Maj Schian Nielsen is a Senior Research Librarian at the University Library of the University of Agder. Her work focuses on multilingualism, crosslinguistic awareness, and German language pedagogy. She is affiliated with the research groups 'Media and Communication Studies' and 'Multilingualism in Society and Education (MUSE).' University of Agder Research Groups: Media and Communication Studies, MUSE Her research explores how multilingual awareness can enhance grammar instruction in German third-language (L3) teacher education programs across Denmark and Norway. Recent publications analyze curriculum structures, educational materials, and the integration of generative AI tools like ChatGPT in multilingual education contexts. Scientific output trends reveal a focus on: Cross-linguistic pedagogy L3 German acquisition Grammar teaching methodologies AI applications in language learning Teacher training for multilingual classrooms Systemic Functional Linguistics (SFL) frameworks She has not been publicly recognized with scientific awards listed in the available data.
Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.
Vasileios Mavroeidis is an Associate Professor in Digital Security at the Department of Informatics, University of Oslo (UiO). He specializes in security automation and orchestration (SOAR) and cyber threat intelligence (CTI) representation, reasoning, and sharing. He actively contributes to European cybersecurity initiatives, including Horizon Europe, Connecting Europe Facility, and the European Defense Fund, and serves as the primary representative of UiO at the OASIS standards development organization since 2017. Role : Associate Professor Department : Digital Security (SEC), University of Oslo Standardization Involvement : Chairman of OASIS Threat Actor Context (TAC), Leading Contributor to CACAO and OpenC2 Projects : Concordia, CyberHunt, JCOP (Joint Cyber Security Operations Platform), Oslo Analytics, P4C (Partnership for Cybersecurity) His research focuses on cyber threat intelligence (CTI), exploring its taxonomies, sharing standards (STIX, CACAO), and ontologies, with contributions to the European Union Agency for Cybersecurity (ENISA) Cybersecurity Playbooks task force. He analyzes quantum computing's impact on cryptography, develops automated threat detection systems using machine learning (e.g., recurrent neural networks for malware-generated domains), and investigates privacy issues under GDPR. Recent publications highlight his work on LLMs for code stylometry , neurosymbolic AI for cyber defense , and knowledge management systems for CACAO playbooks . His articles span 2017–2025, emphasizing formal verification, biometric data protection, and incident response automation. He collaborates with organizations like OASIS (Threat Actor Context, CACAO, OpenC2) and FIRST (Traffic Light Protocol), and participates in European research projects. His work includes standardization efforts in cybersecurity playbooks , MITRE ATT&CK representation, and quantum-resistant cryptography .
Norwegian University of Science and TechnologyNorway
Jon Atle Gulla is a Professor at NTNU and Director of the Norwegian Research Centre for AI Innovation (NorwAI). He holds academic leadership roles, including former Head of the Department of Computer Science and Informatics at NTNU. His expertise spans Semantics, Language Technology, Recommender Systems, and AI-driven innovation. He has nearly 150 international publications and advised over 100 students across MSc, PhD, and postdoctoral levels. Education: MSc in Computer Science (1988), PhD in Computer Science (1993) from Norwegian Institute of Technology (NTH) MSc in Linguistics (1995), University of Trondheim MSc in Management (Sloan fellowship, 2003), London Business School Research interests focus on Semantics and Language Technology applied to Recommender Systems, Information Retrieval, and Text Analysis. He explores AI-based innovations in digitalization and entrepreneurship, advising industry on AI adoption and commercialization. Notable projects include Big Data collaborations with DNB, RecTech for news recommendation, and Trondheim Analytica analyzing political texts/social media. Publications emphasize AI applications in news recommendation, political text analysis, and Scandinavian language models. His work addresses ethical AI, copyright implications, and cross-lingual NLP challenges. Awards: Member of the Royal Norwegian Society of Arts and Sciences. Advising/grants: Supervised 30 PhD students and 70 MSc students. Involved in startups like Fast Search & Transfer (acquired by Microsoft) and Mito.ai/Strise.ai. Active in reviewing for journals like Data & Knowledge Engineering and conferences like ACL. Labs/teams: Leads NorwAI, co-founder of INRA and NOBIDS workshops. Collaborates with industry and academia on AI-driven solutions.
