Diogo Kramel is a Research Fellow in the Industrial Ecology Program at the Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU). He holds degrees in Naval Architecture and Maritime Engineering (BSc/MSc, University of São Paulo) and Maritime Spatial Planning (MSc, University of Sevilla/University of the Azores/University of Venice). His PhD research (2024) focused on climate change mitigation in the maritime sector through the CLIMMS project. Currently, he leads the Nord_H2ub project, applying the MariTEAM model combined with Life-Cycle Assessment to reduce GHG emissions in shipping. His work integrates modeling techniques with policy analysis to inform sustainable maritime strategies. Key research interests include shipping emissions, energy systems modeling, and climate policy. He has published extensively on topics such as shipping scenarios, emissions reduction strategies, and integrated assessment frameworks. His contributions include developing the MariTEAM model for global fleet analysis and linking it with tools like MESSAGEix. Recent presentations include talks at the European Geosciences Union and the Gordon Research Conference on Climate Intervention.
Ana Isabel Silva Martins is a Doctoral Research Fellow at the University of Oslo's Faculty of Mathematics and Natural Sciences , affiliated with the Department of Astrophysics. Her research focuses on cosmology, gravitational waves, and machine learning applications in high-performance computing. Education: MSc in Experimental Physics (2022-2024), Utrecht University BSc in Engineering Physics (2019-2022), Instituto Superior Técnico, University of Lisbon Research Interests: She explores early detection mechanisms for gravitational wave signals from binary neutron star coalescence, leveraging convolutional neural networks (CNNs) and field-programmable gate arrays (FPGAs). Her work bridges astrophysics, machine learning, and computational hardware optimization. Projects & Groups: Active in the CMB&CO group , Cosmoglobe , and CosmoglobeHD . Collaborates on cosmology and extragalactic astronomy research.
Luca Cibinel is a Doctoral Research Fellow at the University of Oslo, affiliated with the Department of Mathematics and the Statistics and Data Science group. His PhD project, supervised by Basil Ell, Johan Pensar, and Riccardo De Bin, focuses on developing statistical learning techniques for assessing and generating transition metal complexes based on observational data and theoretical knowledge. Prior to this, he earned a master's degree in mathematics from the University of Trento (2023), with a thesis on penalized likelihood inference for Gaussian covariance graph models. He also worked as an early-stage researcher at the University of Padua, investigating probabilistic graphical models for count data in high-dimensional scenarios. His research interests include statistical relational learning, probabilistic logic, and machine learning applications for graph-structured data. Luca is based at the Niels Henrik Abels hus in Oslo. Education: Master's in Mathematics, University of Trento (2023) Early-stage Researcher role at University of Padua (2024) Research Focus: Luca's work bridges statistical methodology and computational modeling, particularly in contexts requiring integration of theoretical and empirical data. His current project aims to create frameworks for evaluating and generating transition metal complexes, with potential applications in materials science and chemistry. Professional Affiliations: Faculty of Mathematics and Natural Sciences (student status), Statistics and Data Science research group. Links: LinkedIn Profile
Thordis Linda Thorarinsdottir is a Professor of Statistics and Data Science at the University of Oslo's Department of Mathematics, affiliated with the Faculty of Mathematics and Natural Sciences. She previously worked as a Chief Research Scientist and Research Leader for Climate and Environment at the Norwegian Computing Center (2006–2023). Her research focuses on developing stochastic models for environmental sciences, emphasizing uncertainty quantification and probabilistic prediction in climate, weather, and hydrology. Key areas include spatial and spatio-temporal modeling, Bayesian frameworks, and forecast evaluation. **Education**: PhD in Mathematical Statistics from Aarhus University (2006). **Research Interests**: Environmental applications in climate science, spatial modeling, probabilistic forecasting, and extreme event analysis. She collaborates with experts in meteorology, hydrology, and climate science to address data deficiencies and real-world challenges. **Projects**: Leads initiatives like CONFER (Climate Futures) and is part of the Integreat research group. Her work integrates statistical theory with environmental process understanding to enhance predictive accuracy and decision-making under uncertainty. **Awards**: Not explicitly listed, but her contributions to climate and environmental statistics are recognized internationally. **Grants & Labs**: Active in interdisciplinary collaborations, including the Norwegian Research Council-funded projects. Her team focuses on probabilistic modeling and ensemble forecasting techniques.
