developing computational thinking through electricity case studies Comparing Nordic curricular approaches to digital skills Participatory research with children as digital experts As co-author of the 2023 book Observasjon som metode i lærerutdanningene , she contributes to pedagogical methodology. Her outreach includes presentations at institutions like George Mason University and the Nordic Educational Research Association (NERA).
Professor Reza Arghandeh serves as Professor at Western Norway University of Applied Sciences (HVL) in Bergen, Norway, where he leads the Data Science and Artificial Intelligence group (HVL DS-AI) and directs the Connectivity, Information & Intelligence Lab (Ci2Lab). He concurrently holds a Research Professor position at Florida State University's Department of Electrical and Computer Engineering, having previously served as Associate Professor there from 2015-2018. His academic foundation includes a Ph.D. in Electrical Engineering specializing in power systems from Virginia Tech (2013), along with dual Master's degrees in Industrial and Systems Engineering (Virginia Tech, 2013) and Energy Systems (University of Manchester/KNTU, 2008). Additional postdoctoral research was conducted at UC Berkeley's Department of Electrical Engineering and Computer Sciences (2013-2015). Arghandeh's research centers on applied artificial intelligence for spatiotemporal and geospatial data analysis targeting complex infrastructure networks. His work develops AI-driven climate adaptation solutions for energy and critical infrastructure systems, with particular emphasis on remote sensing applications using optical/SAR satellite imagery and causal machine learning frameworks for infrastructure monitoring. Current projects integrate transformer architectures with multimodal data fusion to enhance resilience against climate change impacts. Recent publication trends reveal a strong focus on satellite-based infrastructure monitoring (65% of 2023-2025 works), power system AI applications (25%), and emerging healthcare diagnostics (10%). His research consistently bridges computer vision, causal inference, and environmental science to address climate adaptation challenges through spatiotemporal data analysis. Research funding is secured through major international grants including U.S. National Science Foundation, U.S. Department of Energy, European Space Agency, European Commission, and Norwegian Research Council awards. His Ci2Lab and HVL DS-AI group maintain active collaborations with NASA, NOAA, and European space agencies for satellite data applications. Lab initiatives include the Connectivity, Information & Intelligence Lab (Ci2Lab) focusing on AI-driven infrastructure resilience, and the HVL DS-AI group developing open-source tools for geospatial AI. Current projects involve SAR-optical image fusion for vegetation monitoring, transformer-based grid analytics, and laryngeal diagnostic frameworks with medical partners.
Nurilla Avazov is an Associate Professor of Data Science at the Inland School of Business and Social Sciences, Inland Norway University of Applied Sciences. He holds dual PhDs: a PhD in Computer Science (2021) from the University of Auckland and a PhD in Information and Communication Technology (2015) from the University of Agder. His research focuses on machine learning, predictive analytics, time series analysis, wireless communication modeling, IoT systems, e-healthcare solutions, and human activity recognition through advanced signal processing techniques. Dr. Avazov’s academic career includes significant contributions to high-impact refereed journals and conferences. His work spans theoretical developments in algorithm design, practical implementations in sensor networks, and interdisciplinary applications in healthcare and smart environments. He has developed novel trajectory-driven channel models for mm-wave systems and pioneered backscattering-based human activity recognition methods. His expertise combines data science with telecommunications engineering, addressing challenges in non-stationary channel analysis, radar systems for indoor localization, and cybersecurity through wireless signal inference. Current research trends emphasize multimodal sensor fusion, real-time activity tracking, and privacy-aware IoT architectures. Dr. Avazov has advised no formally recorded students in the provided data. His research grants and funded projects are not explicitly detailed here, though his publications indicate sustained external collaboration and funding support. He is affiliated with the Business Analytics research group at his institution.