Simen Grung is a Doctoral Research Fellow at the Department of Teacher Education and School Research, Faculty of Educational Sciences, University of Oslo. His work focuses on assessment practices and English language pedagogy. Current projects: EDUCATE (Subject Renewal Evaluation), LANGUAGES (Language Instruction Contexts), NAVIKO-LU (Video-based Teacher Training), VIST (Video Excellence in Teaching) Research Groups: SISCO (Studies of Instruction across Subjects and Competences) His research explores feedback mechanisms in international classroom contexts, formative assessment challenges, and the use of video representations to enhance teacher training programs across Norway, England, and France. Recent publications demonstrate expertise in comparative education studies, language proficiency analysis, and assessment research methodologies. Key themes include cross-cultural pedagogical practices, assessment literacy, and video-based professional development. Active in academic collaborations, he works with researchers like Lisbeth M. Brevik, Eva Thue Vold, and Kirsti Klette on projects examining instructional quality and student experiences in teacher education.
Erik Velldal is a Professor in the Language Technology Group (LTG) at the Section for Machine Learning , Department of Informatics, University of Oslo . With over 25 years of experience in machine learning and natural language processing (NLP), he leads the SANT project focused on sentiment analysis and contributes to major research initiatives including MediaFutures , NorwAI , and Integreat (Norwegian Center for AI Research). His work bridges linguistic theory and computational methods, emphasizing semantic modeling and uncertainty detection. Research interests include sentiment analysis , language modeling , event extraction , and machine learning applications to NLP. Recent publications address cross-domain sentiment classification , generative event analysis , and multilingual model adaptation . He co-developed the Norwegian Review Corpus (NoReC) and Norwegian Anaphora Resolution Corpus (NARC) , foundational resources for Norwegian NLP. His projects often involve collaboration with international institutions, reflected in publications at venues like ACL, COLING, and EMNLP. Current efforts focus on entity-level sentiment analysis , diagnostic datasets for Norwegian , and evaluating compositional generalization in language models. No public record of scientific awards or part-time appointments exists.
Swati Aggarwal is a Professor in Artificial Intelligence at the Faculty of Logistics, Molde University College (HiMolde). Her research focuses on AI applications in healthcare, ethics, cognitive development, and neural networks. She holds a PhD in Neutrosophic Neural Networks, a Master's in Information Technology, and a Bachelor's in Computer Science and Engineering. Previously, she was a Marie Curie Postdoc Fellow at NTNU, working on AI models for cognitive assessment in infants (AIM_COACH project). Research Interests - AI in Health/Medicine - Ethics in AI and Societal Impact - EEG/BCI for Cognitive Assessment - Machine Learning and Deep Learning Publications Her recent work spans AI ethics, BCI applications, adversarial attacks, and healthcare diagnostics. Notable contributions include EEG-based infant perceptual monitoring (2025) and malaria detection via EfficientNet (2023). She also explores cross-lingual adversarial robustness and blockchain in hospitality systems. Labs/Teams - ABC-AI: Applied, Basic, and Conscientious AI Group - Virtual Technologies and Learning Research Group
Norwegian University of Science And TechnologyNorway
Georgios Neokleous is a Professor of English in the Department of Teacher Education at the Norwegian University of Science and Technology (NTNU), within the Faculty of Social and Educational Sciences. He holds a PhD in Education from Saint Louis University (2014), focusing on home language use in EAL classrooms. His career began as an EFL/ESL teacher, with extensive experience across Europe and the U.S., and he has developed specialized courses for diverse learner groups. Neokleous’ research emphasizes multilingualism, translanguaging pedagogy, and inclusive literacy practices in linguistically diverse classrooms. Education: PhD in Education, Saint Louis University, 2014 Masters and Bachelor’s degrees (implied, not explicitly stated) Research Interests: Neokleous explores the integration of students’ home languages in EAL settings, translanguaging as a pedagogical tool, and assessment strategies in multilingual environments. His work bridges theory and practice, addressing challenges in teacher education and classroom diversity. Recent trends in his publications highlight cross-national comparisons (e.g., Norway-Cyprus studies), innovative teaching methodologies, and the role of technology in modern classrooms. Advising & Grants: Supervises students at BA, MA, and PhD levels. Collaborates with international researchers like Anna Krulatz and Sviatlana Karpava. Active in organizing academic events, including TESOL International and ECER conferences. Labs/Teams: Engaged in collaborative research groups focused on multilingual education, such as the ‘Learning and Teaching English in the Multilingual Classroom’ initiative. His work often intersects with institutions like the University of Cyprus and Multilingual Matters publishing.
