Assoc. Prof. Tosin Daniel Oyetoyan is affiliated with the Department of Computing, Mathematics, and Physics at the Western Norway University of Applied Sciences. His academic role focuses on software engineering, cybersecurity, and marine data systems. He leads research in software architecture design, IoT security, and environmental data management. His work bridges theoretical software principles with practical applications in marine technology and agile methodologies. Research Interests: Software architecture patterns for heterogeneous systems Cybersecurity in IoT and cloud environments Data observability and quality in marine sensor networks Empirical analysis of developer security practices API specification repositories and governance His recent publications (2020-2025) emphasize software systems for marine data observatories, cybersecurity in agile teams, and formal specification of environmental monitoring platforms. Noteworthy trends include cross-disciplinary approaches combining software engineering with environmental science. Scientific contributions include frameworks for threat analysis in IoT, empirical studies on developer security competencies, and modular smart ocean observatory designs. His work on data quality requirements for marine systems has informed international standards development. Current projects focus on: Formal specification of marine data platforms Executable architectures for environmental monitoring Agile security practices in distributed teams
Fabrizio Palumbo is an Associate Professor at the Department of Information and Communication Technology, University of Agder (UiA). His research spans artificial intelligence, machine learning, energy systems optimization, and computational neuroscience, with notable contributions to text classification, cybersecurity protocols, and zebrafish behavioral studies. He actively publishes in interdisciplinary venues including AI conferences and neuroscience journals. Key research areas include developing efficient neural network architectures for text analysis, exploring multilingual BERT models, optimizing energy storage systems via neural networks, and investigating neural mechanisms in zebrafish using behavioral protocols. His work bridges theoretical advancements with applied contexts, such as AI in investigative journalism and cybersecurity applications. Palumbo collaborates across disciplines, contributing to both technical and applied research. Notable recent publications address AI project methodologies in journalism and energy system optimization strategies.
Nikolay Arefev is a Research Fellow at the Department of Informatics , University of Oslo . He is affiliated with the Language Technology Group (LTG) and works on the High-Performance Language Technology (HPLT) project. His research focuses on computational linguistics, semantic change modeling, and multilingual natural language processing. Education : Not explicitly stated in the text, but his role implies advanced academic background in computer science or computational linguistics. Research Interests : Arefev's work spans computational linguistics , semantic change detection , word sense induction , and machine learning . He develops models for cross-lingual applications and semantic analysis, contributing to projects like the HPLT dataset. Articles : His recent work explores semantic change using lexical substitution, multilingual models, and transformer-based approaches. Key contributions include diachronic word sense annotations and benchmarking tools for semantic tasks. Labs/Teams : Active in the LTG group, collaborating on projects like HPLT and LSCDiscovery. His work often involves international conferences and shared tasks (e.g., SemEval).
Ryan Anthony Marinelli is a Doctoral Research Fellow at the Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, specializing in digital security with a focus on AI-cybersecurity intersections. His work addresses critical vulnerabilities in emerging technologies while contributing to institutional research objectives in secure computing. Marinelli's research spans AI Security , Adversarial Machine Learning , and Responsible AI Development , examining prompt injection attacks, knowledge leakage in large language models, and cryptographic deployment challenges. His methodology integrates causal tracing , reinforcement learning , and chain-of-thought analysis to develop detection frameworks and optimization strategies for real-world security applications. Analysis of his 2023-2025 publications reveals concentrated expertise in LLM security assessment, with recurring themes of societal risk mitigation and ethical AI development. His work demonstrates technical depth in homomorphic encryption optimization and password leakage evaluation, while maintaining strong connections to practical cybersecurity implementations through IEEE conference contributions and arXiv preprints. As part of UiO's Department of Informatics research ecosystem, Marinelli collaborates across multidisciplinary teams including cybersecurity specialists and AI researchers. His affiliation with the Faculty of Mathematics and Natural Sciences positions him within Norway's leading hub for digital security innovation, though specific laboratory structures aren't detailed in the source material.