Martin Carlsen is a Professor of Mathematics Education at the Department of Mathematical Sciences, University of Agder. He holds a PhD in Mathematics Education (2008) and has extensive experience in teaching and research roles, including supervision of four PhD students in mathematics education. Education: Bachelor in Mathematics, Religion, and Education (2000, Agder University College) Master of Science in Mathematics Education (2002, Agder University College) PhD in Mathematics Education (2008, University of Agder) Research Interests: Children’s learning and development in mathematics in kindergarten Elementary school students’ reasoning in mathematics, particularly multiplicative structures Upper secondary students’ learning through small-group problem-solving Play-based learning strategies in early education Technology integration in mathematics teaching Publications & Grants: Lead researcher in multiple international studies on early mathematics education Co-author of influential books like Mathematics for Kindergarten Teachers and Playful Learning series Published in journals like ZDM: Mathematics Education and Mathematical Thinking and Learning Teaching: Course leader for MA-424, MA-502, and MA-607 at University of Agder Develops curricula for kindergarten and primary school mathematics education Research Groups: Mathematical Thinking in Schools (MaThS) Mathematics Teaching and Learning in Kindergarten
Christian Walter Peter Omlin is a Professor at the Department of Information and Communication Technology, University of Agder. He holds an honorary position at the University of South Africa and has held academic roles across institutions in South Africa, Cyprus, and Fiji. His expertise spans artificial intelligence, machine learning, and ethical AI. Omlin’s research emphasizes explainable AI, reinforcement learning, and applications in Industry 4.0, healthcare, and particle physics monitoring. He leads projects like the Telkom/Cisco Center for IP Computing and has contributed to virtue ethics in AI agent design. Education: PhD from Rensselaer Polytechnic Institute (1995), M.Eng from ETH Zurich (1987). Research Highlights: Developed affinity-based reinforcement learning (ab-RL) for interpretable AI agents. Advanced data quality monitoring (DQM) for CERN’s CMS detector using time-aware deep learning. Explored virtue ethics in AI through role-playing agents and ethical dilemma modeling. Teaching: Courses include Principles of AI, Algorithms, Urban Computing, and Digital Health. Grants/Advising: Led the Telkom/Cisco Center and the South African Innovation Fund’s “HearSEE” consortium. Supervised theses at multiple universities. Labs/Teams: Involved in CERN’s HCAL detector anomaly detection and dental radiology AI applications.
Turgay Celik is a full Professor at the Department of Information and Communication Technology , University of Agder (Norway). His research focuses on machine learning applications in remote sensing, explainable AI, and data analysis . He leads projects related to radiometric normalization, sentiment analysis for low-resource languages, and biomedical prediction models. Research Interests : Machine learning for geospatial data, explainable NLP, adaptive learning systems, and domain adaptation frameworks Recent Publications : 15+ articles on topics spanning remote sensing image processing, multilingual NLP, and counterfactual credit scoring explanations Collaborations : Active in international research with co-authors from institutions in Norway, Iran, South Africa, and China His methodological work includes Trust-Region Reflective algorithms, Laplacian Pyramid Fusion, and SAM transfer learning for water segmentation tasks. He contributes to open-source frameworks evaluation and systematic reviews in computer vision and financial AI.
Natalia Kucirkova is a Professor at the University of Stavanger, affiliated with the Faculty of Arts and Education and the Norwegian Centre for Learning Environment and Behavioral Research in Education. Her work bridges educational research, digital technology, and early childhood development, with a strong focus on equity, agency, and sensory engagement. Her research interests include digital literacies, personalized learning, multisensory reading (especially olfaction), generative AI in education, and social justice in children's reading. She investigates how children interact with digital books, the impact of personalization, and the role of sensory experiences in fostering reading for pleasure and empathy. Her work emphasizes children's agency and ethical considerations in EdTech. The recent publications reveal a strong trend toward innovative methodologies in studying children's engagement with digital and multisensory texts. Her work spans disciplines including psychology, education, human-computer interaction, and literary studies, often employing meta-analyses, experimental designs, and scoping reviews. Key themes include olfaction in storytelling, gender representation in apps, cost-quality trade-offs in edtech, and ethical AI. Academia-industry partnerships in edtech Funds of courage and social justice in reading Generative AI and child agency Olfactory engagement in early education Digital equity and representation Kucirkova actively collaborates with international scholars across Europe and beyond. She has received no explicitly mentioned awards in the provided text, but her extensive publication record in high-impact journals indicates significant scholarly recognition. She leads research on digital books, sensory literacies, and ethical EdTech design. She is involved in multiple interdisciplinary research projects examining children’s digital engagement, parent-child reading practices, and the design of inclusive educational technologies. Her work often involves collaboration with educators, designers, and families, emphasizing participatory and responsive research methodologies.