Professor Vadim Kimmelman is affiliated with the Department of Linguistic, Literary and Aesthetic Studies at the University of Bergen. His primary research interests lie in sign language linguistics, with a focus on syntax, nonmanual markers, classifier predicates, and computational methods in linguistic analysis. He has contributed to foundational studies on Russian Sign Language (RSL), Sign Language of the Netherlands (NGT), and other signed languages. Key research areas include the formal analysis of argument structure, transitivity, metaphorical extensions, and the role of nonmanual components in grammatical and pragmatic functions. His work combines corpus linguistics with computational tools, such as computer vision techniques for analyzing nonmanual behaviors in sign language interactions. Published in high-impact journals like Annual Review of Linguistics , Open Linguistics , and Sign Language Studies . Recipient of ERC funding for projects on nonmanual properties and sign language processing. Co-authored a textbook on sign language linguistics with Svetlana Burkova ( Vvedenie v lingvistiku zhestovyh jazykov ). Recent projects involve developing benchmark datasets (e.g., FluentSigners-50) and tools for sign language recognition, reflecting his interdisciplinary approach to bridging linguistics and computational science.
Harsha Ratnaweera is a full-time Professor of Water and Wastewater Engineering at the Department of Civil and Environmental Engineering, Faculty of Natural Sciences and Technology, Norwegian University of Life Sciences (NMBU), where he has been employed since 2012 (previously as an Adjunct Professor from 2001). He previously served as Director of International Projects and Innovation at the Norwegian Institute for Water Research (NIVA) from 1991 to 2012. His educational background includes a Dr. Ing. in Civil Engineering from the Norwegian University of Science and Technology (NTNU, 1992) and an MSc (Hons) in Chemical Engineering from the National Technical University of Ukraine KPI. Harsha's research focuses on modeling and optimization of coagulation, real-time monitoring and control of treatment processes, membrane technologies, virtual sensors, biofilm systems, and digital tools for wastewater systems. He has pioneered work in process automation, sensor validation, and holistic optimization of water systems. His research also extends to harmonizing graduate water education across Asia, Africa, and Eurasia. The 15 most recent publications reflect a strong trend toward digitalization in water treatment, including AI-based fault detection, machine learning for sensor estimation, cloud-based forecasting, and deep learning for process control. There is also significant emphasis on hybrid treatment systems, resource recovery (e.g., phosphorus, metals), and advanced monitoring of micropollutants like PFAS and pharmaceuticals. IWA Distinguished Fellow People’s Ambassador of Shandong Province, China Qingdao International Scientific Cooperation Award High-End Foreign Expert Fellowships (China, multiple) Fellow, European Academy of Sciences Fellow, Norwegian Academy of Technological Sciences Vice President, European Water Association (EWA) Director, IWA Board Harsha has led numerous major international research and educational projects funded by EU Horizon, Erasmus+, NORAD, and the Research Council of Norway. He has advised multiple master’s and PhD students and leads the Process Analytics and Water Treatment Group at NMBU. He has also served as Head of Research at the Faculty of Science & Technology and held leadership roles in UNESCO and UNECE initiatives. He is the founder and chairman of DOSCON AS and has led over 20 major international projects, including WaterHarmony.net, MEMPREX, and Water ESSENCE, involving more than 100 universities across 54 countries. He has organized multiple international conferences and serves on scientific advisory boards for Aquateam and EU ERA-NET Water JPI.
Weihai Yu is an Associate Professor at the Department of Computer Science, UiT The Arctic University of Norway. His research focuses on distributed systems, collaborative editing, conflict-free replicated data types (CRDTs), edge computing, and decentralized service orchestration. He leads the Open Distributed Systems (ODS) research group and contributes to projects like the Conflict-free Replicated Relation (CRR) and Nudge Project. Yu has authored over 50 publications since 2009, with recent work emphasizing replicated data streams, undo mechanisms in collaborative systems, and edge-cloud integration. Key research interests include: distributed database replication, real-time collaborative editing with CRDTs, fault-tolerant service orchestration, and asynchronous systems design. His work bridges theory and practice, addressing challenges in consistency, scalability, and user experience in distributed applications. Publications trends show a strong focus on CRDT advancements (e.g., low-cost set CRDTs, generic undo support) and edge computing applications. He collaborates extensively with industry partners on projects like the Nudge Project, exploring IoT-driven transportation systems. Yu's contributions are published in top venues such as Springer Nature, ACM, and IEEE journals/conferences. Maintains the Open Distributed Systems (ODS) group at UiT, focusing on collaborative systems and distributed computing innovations. Current research includes local-first software architectures and conflict-free replicated relations for multi-synchronous database management.