Gustavo Borges Moreno e Mello is an Associate Professor in the Department of Computer Science at Oslo Metropolitan University's Faculty of Technology, Art and Design. Based in office SG209 at Stensberggata 29, Oslo, he actively contributes to the Applied Artificial Intelligence research group with focus on cutting-edge computational methodologies. His research spans artificial intelligence with emphases on graph neural networks, multilingual NLP, reinforcement learning, and medical imaging. Key interests include developing propagation schemes for GNNs, cross-lingual BERT adaptations, rodent fMRI analysis, and empathy assessment in human-computer interaction systems. His work bridges theoretical AI with practical healthcare and communication applications. Analysis of his 2020-2024 publications reveals strong thematic continuity in neural architecture design, particularly HITS-GNN variants for graph analytics and time-representation models. Recent work increasingly integrates neuroscience principles with reinforcement learning while maintaining Nordic language processing applications, notably in English-Norwegian BERT alignment. No information regarding graduate student supervision or research grants was found in the provided materials. He maintains active collaboration within OsloMet's Applied Artificial Intelligence research group, focusing on interdisciplinary projects that connect computer vision, natural language processing, and neuro-inspired computing with real-world healthcare and communication challenges.
Ricardo Colomo-Palacios is a Full Professor at the Department of Computer Science and Communication at Østfold University College, Norway. He coordinates the DigiTech research group associated with the Digital Society research priority area. Previously, he worked at Universidad Carlos III de Madrid, Spain. His educational background includes a PhD in Computer Science from Universidad Politecnica of Madrid (2005) and an MBA from the Institute of Empresa (2002). He has professional experience as a Software Engineer, Project Manager, and Software Engineering Consultant, including work at INDRA, a leading Spanish IT company. Colomo-Palacios' research spans several interconnected areas focusing on human aspects of computing and software engineering. His primary interests include Human Factors in Computing, Project Management in Information Systems Development, Global & Distributed Software Engineering, and Systems, Services and Software Process Improvement & Innovation. He also investigates Management Information Systems, Computing Profession Studies, Business Software, and Innovation in IT, with recent work emphasizing cybersecurity applications in critical infrastructure. His recent publications demonstrate a strong focus on cybersecurity applications in critical infrastructure, DevSecOps frameworks, visual analytics for supply chains, and social robotics security. The research shows a clear trend toward integrating security practices throughout the software development lifecycle while addressing real-world challenges in critical systems and infrastructure. He leads several significant research projects including DigiMat (Smart, transparent and sustainable food supply chains), EduTech (Technological assistance to accessibility in Virtual Higher Education), RECYCIN (Reinforcing Competence in Cybersecurity of Critical Infrastructures), and SecuRoPS (User-centred Security Framework for Social Robots in Public Space). As Coordinator of the DigiTech research group, Colomo-Palacios oversees research initiatives within the Digital Society priority area. His work bridges academic research with practical applications in industry, particularly in the areas of software engineering process improvement and cybersecurity for critical infrastructure.
Vikash Katta is an Associate Professor (part-time) at the Department of Computer Science and Communication, Østfold University College. His academic interests focus on Cybersecurity and Security Systems Engineering , with expertise in risk management, industrial control systems, and cyber-physical systems. He teaches courses like ITI42220 Cybersecurity Risk Management and Incident Response (Spring 2024). Prior roles include Senior Researcher at the Institute for Energy Technology (IFE) from 2006 to 2021, and Research and Innovation Manager at Blender Collective (2021–2022). He has led EU- and Research Council-funded projects in air traffic management, energy, robotics, and Smart City domains, emphasizing cybersecurity and risk management. His research explores traceability in safety-critical systems, Bayesian network frameworks for root cause analysis, and digital twin technology for incident prediction. He is affiliated with the Network for Cybersecurity and Innovation and the Information Systems and Software Engineering research group.
Jan Ketil Arnulf is a Professor at the Department of Leadership and Organizational Behaviour at BI Norwegian Business School. His research focuses on leadership, organizational behavior, and the application of semantic algorithms to analyze survey responses and corporate governance. Key areas of expertise include ethical investing, sustainable business practices, and cross-cultural management. He has authored numerous articles in journals such as Journal of Vocational Behavior and Frontiers in Psychology , and co-edited books on military leadership and organizational learning. Arnulf frequently contributes to public discourse through podcasts and media columns, addressing topics like leadership challenges in the digital age and corporate transparency. His work explores how language and semantics shape survey data, challenging traditional psychometric approaches. Notable projects include analyzing ESG reporting in Norwegian companies and investigating the long-term impact of academic performance on career success. Arnulf’s research often bridges psychology, management, and sociology, emphasizing practical relevance for organizational practices and policy. Much of his recent work examines leadership in diverse contexts—from corporate boards to military organizations—and the cognitive biases influencing decision-making. He collaborates internationally, particularly in China, to explore cross-cultural management theories. Despite extensive publications, no formal scientific awards are explicitly mentioned, though his contributions have significantly influenced leadership development methodologies. Arnulf’s interdisciplinary approach combines empirical studies with theoretical insights, aiming to improve organizational effectiveness while critiquing the limitations of conventional research frameworks.
