Professor Vagelis Plevris is affiliated with the Department of Built Environment at Oslo Metropolitan University's Faculty of Technology, Art and Design. His research focuses on structural engineering, computational methods, and sustainable construction materials. Key areas include seismic assessment of historical structures, AI-driven structural analysis, and digital twin frameworks for infrastructure management. He leads the Structural Engineering Research Group (SERG) and collaborates on Industry 5.0 applications in cultural heritage preservation. Research interests span structural analysis techniques (e.g., finite element methods, symbolic matrix analysis), optimization algorithms (e.g., ant colony optimization), and computational tools for engineering education. His work addresses challenges in earthen historical structures, wooden defect detection via CNNs, and emergency inspection scheduling post-disasters. His recent publications emphasize synthetic data generation for bridge digital twins, chatbot performance in mathematical reasoning, and open-source frameworks for reinforced concrete modeling. He actively contributes to conferences like the World Conference on Earthquake Engineering and ECCOMAS events.
Hans Z. Munthe-Kaas is a Professor of Mathematics at the University of Bergen , Norway. His academic roles include Chair of the Abel Prize Committee , Editor-in-Chief of Foundations of Computational Mathematics , and President of the Norwegian Mathematical Society . As Project Leader for the Lie-Størmer Center and the RCN-Fripro CODYSMA project, he bridges pure and applied mathematics with computational science. Department of Mathematics, University of Bergen Editor-in-Chief, Foundations of Computational Mathematics Chair, Abel Prize Committee President, Norwegian Mathematical Society His research spans geometric integration , post-Lie algebras , and Lie-Butcher series , with applications in numerical analysis , stochastic differential equations , and combinatorial Hopf algebras . Recent publications focus on equivariant methods , symmetric spaces , and aromatic B-series . Earlier work includes parallel algorithms and coordinate-free numerics , leading to the development of the DiffMan MATLAB toolbox for manifold differential equations. Scientific contributions emphasize algebraic structures in numerical methods, with connections to renormalization , non-commutative symmetric functions , and spectral element methods using multivariate Chebyshev polynomials . Awards include the Esso prize (1999) . He teaches courses like Mat101 (Precalculus) and mentors students in advanced mathematical computation.
Jill Walker Rettberg is a Professor of Digital Culture at the University of Bergen, Norway, and Co-Director of the Center for Digital Narrative (CDN), a Norwegian Center of Excellence funded by a €15M grant (2023–2033). She leads the ERC Advanced Grant project AI STORIES (2024–2029) and previously directed the ERC Consolidator project Machine Vision in Everyday Life (2018–2024). Her research focuses on how technologies like AI and machine vision shape narratives and cultural practices. Education and Background: Rettberg holds a PhD in Humanistic Informatics from the University of Bergen and has a background in Comparative Literature. She has been a pioneer in digital culture studies since the late 1990s, winning awards such as the Ted Nelson Newcomer Award (1999) and the John Lovas Memorial Award (2017). Research Interests: Her work spans AI narratives, machine vision ethics, social media storytelling, electronic literature, and the societal impacts of algorithms. Recent projects include analyzing generative AI’s cultural biases and exploring how machine vision influences human perception. Publications: Rettberg authored Machine Vision: How Algorithms Are Changing the Way We See the World (2023), Seeing Ourselves Through Technology (2014), and Blogging (2008). Her articles and datasets investigate topics like AI-generated narratives, facial recognition bias, and digital art interfaces. Grants and Awards: Besides ERC grants, she has been recognized with the Meltzer Foundation Prize (2006) and serves in leadership roles at UiB AI and LEAD AI networks. Teaching and Mentoring: Rettberg supervises PhD students in digital culture, including work on conversational apps for chronic patients and haptic interfaces in digital art. She teaches courses on machine vision, critical digital theory, and AI ethics.
