Zhiyuan Wu is a Doctoral Research Fellow at the University of Oslo , affiliated with the Digital Signal Processing and Image Analysis research group under the Faculty of Mathematics and Natural Sciences . Education: Bachelor’s degree in Communication Systems and Information Technology from Lanzhou University, China Master’s degree from the Technical University of Munich, School of CIT Research Focus: Zhiyuan Wu specializes in machine learning, with particular emphasis on probabilistic graphical models, information theory, and tackling real-world challenges such as distributional shifts and privacy concerns. His work explores entropy regularization techniques to address label shift in distributed learning systems, aiming to improve model robustness and data privacy across diverse domains like medical applications. Publications & Research Trends: His recent publication at the International Conference on Learning Representations highlights a novel approach to mitigating label shift through entropy regularization. This aligns with his broader research goals of enhancing the adaptability and interpretability of machine learning models in dynamic, privacy-sensitive environments. Labs & Teams: He is actively involved with the Digital Signal Processing and Image Analysis (DSB) research group, contributing to collaborative projects that bridge theoretical advancements with practical implementations in machine learning.
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.
Joao Carlos Amaro Ferreira is a Professor at the Faculty of Logistics, Molde University College (HiMolde), Norway. He holds PhDs in Computer Engineering and Industrial Engineering from the Technical University of Lisbon and the University of Minho, respectively. His research focuses on Artificial Intelligence (AI) applications in healthcare, energy, transportation, IoT, blockchain, and smart cities. He has led over 40 projects, including 6 as Principal Investigator, and contributed to international conferences like OAIR and INTSYS. He served as IEEE CIS President (2016-2018) and is an IEEE Senior Member since 2015. His academic contributions span AI-driven solutions for public sector informatics, healthcare data quality, and cybersecurity. He actively participates in European projects such as e-Hospital4Future and explores blockchain applications in supply chains and medical records. Ferreira leads the ABC-AI research group, emphasizing ethical and applied AI. His work bridges academia and industry through projects like gamification systems for eco-driving and AI in fisheries traceability. Recent publications highlight AI's role in cardiovascular disease detection, emergency department optimization, and blockchain-enhanced healthcare interoperability. He collaborates internationally, co-editing journals like Applied Sciences , and has authored patents in edge computing for maritime monitoring.
Torgeir Welo is a Professor at the Department of Mechanical and Industrial Engineering , Norwegian University of Science and Technology (NTNU) . He specializes in metal forming , particularly aluminum alloy structures , with a focus on plastic bending behavior , dimensional stability , and 3D forming technologies . His research also encompasses Lean Product Development , emphasizing knowledge reuse and maximizing customer value in automotive and aerospace applications. Key Research Areas : Metal Forming, Aluminum Processing, Springback Control, Lean Development, Additive Manufacturing, Material Substitution Teaching : Courses on Aluminum Technology , Metal Forming Analysis , and Machine Element Design Publications (15 most recent): Focus on springback monitoring , charge weld evolution , flexible forming , machine learning applications , and circular economy frameworks in metal manufacturing.
Jason Nelson is a Professor of Digital Culture in the Department of Linguistic, Literary and Aesthetic Studies at the University of Bergen, Norway. He is a creator of digital poems and fictions, builder of surrealist and politically focused art games and digital creatures. His work is exhibited widely in galleries and journals around the globe at FILE, ACM, LEA, ISEA, SIGGRAPH, ELO and numerous other venues. Nelson serves on organizational boards including the Australia Council Literature Board and the Electronic Literature Organization. Nelson's research focuses on the intersection of digital technology, creative writing, and artistic expression. He explores how AI and machine learning can be harnessed for creative purposes, developing new forms of digital literature and interactive art. His work often involves building expansive visual worlds through collaborative AI processes, creating interactive digital poetry, and developing novel approaches to digital narrative. Nelson's research spans digital humanities, electronic literature, AI-generated art, and interactive media, with particular emphasis on how these technologies transform creative processes and experiences. Over the past decade, Nelson's work has increasingly focused on the creative potential of AI technologies, especially in the areas of text-to-image generation and multimodal authorship. His projects often blend game engines with poetic expression, creating immersive experiences that challenge traditional boundaries between human and machine creativity. Recent works explore themes of multispecies futures, time perception, and the transformation of physical spaces through augmented reality. Nelson has received numerous scientific awards and fellowships including: Fulbright Fellowship at the University of Bergen Moore Fellowship at the National University of Ireland Winner of the Digital Writing Prize, Queensland Literary Awards (15,000 AUD) Winner of the Woollahra Library Digital Poetry Prize (5,000 AUD) Runner-Up Prize at the Videomedeja digital art exhibition Finalist for the Turn-on Literature Prize Finalist for the Queensland Literary Awards, Digital Writing Category Multiple finalist nominations for the New Media Writing Prize Nelson actively participates in academic advising and has secured significant research funding, including a 125,000 AUD grant from the Australia Council of the Arts, Literature Board for his project "Cube Cryptext and Nomencluster," which was recognized as the world's largest interactive art-game. His work "Nine Billion Branches" received multiple awards including the Digital Writing Prize from the Queensland Literary Awards. He has also received a 75,000 NOK grant for the "Flood Mosaic Artwork" project featured in the Floodlines Exhibition at the State Library of Queensland. Nelson is affiliated with the Center for Digital Narrative at the University of Bergen, where he collaborates with researchers like Scott Robert Rettberg and Alinta Krauth. Together they form EphemerLab, exploring new creative processes that move beyond simple "ask and generate" AI methods. Their work involves stitching together hundreds of individual image fragments and components into cohesive visual and narrative concepts, pushing the boundaries of what's possible with current AI technologies.