Professor Mila Dimitrova Vulchanova is affiliated with NTNU's Department of Language and Literature within the Faculty of Humanities. Her research focuses on language acquisition, language processing, and neurodevelopmental disorders, particularly autism spectrum disorders (ASD). She leads major international projects like SCALA (HorizonEurope-UKRI), MultiplEYE (COST Action), and e-LADDA (MSCA ITN). Her work bridges linguistic theory, cognitive science, and clinical applications, emphasizing multilingualism and intervention strategies for developmental language deficits. Recent research explores figurative language processing in ASD, spatial communication universals, and the interplay between statistical learning and language development. She co-directs the Language Acquisition and Language Processing Lab and has coordinated several EU-funded initiatives addressing multilingualism and STEM education. Her publications span journals like Nature Human Behaviour , Autism Research , and Journal of Neurolinguistics , reflecting her interdisciplinary approach to language and cognition. Key grants include the NFR FRIHUM project on situated reference in language acquisition and the EDULANG grant focusing on multilingual education. She actively engages in outreach, including public lectures on language development and digital media's role in education.
Vladislav Mikhailov serves as a Postdoctoral Fellow in the Research Group for Language Technology at the University of Oslo's Department of Informatics. His work advances computational linguistics for Norwegian and other languages of Norway through resource development and evaluation frameworks. His research prioritizes Norwegian Language Technology, Natural Language Processing, and Low-Resource Language Processing, with specific focus on creating benchmarks, datasets, and models for under-resourced Scandinavian languages. Technical contributions span language understanding, generation, and ethical implications of large language models. Publications reveal a cohesive research trajectory centered on Norwegian language resources: developing evaluation benchmarks (NorEval), multilingual datasets (HPLT), question-answering corpora, and investigations into copyright impacts and continual training for small languages. This work consistently addresses data scarcity challenges through specialized resource construction. Mikhailov actively contributes to the Research Group for Language Technology, collaborating on projects that bridge academic research and practical language technology solutions for Norway's linguistic landscape.
Jelena Radišić is a Researcher at the Department of Teacher Education and School Research at the University of Oslo, where she has been employed since 2016. She is actively involved in international research projects focused on teaching-learning processes, with particular emphasis on observing learning as a dynamic process rather than just outcomes. Her work bridges multiple educational contexts across Europe, including Norway, Serbia, and other international settings through large-scale assessment studies. Educational Background: Graduated in psychology from the Faculty of Philosophy, University of Belgrade Obtained magister degree in developmental psychology from University of Belgrade Completed doctoral studies in educational psychology at the University of Belgrade Part of doctoral training at the Doctoral School in Educational Sciences: Learning, Interaction and Schooling (DSES-LEARN), University of Gothenburg Jelena Radišić's research focuses on the dynamic nature of teaching and learning processes, examining how students and teachers interact in educational settings. She investigates motivation for learning and how instructional practices can foster development, with particular attention to student, teacher, and school characteristics that affect academic achievement. Her work often involves secondary analysis of international large-scale assessment data from TIMSS, TALIS, and PISA, allowing for cross-national comparisons of educational systems. Through mixed methods approaches, she examines both quantitative and qualitative aspects of educational processes, with recent emphasis on mathematics motivation in primary education across different cultural contexts. Analysis of Radišić's recent publications reveals a strong focus on mathematics education, motivation, and international comparative studies. Her work frequently employs TIMSS, PISA, and TALIS data to examine educational phenomena across national contexts, particularly in Nordic countries and Serbia. There's a clear trajectory toward understanding the complex interplay between student motivation, classroom environments, and academic achievement, with increasing attention to longitudinal perspectives on mathematics development. Her research consistently bridges theory and practice, aiming to inform educational policy and classroom instruction through evidence-based findings. Scientific Awards: EERA Scholarship for Emerging Researchers (2010) from the European Educational Research Association ESP Research Fellowship (2010) from the Educational Support Program of the Open Society Institute ESP Research Fellowship (2009) from the Educational Support Program of the Open Society Institute Radišić serves in significant editorial and coordination roles that demonstrate her standing in the educational research community. Since 2022, she has been an Associate Editor for the European Journal of Psychology of Education, having previously served as Assistant Editor from 2015-2021. She is also the Senior Coordinator for EARLI SIG 10—Social Interaction in Learning and Instruction (2021-2025), and previously served as JURE Coordinator for the same SIG (2015-2019). Currently, she leads an international project "Co-constructing mathematics motivation in primary education - A longitudinal study in six European countries (MATHMot)" which examines mathematics motivation development across school and home environments. Radišić is actively involved with the TIMSS research group at the University of Oslo and contributes to the EKVA Large-scale Educational Assessment research group. Her current projects focus on mathematics competence, motivation, academic emotions, and computer-based assessment in mathematics. She collaborates extensively with researchers across Europe, including ongoing work with institutions in Italy, Sweden, Finland, and Serbia, reflecting her commitment to international educational research that addresses common challenges across different educational systems.