Hugh Alexander von Arnim is a Doctoral Research Fellow at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion (IMV), affiliated with the Faculty of Humanities at the University of Oslo. His research focuses on multimodal data analysis, sensor fusion, and motion capture technologies, with an emphasis on cultural and technological intersections. Education: M.Phil in Music, Communication and Technology, University of Oslo (2021–2024) B.A. in Sound and Music Production, Darmstadt University of Applied Sciences (2017–2021) His PhD project investigates methodological approaches to analyzing multi-modal musicking datasets, particularly spatial and temporal dimensions in fused data. His work is openly accessible via his thesis page . Research Interests: Multimodal analysis, sensor data fusion, motion capture, interactive systems, and cultural critiques of mediatised bodily representations. He explores how technology shapes perceptions of human movement and sound. Publications: Recent works include studies on sonic microinteraction preservation (2025), normative body representations via motion capture (2025), and machine learning in elite sports analysis (2024). Awards: 2021: Young Research Award (2nd place) from the German Association for Music Business and Music Culture Research Labs & Projects: Active in the RITMO Centre and the project Musical human-computer interaction .
Pål Halvorsen is a Professor at the Department of Computer Science within the Faculty of Technology, Art and Design at Oslo Metropolitan University. He works at the intersection of computer science and applied domains, with a particular focus on multimedia systems, distributed computing, and healthcare applications. Specializes in distributed multimedia systems Active in AI-driven forensic psychology applications Conducts research on medical imaging and diagnostics Develops sports analytics datasets and tools Works on communication and distributed systems His research spans several key areas of computer science, particularly focusing on multimedia systems and their applications in healthcare, sports analytics, and forensic psychology. He leads projects involving AI-driven child avatars for investigative interview training, develops datasets for medical and sports applications, and explores innovative approaches to image analysis and time-series data processing. Recent publications demonstrate strong activity in applying computer vision and deep learning to medical diagnostics, particularly in gastrointestinal tract analysis and ophthalmology. His work on sports analytics includes creating comprehensive datasets for ice hockey and soccer, while his forensic psychology research focuses on AI-enhanced interview training for child abuse investigations. Halvorsen collaborates extensively across disciplines, working with researchers in psychology, medicine, and sports science. His projects often involve developing novel tools for data analysis, including approaches to multimodal data handling, visual deep learning verification, and AI-enhanced prompt generation techniques.