Elisabeth Oxfeldt is a Professor of Scandinavian Literature at the University of Oslo's Faculty of Humanities, Department of Literature, Area Studies and European Languages. Specializing in Danish and Norwegian literature from the 19th to 21st centuries, she examines cultural narratives through postcolonial and orientalist frameworks with particular focus on guilt discourse in Nordic contexts. Her educational background includes a Ph.D. in Scandinavian Literature from U.C. Berkeley (2002), M.A. in Scandinavian Literature (1996), and B.A. in French Literature (1988). This interdisciplinary foundation informs her transnational approach to Nordic literary studies. Oxfeldt's research centers on Scandinavian narratives of guilt and privilege within globalization, exploring how Orientalism, postcolonialism, and (post)nationalism manifest in literature and visual media. Her work critically engages with Hans Christian Andersen adaptations, war literature, travelogues, and literary activism, revealing tensions between Nordic self-perception as egalitarian societies and historical complicity in colonial structures. She frequently analyzes word-image relationships and adaptation processes across media. Publication trends show sustained engagement with postcolonial critique since her 2005 monograph Nordic Orientalism , evolving toward contemporary analyses of activist voices and guilt narratives. Recent edited volumes address literary rights struggles (2024) and ecological perspectives in Nordic literature (2023), demonstrating expanding interdisciplinary scope while maintaining core focus on representation and power dynamics. Awards and honors: Member of Det Danske Akademi (The Danish Academy) Advising and grants information was not specified in source materials, though her leadership of major research projects suggests significant supervisory and funding acquisition activities. Teaching responsibilities include courses on Nordic literature after 1800, Norwegian world literature, and literary activism. Oxfeldt co-leads the DINO (Diversity in Nordic Literature) research group and previously participated in ECODISTURB. Her current projects include 'Scandinavian Narratives of Guilt and Privilege in an Age of Globalization' (ScanGuilt) and 'Norwegian Romantic Nationalisms' (NORN), examining historical and contemporary constructions of Scandinavian identity through literary lenses.
Xuan Zhang is an Associate Professor at the Department of Information and Communication Technology, University of Agder. His research focuses on Tsetlin Machines, learning automata, and their applications in machine learning, computer vision, and hyperspectral imaging. Research Trends: Zhang’s recent work includes developing interpretable machine learning models (e.g., Tsetlin Machines), optimizing convolutional architectures for image processing, and applying automata theory to solve multi-armed bandit problems and channel selection in cognitive networks. His field spans theoretical analysis and practical implementations in AI, remote sensing, and health informatics. Scientific Contributions Co-developed advanced Tsetlin Machine variants for XOR/NOT operator convergence, disease forecasting, and image restoration Published in journals like IEEE Transactions on Pattern Analysis and Machine Intelligence , Information Sciences , and Applied Intelligence Explored Bayesian pursuit algorithms, hierarchical learning automata, and particle swarm optimization techniques Contact: xuan.zhang@uia.no
Anis Yazidi is a Professor at Oslo Metropolitan University, affiliated with the Faculty of Technology, Art and Design and the Department of Information Technology. His research focuses on Artificial Intelligence, Machine Learning, Medical Technology, and IoT Security, with a particular emphasis on Applications of AI in Healthcare, EEG Signal Processing, and Digital Transformation. Active research projects include AI Mind (dementia diagnostics), Glycopathology in dry eyes, and Pain and mental distress analysis Completed projects: Digital hate speech analysis, AI in reproductive technology, Nano-antibiotics development His recent publications (2023-2025) demonstrate expertise in: Tsetlin Automaton algorithms for concept learning EEG classification using visibility graphs and vision transformers AI ethics frameworks for medical practice Deepfake detection methodologies Collaborative work spans institutions in Norway, Czech Republic, and international AI research communities.