Kyle Andrew Porter is a Researcher in the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU), Gjøvik campus. His work focuses on the intersection of digital forensics, natural language processing, and artificial intelligence applications in law enforcement contexts. Dr. Porter's research interests include: Digital forensics methodologies and techniques Natural language processing for criminal investigations AI regulation compliance in law enforcement Data science approaches to security challenges Human factors in digital evidence analysis Metadata extraction and filesystem analysis His publication history reveals a clear evolution from foundational technical work in digital forensics (2017-2018) toward increasingly applied research addressing real-world investigative challenges (2021-2025). Recent work demonstrates sophisticated integration of NLP and AI techniques to solve practical problems in criminal investigations while navigating regulatory landscapes like the AI Act. His research consistently addresses efficiency improvements, accuracy concerns, and cognitive factors in digital evidence processing. Dr. Porter teaches IMT4133 - Data Science for Security and Forensics, contributing to the development of future professionals in this specialized field. His educational contributions also include an Educational Guide published with John Wiley & Sons in 2022.
Yong Man Ro is a Professor of Electrical Engineering and ICT Endowed Chair Professor at Korea Advanced Institute of Science and Technology (KAIST). His research spans multiple areas of computer vision and artificial intelligence with a focus on facial expression recognition, video anomaly detection, and virtual reality applications. Professor Ro's research interests include: Computer Vision and Image Processing Facial Expression and Micro-expression Recognition Video Anomaly and Abnormal Event Detection Virtual Reality and 360-degree Image Quality Assessment Large Language and Vision Models Medical Image Analysis His recent publications demonstrate a strong trend toward multimodal AI systems, particularly focusing on the integration of language and vision capabilities. Professor Ro has made significant contributions to video anomaly detection with his BMAN framework and has recently been exploring the frontiers of large language and vision models with works like Moai and Meteor. His research trajectory shows evolution from foundational work in facial recognition to cutting-edge multimodal AI systems. Professor Ro has received substantial academic recognition with his work being cited over 12,865 times, indicating significant influence in his field.
Sahar A. Abdelmohsen is a Professor at the Adult Nursing Department, Faculty of Nursing, Assiut University (Egypt) with a concurrent affiliation at the Department of Nursing Sciences, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University (Saudi Arabia). Her research focuses on artificial intelligence applications in healthcare, particularly their impact on nursing workflows, clinical decision-making, and patient outcomes in medical-surgical contexts. Her work analyzes AI technologies including machine learning for diagnostic accuracy, natural language processing for clinical documentation, and robotic systems for patient care. Recent publications demonstrate strong emphasis on evidence synthesis through systematic reviews to evaluate AI efficacy in nursing. She currently leads research supported by Prince Sattam bin Abdulaziz University (Grant: PSAU/2025/R/1447) investigating AI-driven improvements in healthcare delivery systems.
Phuong Dinh Ngo is an Associate Professor in Machine Learning at the Department of Physics and Technology , UiT The Arctic University of Norway. With extensive work in healthcare AI and reinforcement learning, their research spans clinical coding, diabetes management, and clinical text de-identification. Affiliation: UiT The Arctic University of Norway Email: phuong.ngo@uit.no Research Interests: Phuong's work focuses on applying machine learning to healthcare challenges, particularly in clinical coding automation, diabetes management systems, and privacy-preserving clinical language models. Their projects include ICD-10 code prediction, telehealth development, and reinforcement learning for blood glucose control. Collaborations: They collaborate with researchers across Scandinavia and internationally, including Miguel Angel Tejedor Hernandez, Taridzo Fred Chomutare, and Fred Godtliebsen. Their publications appear in journals like Journal of Medical Internet Research , Applied Sciences , and Diabetes Technology & Therapeutics . Recent Publications: Their 2025 work on NorDeClin-BERT for ICD-10 prediction in Norwegian clinical texts represents cutting-edge medical NLP research. Earlier studies address diabetes management systems, including risk-averse food recommendation algorithms and reinforcement learning frameworks for type 1 diabetes patients.