Ingrid Chieh Yu is an Associate Professor in the Department of Informatics at the University of Oslo, where she also serves as Head of Research and Vice Head of Department (2021-2024) and Deputy Centre Director of SIRIUS. Her academic home is within the Data and Knowledge Management (DKM) research group, with additional affiliations to the Concurrent Security and Robustness for Networked Systems (ConSeRNS) initiative. Professor Yu's research spans multiple domains in computer science, with early work focused on formal methods, software product lines, and concurrent systems. In recent years, her research has expanded significantly into explainable AI, counterfactual explanations, and machine learning interpretability. She has made substantial contributions to feature model evolution, software configuration, and model checking techniques. Her work demonstrates a consistent thread of applying formal methods to practical software engineering challenges, with a notable pivot toward AI explainability in the past five years. Her publication record shows a clear evolution from foundational work in software engineering and formal methods (2006-2015) to increasingly AI-focused research (2016-present). The most recent publications (2020-2025) predominantly address explainable AI, counterfactual explanations, and robust recourse methods, reflecting her adaptation to emerging challenges in machine learning. She maintains strong collaborative relationships with researchers including Einar Broch Johnsen, Crystal Chang Din, and Peyman Rasouli. Professor Yu teaches core computer science courses including INF2220 Algorithms and Data Structures, INF3230 Formal Modeling and Analysis of Communicating Systems, and INF5130 Selected Topics in Rewriting Logic. Her teaching reflects her research expertise, bridging theoretical foundations with practical applications. She leads significant research projects including HyVar: Scalable Hybrid Variability for Distributed Evolving Software Systems and Leveraging Energy-Aware Programming (LEAP), demonstrating her capacity to secure and manage substantial research initiatives. Her work with the SIRIUS center indicates strong industry connections and applied research focus.
Haidar Hosamo is an Associate Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, affiliated with the Department of Built Environment. His work focuses on building sustainability, energy optimization, and digital twin technologies. Research areas: BIM, occupant modeling, predictive maintenance Key tools: Machine learning, AI-driven sensitivity analysis Recent publications address machine learning for floating solar arrays, dynamic LCA uncertainty, 5D BIM adoption, and knowledge graph integration in heritage conservation. His work combines data science with civil engineering to enhance building performance and occupant comfort.
Roy Krøvel serves as Professor of Journalism at Oslo Metropolitan University (OsloMet) within the Faculty of Social Sciences, Department of Journalism and Media Studies. He additionally holds a Professor II position at Sami University College and has previously maintained affiliations with Latin American studies at the University of Oslo. His academic journey spans civil engineering (with thesis work on project management and environmental risk) to journalism/media studies, culminating in a 2006 PhD on guerrilla organizations and digital journalism in Mexico and Central America. Professor Krøvel's research interests center on journalism safety frameworks, indigenous media systems, and intercultural education models. His work bridges digital journalism practices with indigenous knowledge sovereignty, particularly focusing on Sámi media systems in the Arctic region. He has made significant contributions to understanding journalist safety in conflict zones and the impact of emerging technologies like AI on journalistic practices, while maintaining longstanding engagement with Latin American indigenous university networks. Analysis of his recent publications reveals three dominant thematic trajectories: (1) the growing intersection of artificial intelligence and journalism safety, with increasing focus on deepfakes and automated content generation; (2) sustained scholarly attention to Sámi and indigenous media sovereignty, particularly regarding land rights and democratic participation; and (3) methodological development in journalist safety research, establishing conceptual frameworks for this emerging subfield. His collaborative approach is evident through consistent partnerships with scholars across Scandinavia, Latin America, and indigenous institutions. Professor Krøvel leads substantial research initiatives including the NORHED-funded collaboration with RUIICAY (network of intercultural and indigenous universities in Latin America) and the Research Council of Norway project "Making Transparency Possible." He co-directs OsloMet's "Media, war and conflict" research group and has coordinated international partnerships since 2008. His grant portfolio demonstrates consistent funding from major Norwegian research bodies including DIKU, NORAD, and the Research Council of Norway, supporting both theoretical research and practical implementation of intercultural education models. Within institutional structures, Krøvel contributes to multiple research environments including OsloMet's Digital Journalism group and KlimaVel initiative. His work with the RUIICAY network represents a significant transnational team effort connecting indigenous universities across Latin America, facilitating knowledge exchange and capacity building between Northern and Southern academic institutions. This collaborative framework has enabled substantial publishing output across journals, textbooks, research reports, and dissemination activities.