Per-Arne Andersen is an Associate Professor at the Department of Information and Communication Technology within the University of Agder . His research focuses on artificial intelligence , reinforcement learning , Tsetlin machines , and deep learning , with applications in real-time strategy games , industrial environments , and IoT systems . Projects: RESTORE Research Groups: CAIR - Center for Artificial Intelligence Research, CIEM - Center for Integrated Crisis Management, Intelligent Mechatronics (iTron) His work explores safe and sustainable reinforcement learning , interpretable AI , and generative environment modeling . He has developed frameworks like CaiRL and CostNet for high-performance RL environments and goal-directed learning. Recent publications include advancements in Tsetlin automaton analysis , GNSS jamming classification , and road quality detection . Articles from 2025-2016 span machine learning , computer vision , and environmental modeling . He contributes to IEEE , Springer , and LNCS publications, with a focus on interdisciplinary AI applications in crisis management , cybersecurity , and industrial optimization .
Alvaro Köhn-Luque is an Associate Professor at the Oslo Center for Biostatistics and Epidemiology, University of Oslo, and Group Leader at the Department of Medical Genetics, Oslo University Hospital. His work bridges mathematical modeling with clinical applications, particularly in cancer research. His academic background includes a PhD in Mathematical and Computational Biology from Complutense University of Madrid (2012), preceded by multiple Master's degrees in Mathematics and Physics from Spanish universities. Dr. Köhn-Luque's research focuses on mathematical oncology , developing computational models to understand cancer dynamics and improve treatment strategies. His work spans multiscale modeling of tumor growth, personalized cancer medicine through computer simulations, and biomarker discovery using machine learning approaches. He has made significant contributions to modeling breast cancer progression and treatment response, particularly in the context of endocrine therapy and CDK4/6 inhibition. His recent publications demonstrate a strong trend toward integrating mechanistic learning approaches that combine mathematical models with machine learning techniques. This hybrid methodology allows for more accurate prediction of treatment outcomes while maintaining biological interpretability. His work frequently involves collaboration with clinical researchers to ensure models are grounded in real patient data and have direct translational potential. Computational modeling of tumor heterogeneity and drug response Development of methods for phenotypic deconvolution in cancer cell populations Integration of multi-omics data for personalized treatment prediction Application of birth-death processes to model tumor evolution Creation of user-friendly computational tools for biomedical researchers Dr. Köhn-Luque has supervised multiple PhD students including Even M Myklebust, Salim Ghannoum, and Xiaoran Lai, and has secured funding for projects including RESCUE, BigInsight, and Integreat. His research demonstrates a consistent trajectory from theoretical mathematical biology toward increasingly clinically relevant applications in personalized cancer medicine.
Professor Ole-Christoffer Granmo is a distinguished academic at the University of Agder, Norway, where he serves as Professor in the Department of Information and Communication Technology. He is the Founding Director of the Centre for Artificial Intelligence Research (CAIR) at the University of Agder, leading cutting-edge research in artificial intelligence and machine learning. Dr. Granmo obtained his master's degree in 1999 and his PhD in 2004, both from the University of Oslo. His academic journey has been marked by significant contributions to the field of AI, most notably the creation of the Tsetlin machine in 2018, for which he received the AI research paper of the decade award from the Norwegian Artificial Intelligence Consortium (NORA) in 2022. Professor Granmo's research primarily focuses on logical and causal world modeling across multiple modalities including images, sound, and natural language. His work spans logical auto-encoding, convolution, regression, transformer architectures, and reinforcement learning, all with the overarching goal of creating ultra-low-power artificial general intelligence through transparent logical learning and reasoning. His publications reveal a strong emphasis on interpretable AI systems, hardware implementations, and applications across diverse domains including cybersecurity, healthcare, social media analysis, and bioinformatics. AI Research Paper of the Decade (2022) - Norwegian Artificial Intelligence Consortium (NORA) Eight paper awards in machine learning Professor Granmo has coordinated over seven research projects and mentored 55+ master's students and nine PhD students. His leadership extends to co-founding the Norwegian Artificial Intelligence Consortium (NORA) and establishing two companies: Anzyz Technologies AS and Tsense Intelligent Healthcare AS. As an advisor at Literal Labs, he actively bridges academic research with practical industry applications, demonstrating his commitment to translating theoretical innovations into real-world solutions that address complex challenges across multiple sectors.