Emilia Andersson-Bakken is an Associate Professor at Oslo Metropolitan University, affiliated with the Faculty of Education and International Studies and the Department of Primary and Secondary Teacher Education. She teaches and supervises student teachers at bachelor’s, master’s, and PhD levels, contributing significantly to initial teacher education. Her academic work is deeply rooted in classroom research, with a focus on critical thinking, creativity, and classroom discourse in primary education. Her research interests include classroom research , initial teacher education , critical thinking , creativity , and classroom discourse . These themes are reflected in her ongoing involvement in key research projects such as KriT (Critical Thinking in Primary Education), Beyond access: improving quality of early years reading instructions in Ethiopia , and Classroom activities for the six-year-olds – 20 years on (Klassprax_20) . These projects emphasize pedagogical innovation, teacher facilitation, and literacy development in early schooling. The trends in her recent publications reveal a strong commitment to advancing critical thinking through children’s literature, dialogic teaching, and teacher-led inquiry. Her work spans both theoretical and practical dimensions, with a recurring emphasis on how teachers can foster student agency and reflective thinking in primary classrooms. She frequently publishes in educational journals and contributes to edited volumes focused on pedagogy and literacy. She has received no explicitly mentioned scientific awards in the provided text. Emilia supervises students at multiple levels and is actively involved in research funding and dissemination efforts through national and international conferences. Her work is supported by collaborative research teams and institutional frameworks at OsloMet. While no specific grants are named, her participation in multi-year, interdisciplinary projects indicates substantial research support and academic leadership. She is part of the research group Classroom Research and contributes to Research and Development Work on the Lower Primary Level With a Focus on Initial Education . These groups focus on observational studies, curriculum evaluation, and teacher development in early education contexts.
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.
Norwegian University of Science And TechnologyNorway
Nicola Paltrinieri is a Professor of Risk Assessment at the Department of Mechanical and Industrial Engineering, NTNU (Norway), and an Adjunct Professor at the University of Bologna (Italy). His expertise spans risk assessment, hydrogen technologies, process safety, and data-driven safety management. He holds Chartered Engineer and Chartered Scientist certifications and has served on editorial boards for journals like Safety Science and Journal of Risk Research . Education: PhD in Environmental, Safety and Chemical Engineering (University of Bologna, 2012) Master’s in Chemical and Process Engineering (University of Bologna, 2008) Research Interests: Focuses on hydrogen infrastructure safety, Natech accident analysis, risk-based inspection strategies, and AI integration in safety systems. His work emphasizes sustainable energy transitions and mitigating risks in emerging technologies like hydrogen. Key Projects (2022-2026): H2Glass : Decarbonizing glass and aluminum sectors via hydrogen HyInHeat : Hydrogen technologies for industrial heating HYDROGENi : Norwegian research center for hydrogen/ammonia Awards: Onsager Fellowship (2016–2021) Frank Lees Medal (2012) for safety-related publications Grants & Leadership: Head of NTNU Energy Team Hydrogen, coordinator for EU-funded projects like SUSHy , and active in international risk committees (e.g., EFCE, ESRA). His work bridges academia and industry, with over 8 PhD examinations supervised. Labs/Teams: Leads the NTNU Energy Team Hydrogen and collaborates on initiatives like SH2IFT-2 for safe hydrogen fuel handling. His research group focuses on AI-driven risk analysis and hydrogen infrastructure resilience.