Bram Danneels is a Postdoctoral Fellow at the Department of Informatics, University of Bergen, actively contributing to the Earth Biogenome Project Norway (EBP-Nor) and the Marma-detox project. His work focuses on developing genomic workflows for structural and functional annotation of newly sequenced genomes, with particular emphasis on marine mammal toxicology and biodiversity genomics. His research interests center on comparative genomics , chemical defensome analysis , and marine mammal adaptation . Dr. Danneels investigates gene loss events in cetaceans, particularly examining how the absence of key xenobiotic receptors like PXR and CAR has reshaped detoxification pathways in whales and dolphins. His work integrates computational approaches with evolutionary biology to understand how environmental contaminants interact with marine mammal physiology. Analysis of his recent publications reveals a strong focus on genome assembly , gene annotation , and comparative analysis of detoxification pathways across species. His research spans from Arctic chironomids to marine mammals, demonstrating expertise in both structural and functional genomics across diverse taxonomic groups. Dr. Danneels is actively involved in the Marma-detox project (funded by the Research Council of Norway, reference 334739), which aims to decode marine mammal toxicology through in vitro and in silico approaches. His work contributes to ELIXIR Norway's efforts in harmonizing computational solutions for life science data processing, particularly for biodiversity datasets. He collaborates extensively with researchers from the University of Porto, University of Oslo, NTNU, and international institutions, working within the Computation Biology Unit at UiB. His current research involves developing computational pipelines for genome analysis and contributing to the Earth Biogenome Project's mission to sequence all eukaryotic species in Norway.
Sondre Rønjom is a Professor in the Department of Informatics at the University of Bergen, specializing in cryptography and cybersecurity. His research focuses on cryptanalysis of symmetric ciphers, including AES, stream ciphers, and post-quantum security protocols. He has contributed to advancements in algebraic attacks, invariant subspace techniques, and privacy-preserving technologies like ZK-SNARKs and blockchain security. Education: Completed his doctoral dissertation in 2009 titled Cryptanalysis of ciphers over finite cyclic groups . Research Interests: Explores vulnerabilities in cryptographic algorithms through methods such as differential cryptanalysis, algebraic attacks, and linear subspace analysis. His work bridges theoretical cryptography with practical applications in secure communication and data protection. Publications: Over 15 peer-reviewed articles, including seminal works on AES distinguishers, preimage attacks on Grendel, and cryptanalysis of lightweight ciphers like Robin and Zorro. His research has been featured in top-tier journals like Lecture Notes in Computer Science and IEEE Transactions on Information Theory . Grants & Collaborations: Engaged in collaborative projects with institutions worldwide, contributing to advancements in post-quantum cryptography and blockchain security protocols.
Brynjulf Owren is a Professor at the Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU), since January 1994. He holds a Doktoringeniør (PhD) in Numerical Analysis from NTH (now NTNU) and has extensive research experience in numerical analysis, differential equations, and geometric integration. His work includes leading projects like GALA (EU Sixth Framework Programme) and SPIRIT (Research Council of Norway) and serving as Vice Dean of Education at NTNU's Faculty of Information Technology and Electrical Engineering (2012–2017). Education: PhD in Numerical Analysis (1990, NTNU), MSc in Physics and Mathematics (1984, NTNU). Research focuses on numerical methods for differential equations, geometric integration, and mathematical modeling. He has supervised 10 PhD and 31 MSc students. Leadership roles include President of the Norwegian Mathematical Society (2007–2011) and membership in SEFI and ECMI. Research interests span geometric numerical integration, structure-preserving methods, and applications in computational physics and machine learning. His work bridges theoretical mathematics and computational science, emphasizing methods that preserve geometric properties of dynamical systems. Key contributions include advancements in Lie group integrators, Kahan discretization, and energy-preserving algorithms. Collaborations with institutions like SINTEF and international projects highlight his interdisciplinary impact. Current projects explore neural networks’ integration with differential equations and scientific computing.
Frank Lindseth is a Professor at the Norwegian University of Science and Technology (NTNU) within the Faculty of Information Technology and Electrical Engineering and its Department of Computer Technology and Informatics . His work bridges computer science with medical and engineering applications. Research Interests span Medical Image Analysis , Computer Vision , Autonomous Vehicle Systems , and 3D Segmentation Techniques . Recent publications focus on deep learning for cardiac imaging, digital twins in wind farm maintenance, and winter condition LiDAR data analysis. Collaborative Efforts include projects on ultrasound-guided surgery , privacy-preserving image generative models , and virtual reality medical training . His team develops frameworks for LiDAR-GNSS data fusion and anonymization of full-body images .