Lemei Zhang is a Postdoctoral Fellow at the Norwegian University of Science and Technology (NTNU) in the Department of Computer Technology and Informatics. Their work focuses on advanced machine learning techniques for recommendation systems and social media analysis. Research interests include Deep learning architectures for news recommendation Aspect-based sentiment analysis in multilingual contexts Temporal and graph embedding methods for social recommendation Contextual augmentation in session-based systems Time series modeling for user interest prediction Creation of large-scale datasets like Adressa for media analytics Recent publications demonstrate a trajectory from foundational work on news recommendation datasets (2016-2017) to increasingly sophisticated neural approaches incorporating attention mechanisms, multimodal fusion, and real-time graph analysis across ACM Transactions, Machine Learning, and Human-Computer Studies venues.
Katrien De Moor is an Associate Professor at the Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), with research focusing on socio-technical approaches in ICT. Her work bridges user experience, digital ethics, and cybersecurity, particularly in immersive environments and next-generation networking. Co-Editor-in-Chief, Quality and User Experience (Springer) Steering Committee, QoMEX Member, Young Academy of Norway Her research spans Quality of Experience (QoE) , User Engagement , and Digital Ethics , with projects like TufiQoE (2020-2024) on ecological validity in immersive media evaluation, and Digital Carbon Footprint studies. Since 2023, she leads NTNU's 5-year Master program in Cybersecurity and Data Communication. She supervises multiple PhD students including Camille Sivelle (Secure XR Experiences), Kaja Ystgaard (Smart Environments), and Ole Martin Edstrøm (Cryptology Modeling). Current projects like Amplify (EU-funded) and Societal Security and Digital Identities (NFR-funded) emphasize ethical AI and human agency. Scientific Recognition: Founding member, Young Academy of Norway Editorial roles in TOMM and QoMEX Conference leadership at ACM IMX and MMSys Her lab collaborates with the Department of Sociology and Political Science at NTNU on cryptology's social dimensions, while mentoring early-career researchers through the PERSEUS Doctoral Programme.
Arne Styve is an Associate Teaching Professor at the Department of Information and Communication Technology and Natural Sciences, Norwegian University of Science and Technology (NTNU). He serves as a Studyprogram coordinator for Computing Science and teaches object-oriented programming and system development at the Bachelor level, along with simulation and visualization at the Master level. Education: B.Eng. w/Hons (Sivilingeniør) in Microelectronics and Software Engineering, University of Newcastle upon Tyne Research Interests: Specializing in Agile development, simulation modeling, and technology education. His work focuses on programming pedagogy, digital twin applications, and co-simulation frameworks. Recent research explores integrating generative AI into critical thinking practices in programming courses. Publication Trends: His publications over the past decade demonstrate expertise in simulation modeling for maritime systems, educational technology innovations, and software engineering frameworks. Key themes include digital twins, flipped classroom methodologies, and cross-campus programming education. Teaching Contributions: Instructs courses including IDATA2505 (Practice-Based Learning), IDATT2003 (Programming 2), and IDATA2302 (Algorithms & Data Structures). His pedagogical approach combines industry experience with academic innovation. Industry Experience: Over 20 years in Norwegian IT industry, including roles at Offshore Simulator Center (OSC AS), developing advanced maritime simulators and command control systems for defense applications.
Toril A. Nagelhus Hernes is a Professor of Medical Technology and Pro-Rector for Innovation at the Norwegian University of Science and Technology (NTNU). She leads NTNU's strategic innovation initiatives and maintains industry partnerships while contributing to medical technology research. Current role in Rector's management team Focus on healthcare technology commercialization Advocate for medical research funding Active in diversity and patient safety efforts Her research bridges medical imaging , minimally invasive surgery , and ultrasound simulation , with over 20 years of contributions to image-guided therapy systems and surgical training platforms. Recent publications focus on 3D reconstruction , endoscopic navigation , and haptic feedback limitations in surgical simulators. She contributes to both technical development and healthcare policy discourse. She actively collaborates with St. Olavs Hospital , C3: Centre for Connected Care , and European Surgical Research networks to advance medical technology adoption in clinical practice.
Ermes Franch is a Postdoctoral Fellow at the Department of Informatics, University of Bergen, Norway. He is actively engaged in research in coding theory and cryptography, contributing to both theoretical developments and cryptanalytic investigations. Research Interests: His work primarily focuses on coding theory , especially low-rank parity-check codes, and their applications in cryptography . He investigates algebraic constructions of error-correcting codes, decoding algorithms, and the security of post-quantum cryptographic schemes such as Integer-RLWE. His research lies at the intersection of information theory, algebra, and computer security. Publication Trends: His recent publications show a consistent focus on generalizations and algorithmic improvements in rank-metric coding, with increasing contributions to high-impact IEEE journals. The work demonstrates a trajectory toward both theoretical depth and practical relevance in secure communications. Scientific Funding and Projects: Associated with the project Sequences and Their Applications funded by the Research Council of Norway (Reference: 311646). Advising and Grants: While no formal students are listed, his collaborative publications with senior researchers like Chunlei Li and Philippe Gaborit suggest active participation in a research group. His work is supported by national research grants, indicating recognition of his contributions in the field. Labs and Research Teams: He is affiliated with the Department of Informatics at the University of Bergen, contributing to research in sequences, coding, and cryptography, likely as part of a larger research group focused on theoretical informatics and security.