Alvaro Fernandez Quilez is an Associate Professor in Artificial Intelligence at the Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, University of Stavanger. He leads the Stavanger AI Laboratory (SAIL), fostering interdisciplinary AI research with a focus on healthcare and education applications. Research Interests: His work centers on responsible AI, emphasizing ethics, fairness, transparency, and uncertainty in AI systems. He applies deep learning and machine learning techniques to medical imaging, particularly in prostate cancer and neurodegenerative diseases like Alzheimer’s and Parkinson’s. His research integrates algorithmic innovation with clinical relevance, addressing challenges in data scarcity, bias, and model interpretability. The recent publications highlight a strong trend in developing and evaluating AI models for diagnostic support in radiology and neurology. Key themes include uncertainty quantification, self-supervised learning, synthetic data generation via GANs, and fairness analysis across gender and centers. The work spans from foundational AI methods to their clinical translation in multi-center studies. Teaching and Academic Leadership: He coordinates the course DAT105 - AI for everyone and has contributed as a guest lecturer in bioinformatics, technological foundations, and PhD ethics, particularly on AI and ethics. He is also enrolled in a PhD supervisory qualification program, underscoring his growing role in graduate education. Advising and Grants: While specific students and grants are not listed in the text, his leadership of SAIL and active publication record suggest involvement in research supervision and project funding. His collaborations span multiple institutions and disciplines, indicating strong team-based research efforts. Laboratories and Teams: He leads the Stavanger AI Laboratory (SAIL), which serves as the central hub for AI research at the University of Stavanger, promoting collaboration across departments and with external partners in healthcare and technology.
Daniel Groos is a Researcher at the Department of Computer Science, NTNU, specializing in the development of machine learning models for medical and sports-related motion analysis. His work focuses on applying deep learning techniques to video-based movement analysis for early detection of cerebral palsy in infants and performance analysis in elite sports. Education: PhD in Medical Technology (NTNU, 2018-2022), MSc in Computer Science with specialization in AI (NTNU, 2013-2018). Research interests include interdisciplinary collaborations with St. Olavs Hospital and Norwegian Open AI Lab. Key topics are deep learning applications in healthcare, computer vision for movement analysis, and sports biomechanics. Publications emphasize automated clinical analysis, video-based diagnostics, and human pose estimation. Notable projects include a deep learning method for cerebral palsy prediction and motion tracking systems for elite ski jumpers. Collaborations with institutions like the Centre for Elite Sports Research and Olympiatoppen highlight his work in sports performance analysis. No formal scientific awards listed but active in academic outreach with lectures at European conferences on childhood disability and movement analysis.
Efthymios Papatzikis is a Professor of Infant Brain Development at Oslo Metropolitan University’s Faculty of Education and International Studies. He leads the Advanced Health Intelligence and Brain-Inspired Technologies (ADEPT) Research Group, focusing on brain development in the first 1500 days of life using neuroimaging, behavioral analysis, and AI-driven tools. Research Focus: Multimodal neuroimaging (qEEG, ABR, aEEG), computational neuroscience, AI in NICU diagnostics, and personalized sound/music interventions. Collaborations: Harvard University, Martinos Center for Biomedical Imaging, UCL, and Bergen University. Editorial Roles: Associate Editor for Frontiers in Pediatric Psychology, Guest Editor for Frontiers in Pediatric Neurology. His work bridges neuroscience with clinical neonatal care, emphasizing family-centered medicine and precision diagnostics. Recent projects include EU Cost Action CA22111 on real-world environments’ impact on brain development. Scientific Awards: Fellow of the Higher Education Academy (FHEA), UK Certified Specialist in Social Prescribing by the World Health Organization He contributes to journals as reviewer and editor, co-develops computational tools for NICU EEG analysis, and advises international foundations on maternal/child health and early childhood education.
Elisabet Apelmo serves as Senior Lecturer in the Department of Social Work at Malmö University, Sweden. A visual artist with a PhD in Sociology, she bridges creative practice and academic inquiry to explore the complex intersections of bodily experience, sport, disability, and gender within contemporary society. Her research program centers on sociological analyses of bodily difference, particularly examining how physical education environments construct inclusion and exclusion through gendered and ableist frameworks. Apelmo employs phenomenological approaches and visual methodology to investigate how disabled bodies navigate sporting spaces, challenging normative assumptions about athletic participation and bodily capability. Her work consistently reveals how sporting cultures simultaneously enable and constrain identities, particularly for women and people with disabilities. Analysis of Apelmo's publication history shows sustained engagement with critical disability theory, gender studies, and sport sociology spanning nearly two decades. Her scholarship demonstrates methodological innovation through visual approaches and phenomenological frameworks, with recurring themes including the social construction of disability in sport contexts, gendered bodily experiences in physical education, and the material-semiotic relationships between bodies and technologies like wheelchairs. Among her current research initiatives are investigations into participation and exclusion dynamics in mainstream physical education from disability and gender perspectives, and ethnographic studies examining how dancers strategically use their bodies and gazes to disrupt stereotypical perceptions of physical impairment. These projects reflect her commitment to generating knowledge that directly challenges ableist assumptions within sporting cultures and educational settings.