Antonella Zanna Munthe-Kaas is a Professor and Head of Department at the Department of Mathematics at the University of Bergen (UiB). Her primary research focuses on numerical analysis of differential equations, particularly geometric integration methods that preserve underlying geometrical structures of equations like symplecticity or volume preservation. Her research spans both theoretical and applied mathematics, with significant contributions to the development of numerical methods for differential equations. She has established numerous interdisciplinary collaborations, particularly in medical applications including image processing, image analysis, modeling of physiological processes, and the use of machine learning for diagnostic tools. Professor Munthe-Kaas has supervised numerous Master's and PhD students, with research topics ranging from geometric integration methods for image denoising to renal function estimation using MRI. Her recent publications demonstrate a continued focus on computational methods for medical imaging and numerical analysis of differential equations. Her research is supported by various projects including NFR Frinatek no. 262203 'Flow based interpretation of Dynamic Contrast-Enhanced imaging' (2017-2021), and previous projects such as GeNuIn (2009-2012) and CRiSP (EU, 2011-2013). Scientific Awards: Holmboe Prize (2020-2021) Holmboe Prize (2022) Professor Munthe-Kaas has made significant contributions to the field through her research, teaching, and interdisciplinary collaborations. Her work bridges theoretical mathematics with practical medical applications, demonstrating the power of mathematical modeling in solving real-world problems.
Peder Langeland Myhre is a Professor in the Department of Cardiology at the University of Oslo, affiliated with Oslo University Hospital (OUS HF Rikshospitalet). His research focuses on heart failure, biomarkers, proteomics, and the application of artificial intelligence in cardiology. Dr. Myhre's research interests span several critical areas in modern cardiology, including heart failure management, cardiovascular biomarkers, proteomics, and digital health applications. His work particularly emphasizes the role of biomarkers in predicting outcomes in heart failure patients, understanding the pathophysiology of heart failure with different ejection fractions, and developing novel diagnostic and prognostic tools. His recent publications demonstrate a strong focus on the intersection of traditional cardiology with emerging technologies, particularly artificial intelligence applications in echocardiography and digital health tools for heart failure management. Dr. Myhre leads or participates in several significant research projects including COVID-MECH (Cardiac impact of COVID-19), OMEMI (Omega-3 after heart attack), SMASH-1 (Prediction of ventricular arrhythmias), and STRONG@HOME (Digital home monitoring of heart failure). He is affiliated with the KG Jebsen Center for Cardiac Biomarkers and NorTrials Cardiovascular research groups, contributing to Norway's cardiovascular research infrastructure. Current Projects: COVID-MECH: Cardiac impact of COVID-19 OMEMI: Omega-3 after heart attack SMASH-1: Prediction of ventricular arrhythmias STRONG@HOME: Digital home monitoring of heart failure Research Groups: KG Jebsen Center for Cardiac Biomarkers NorTrials Cardiovascular Dr. Myhre's work has significant implications for improving risk stratification, treatment selection, and monitoring of patients with various cardiac conditions, particularly heart failure. His research bridges the gap between basic science and clinical application, with a strong emphasis on translating biomarker discoveries into clinical practice.
Åsa Birna Birgisdottir is an Associate Professor at the Department of Clinical Medicine , UiT The Arctic University of Norway , where she leads the experimental molecular biology research activity and serves as Deputy Head of the Cardiovascular Research Group. Her work focuses on mitochondrial quality control in heart cells, particularly through autophagy mechanisms, and she has established a human engineered heart tissue (EHT) model. Primary Research Area: Mitochondrial dynamics in cardiomyocytes Key Techniques: Super-resolution microscopy, machine learning-based image analysis Projects: VirtualStain, NanoAI Her recent publications emphasize mitochondrial morphology quantification, mitophagy induction, and development of advanced imaging methodologies. She supervises students across Bachelor’s to PhD levels in biomedical disciplines.
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.
Assoc. Prof Daniel Patel holds a position in the Department of Computing, Mathematics, and Physics at Western Norway University of Applied Sciences (HVL). His research focuses on interdisciplinary applications of computer graphics and visualization techniques in geosciences, VR training systems, and educational technology. Key areas include collaborative software for subsurface CO2 storage, seismic data interpretation, and vision-training serious games for children. His work bridges computer science with geology, energy systems, and marine biology, emphasizing real-time visualization algorithms and secure rendering methods. He has pioneered VR training systems for safety assessment and developed web-based 3D geology visualization tools using open standards like X3DOM. His research also extends to acoustic marine species identification and GPU-optimized rendering techniques. Notable contributions include advancements in homomorphic-encrypted volume rendering for secure visualization, multi-GPU processing with the Vulkan API, and sketch-based geological modeling tools. His projects often integrate modern hardware (e.g., VR headsets) with game engines to enhance training and educational outcomes.