Anders Rønnquist is a Professor and Head of the Department of Structural Engineering at NTNU’s Faculty of Engineering Science. His research focuses on structural dynamics, railway systems, and bridge engineering. He leads projects on structural health monitoring, wind engineering, and architectural collaboration in structural design. Key collaborations include work with KTH Stockholm, Vilnius University, and the University of Porto. He teaches courses such as Structural Dynamics, Steel Structures II, and Railway Catenary Systems. His work integrates advanced sensor technologies, machine learning, and probabilistic methods to address challenges in infrastructure longevity and safety. Research interests span structural monitoring, overhead contact line dynamics, and reliability assessments. Notable contributions include the development of Kalman filter-based systems for crosswind load identification and shape grammar approaches for architectural-structural design integration. His team has pioneered non-contact measurement techniques using digital image correlation and advanced neural networks for bridge rivet inspection. Publications highlight innovations in railway catenary optimization, timber structure dynamics, and probabilistic fatigue analysis. His work emphasizes interdisciplinary approaches, bridging engineering and architectural disciplines to enhance sustainable infrastructure solutions.
Hilde Leikny Sommerseth is a Professor of Historical Demography at UiT The Arctic University of Norway, affiliated with the Department of Archaeology, History, Religious Studies and Theology. She serves as Academic Director of the Historical Population Data Lab (HistLab) and leads externally funded projects like ' Persistence or change? – Lessons from the introduction of the unitary school in Denmark and Norway (2023-2027) ' under FRIPRO funding. Collaborations : National Archives, Norwegian Institute of Public Health, Statistics Norway, Norwegian Central Statistical Office, National Library, Norwegian School of Economics Editorial Roles : Editor of Historical Methods, A Journal of Quantitative and Interdisciplinary History ; board member of Michael (Norwegian medical journal); advisor to international register projects Her research spans historical demography, generational studies, and social inequality in health , with a focus on 1801-1964 Norwegian microdata. Recent work examines automated cause of death coding using machine learning and infant mortality patterns in Trondheim (1830-1910). Scientific contributions include developing AI-constructed population methodologies and analyzing transgenerational health impacts of economic hardship in preindustrial Norway. She also investigates validity of self-reported education in the Tromsø Study and age-specific mortality in Arctic communities during pandemics. HistLab under her leadership formats historical population data for international research access , including a public platform with interactive analysis tools. Current teaching includes HIS-1035 Slektshistorie , applying historical methods to family history research.
Haakon Kristian Kvidaland serves as a PhD Candidate at the Department of Health and Caring Sciences, Western Norway University of Applied Sciences. He is employed at the Institute for Master's in Clinical Nursing in a PhD fellowship position, conducting research while teaching specialized courses in physiotherapy and respiratory medicine. Dr. Kvidaland completed his physiotherapy education at the Western Norway University of Applied Sciences and earned a Master's in Health Sciences from the University of Bergen. His clinical background includes work in primary healthcare with Bergen municipality and secondary care at Haukeland University Hospital, where he developed expertise in exercise-induced laryngeal obstruction (EILO) and long-term respiratory conditions. His research focuses on respiratory pathophysiology , particularly EILO diagnostics and management, with significant contributions to understanding its relationship with asthma. His scholarly work extends to COPD follow-up protocols in general practice, childhood cancer survivorship issues, and neuromuscular disorders affecting pediatric populations. Dr. Kvidaland integrates clinical insights with innovative methodologies including machine learning applications for respiratory diagnostics. As an educator, he teaches Pulmonary Function Measurements, Cardio Pulmonary Exercise Testing, and Skill Training in Physiotherapy. His upcoming teaching responsibilities include FYS300 (Physiotherapy Assessment Management and Evaluation) for Fall 2025 and Spring 2026 semesters, plus FYS390 (Bachelor's Thesis) in Spring 2026. At Haukeland University Hospital, Dr. Kvidaland previously managed quality assurance at the heart and lung test laboratories within the Vitality Center for Children and Youth. His dual expertise in clinical practice and research enables him to develop evidence-based approaches for diagnosing and treating complex respiratory conditions, particularly through randomized controlled trials examining EILO treatment efficacy and prevalence among asthmatic patients.