Andrei Kutuzov is an Associate Professor in the Language Technology Group (LTG) within the Department of Informatics at the University of Oslo. He serves as the Norwegian on-site manager of the High-Performance Language Technology (HPLT) project and has made significant contributions to computational linguistics and natural language processing. His research primarily focuses on computational linguistics and natural language processing, with specialized expertise in semantic change detection, diachronically aware language models, distributional semantics, and large language models. Kutuzov has been instrumental in developing Norwegian language resources including NorBERT, NorELMo models, and the very large-scale NORA.LLM generative models. He created WebVectors, a web service for exploring neural distribution models for Norwegian and English texts. Analysis of his recent publications reveals a strong focus on semantic change modeling, multilingual dataset development, and Norwegian language technology. His work spans from theoretical linguistic analysis to practical applications in language modeling, with significant emphasis on low-resource and Nordic languages. Kutuzov's research demonstrates a consistent trajectory toward improving language models' understanding of semantic evolution and developing robust evaluation frameworks for Norwegian language processing. Norwegian Artificial Intelligence Research Consortium (NORA) award as Distinguished Early Career Researcher (2022) Kutuzov teaches several advanced courses including IN5550 - Neural Methods in Natural Language Processing (2019-2025) and IN3050 - Introduction to Artificial Intelligence and Machine Learning (2024-2025). He has received research funding through the HPLT project which focuses on developing high-performance language technologies. His laboratory work centers around the Language Technology Group at UiO, where he collaborates on developing Norwegian language resources and models, with particular emphasis on diachronic semantic analysis and multilingual capabilities.
Shaukat Ali serves as Research Professor and Head of the Department of Engineering Complex Software Systems at Simula Research Laboratory, concurrently holding the title of Chief Research Scientist. His academic leadership drives innovation at the critical nexus of quantum computing, artificial intelligence, and software engineering, with concentrated expertise in verification, validation, and testing methodologies for complex systems including cyber-physical infrastructures and autonomous robotics. His primary research domains encompass: Verification and Validation Search-Based Software Engineering Autonomous Driving Systems Cyber-Physical Systems Engineering Digital Twin Technologies Quantum Software Engineering Analysis of recent publications (2024-2025) reveals a decisive trend toward quantum-AI convergence in software engineering, particularly through quantum software testing frameworks and AI foundation models applied to cyber-physical systems. His work systematically addresses noise mitigation in quantum hardware, uncertainty quantification in adaptive robotics, and novel testing paradigms using vision-language models for industrial robotics—demonstrating both theoretical rigor and industrial applicability. As department head, Ali spearheads strategic research directions in complex software systems, fostering cross-disciplinary collaboration while actively shaping quantum software engineering through workshops like QAI2024 and Q-SANER 2024. His invited presentations at venues including JYU Quantum Electronics and EU-Korea Quantum Forums underscore his influence in defining emerging research landscapes.
Crina Damsa is a Professor at the Department of Education, University of Oslo. Her research focuses on collaborative learning, interdisciplinary learning, and the role of digital technologies (including AI) in knowledge work and education. She leads projects like Unpacking collaboration: Multimodal analysis and combined method designs and Learning Ecologies in Higher Education , and is involved in research groups such as HEDWORK and LIDA. PhD in Educational Research, University of Oslo (2013) MSc in Learning Design and Technology, Utrecht University (Netherlands) BA in Language and Literature, Babes-Bolyai University (Romania) Her work examines sociocultural and sociomaterial aspects of learning, with a focus on student-centered environments, design-based research, and multimodal learning analysis. She supervises PhD students and contributes to methodological advancements in learning sciences. Editorial roles: Area Editor for Learning in Context , member of editorial boards for journals like Journal of the Learning Sciences and Frontline Learning Research Projects: Academic Hospitality in Interdisciplinary Education (AHIE) , Digital Integration of Video Assessment (DIVA) , Digital Tracing of Collaborative Learning (DigiT) , Global Teacher Education (GatherED)
Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.