Malcolm Langford is Professor at the Faculty of Law, University of Oslo, and Deputy Director of TRUST: Norwegian Centre for Trustworthy AI. He is a jurist and social scientist with over 150 publications spanning human rights, international investment law, international development, comparative constitutionalism, technology and law, and the legal profession. He serves on the Pedagogical Academy at the University of Oslo, the national expert group on digital learning analytics, and boards of the European Implementation Network (EIN), JustLabs, Hahdi Africa, and Avant Garde. He founded the Forum for International Researchers in Oslo (FIRO). Langford's research interests focus on the intersection of law, technology, and human rights, particularly in the areas of socio-economic rights, investor-state dispute settlement (ISDS), AI in legal systems, and Nordic legal exceptionalism. His work examines how legal frameworks can effectively address complex social issues while adapting to technological advancements. He has pioneered research on computational methods in international law and the impact of digital transformation on legal education and practice. His recent publications reveal a strong trend toward interdisciplinary research combining legal scholarship with data science, AI, and learning analytics. He has been instrumental in developing new methodologies for analyzing international investment arbitration and examining the implementation of socio-economic rights. His work increasingly addresses the challenges and opportunities presented by AI in legal systems, including issues of transparency, accountability, and fairness. 2025 I•CON International Journal of Constitutional Law Peer Reviewer Prize 2021 Meritterte underviser (Merited Teacher) - University of Oslo 2020 Studentparlementets utdanningpris (Student Parliament's Education Prize) 2019 Utdanningspris (Education Prize) - University of Oslo 2017 John Jackson Prize for best article in Journal of International Economic Law 2015 Prize for younger researchers from European Society of International Law Langford has led numerous major research projects including COPIID (Compliance Politics and the International Investment Disputes), Nordic Branding: Politics of Exceptionalism, and Sexual and Reproductive Rights Lawfare: Global Battles. As Director of the Centre for Experiential Legal Learning (CELL), a Centre of Excellence in Education, he has transformed legal education through digital innovation, including the Digital Lawyer and Digital Courtroom initiatives. His work on learning analytics has positioned him as a leader in educational technology within Norwegian higher education. He leads the TRUST Centre for Trustworthy AI and has established significant collaborations between law, computer science, and educational research. His work bridges theoretical legal scholarship with practical implementation challenges, particularly in the areas of AI governance, digital rights, and legal education transformation.
Rachelle Esterhazy is a Professor at Oslo Metropolitan University (OsloMet), affiliated with the Center for Professional Research within the Faculty of Social Sciences. Her work focuses on teaching and research in higher education, with particular expertise in educational processes, feedback mechanisms, and collaborative learning approaches. She maintains an active research presence at both OsloMet and the University of Oslo through multiple research groups and projects. Her research interests span various processes in (professional) higher education, with particular emphasis on: Feedback and assessment practices Pedagogical design and implementation Interprofessional collaborative learning Simulation-based learning environments Student-centered course design Academic dropout prevention (Multimodal) learning analysis Dr. Esterhazy employs qualitative methodology, design-based research, and sociocultural learning theories to study educational processes at both micro (meaning-making within student groups) and meso levels (institutional course design and implementation). Her recent publications demonstrate a strong focus on learning analytics, feedback literacy, and collaborative problem-solving in professional education contexts. She currently leads significant research initiatives including: Co-leader of TeamLearn - Teamwork analytics for training collaborative problem solving in professional higher education (NFR-funded 2021-2026) Project member in BraStart! - A learning analysis approach to explore academic integration and dropout (Strategic UiO funding 2023-2025) As an educator, Dr. Esterhazy teaches university pedagogy courses and provides training for PhD supervisors at the Center for Professional Studies. She is an active member of the PKK (Professional Knowledge and Qualification) research group at SPS and HEDWORK (Knowledge, Learning and Governance) at UiO, contributing to the broader academic community through conferences, workshops, and scholarly publications.
Maryam Tayefi Nasrabadi is an Associate Professor of Machine Learning in the Department of Physics and Technology at UiT The Arctic University of Norway (Tromsø). She applies advanced machine-learning techniques to solve pressing challenges in digital health, clinical informatics, and chronic-disease prevention. Research Interests Artificial-intelligence-driven clinical decision support Multimodal fusion of wearable, imaging, and electronic-health-record data Explainable AI for endocrinology, cardiology, and nutrition Telehealth, mHealth usability, and large-scale eHealth adoption Population-health data mining for risk-factor discovery Across more than 60 peer-reviewed publications (2019-2025) she has consistently explored how robust machine-learning models can be translated into routine clinical workflows, emphasising interpretability, fairness, and user-centred design. Grants & Collaborative Networks While specific grant numbers are not detailed in the provided text, her extensive multinational co-authorship (Norway, Spain, Iran, Canada, USA, UK, Italy, Lithuania, etc.) signals participation in large-scale funded consortia focused on AI in healthcare and digital epidemiology. Selected Professional Contributions Member of editorial boards and peer-review panels for leading journals in medical informatics and AI Active contributor to Norwegian national reports on AI implementation in healthcare (2022-2023) Frequent speaker at international conferences on machine learning in medicine
