Evrim Acar Ataman is a Research Professor and Chief Research Scientist at Simula Metropolitan, where she serves as Head of the Department of Data Science and Knowledge Discovery. Her research focuses on advanced data mining techniques for complex, multi-modal datasets across biomedical and network domains. Her primary research interests include Data Mining , Matrix and Tensor Factorizations , and Data Fusion for multi-modal data analysis. She develops constrained and coupled factorization methods to extract interpretable patterns in applications spanning neuroimaging, metabolomics, and mobile network analysis, with emphasis on dynamic and longitudinal data structures. Her work integrates mechanistic models with data-driven approaches to enhance biological and system understanding. Analysis of her recent publications (2024-2025) reveals a dominant trend applying tensor and coupled matrix-tensor factorizations to biomedical data for biomarker discovery, particularly in metabolomics and neuroimaging. Key innovations include tracking evolving patterns in temporal data (tPARAFAC2), constrained fusion methods (dCMF), and integration of mechanistic models with tensor decompositions for longitudinal analysis. As Head of the Department of Data Science and Knowledge Discovery, she leads research in developing novel data mining methodologies and their real-world applications at Simula Metropolitan, with significant contributions to interpretable AI for complex systems.
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 .
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.
Anne H Schistad Solberg is a Professor in the Department of Informatics at the University of Oslo's Faculty of Mathematics and Natural Sciences. She leads research in digital signal processing and image analysis, with a focus on machine learning applications across multiple domains. As co-director of SFI Visual Intelligence, she oversees research on interpretable deep learning models, uncertainty quantification, contextual learning, and self-supervised learning approaches. Her research spans medical imaging (particularly cardiovascular ultrasound), environmental monitoring using satellite imagery, and seabed mapping with sonar technology. Professor Solberg's work demonstrates a consistent trajectory from foundational signal processing techniques to cutting-edge deep learning applications. Her recent publications show increasing specialization in medical image analysis, particularly in echocardiography enhancement and cardiac structure segmentation, while maintaining strong contributions to remote sensing and geophysical applications. She teaches several popular courses including IN2070, IN3310, and IN5400 (Machine Learning for Image Analysis), which is noted as the most popular master's/PhD course on deep learning at the University of Oslo. Professor Solberg serves as principal investigator for the Intelligent Cardiovascular Ultrasound Scanner (INCUS) project, collaborating with GE Vingmed Ultrasound to develop AI-enhanced cardiac imaging systems that improve diagnostic accuracy and productivity in echocardiography. Co-director of SFI Visual Intelligence research center Principal Investigator for the INCUS project (Intelligent Cardiovascular Ultrasound Scanner) Member of the Digital Signal Processing and Image Analysis (DSB) research group Member of the Strategic Research Initiative: Multimodal Medical Imaging and Image Analysis (MEDIMA) Her research group develops algorithms that address real-world challenges in medical diagnostics and environmental monitoring, with a particular emphasis on making deep learning models more interpretable and reliable for critical applications. The INCUS project, funded through User-driven Research-based Innovation (BIA), aims to reduce the time wasted during cardiac ultrasound examinations by implementing intelligent algorithms that learn from expert users and historical data.
Toril Aalberg is a Professor and Head of the Department of Sociology and Political Science at the Norwegian University of Science and Technology (NTNU) in Trondheim. She was previously Head of the same department from 2017-2021 and serves as Guest Professor at Mid Sweden University in 2024-2025. Aalberg is a member of the research networks NEPOCS (Network of European Political Communication Scholars) and EVPOC (Elections, Values and Political Communication). Her research interests include comparative politics , election campaigns , media effects , public opinion , and stereotypes in political contexts. She has led major projects like the COST Action IS1308: Populist Political Communication in Europe (2014-2018). Toril Aalberg has authored or co-authored 9 books, including Populist Political Communication in Europe (2016) and Communicating Populism (2019). Her recent articles focus on misinformation during crises , media’s role in political efficacy , and cross-national disinformation studies , often analyzing data from 17-19 democracies. She has conducted extensive research on media polarization , immigrant attitudes , and news consumption patterns . Aalberg has held visiting appointments at institutions including the University of California, Berkeley , Stanford University , and University of Oslo , with current affiliations at the Centre for Advanced Studies in Oslo and Mid Sweden University . Her work spans political communication theory , media sociology , and democratic processes .
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.
Anders Åkerman is a Professor in the Department of Economics and Finance at the UiS School of Business and Law, University of Stavanger. He is an active researcher in applied economics with a focus on labor, trade, technology, and environmental policy. His research interests span applied microeconomics , international trade , labor economics , and environmental economics . He investigates how technological change, market structure, and global shocks affect firms, workers, and policy outcomes. His work often combines empirical methods with policy relevance. The recent articles reflect a strong trend in analyzing digital transformation (e.g., broadband and trade), environmental impacts of trade and production , and economic resilience during crises such as the pandemic. His publications appear in leading journals like the Quarterly Journal of Economics and American Economic Journal: Applied Economics . He has contributed to public discourse through op-eds in Dagens Nyheter and Svenska Dagbladet , and has been involved in policy commissions like Sweden's Coronakommissionen. He regularly presents research at seminars and international conferences, including the Nordic International Trade Seminar and Stockholm Institute of Transition Economics. Anders Åkerman has advised or collaborated with several researchers and policy experts, including Torsten Persson, Magne Mogstad, and Karolina Ekholm. While formal student advisees are not listed, his collaborative work suggests active mentorship and team leadership. He is affiliated with research networks and institutes such as IFN (Research Institute of Industrial Economics) and participates in interdisciplinary teams focused on economic policy and transition. His upcoming work continues to explore the intersection of technology, environment, and labor markets.
Katrine Eldegard is a Professor at the Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), Department of Ecology and Natural Resource Management (INA). She leads BatLab Norway and contributes extensively to national and international conservation science policy. Institution: Norwegian University of Life Sciences School: Faculty of Environmental Sciences and Natural Resource Management Department: Department of Ecology and Natural Resource Management Position: Professor Her research centers on understanding how human activities and land use affect natural ecosystems and species across taxa and spatial scales. She specializes in the behavioral, population, and community-level responses of mammals, birds, and insects to anthropogenic pressures such as energy infrastructure, transport networks, and forestry. A major focus is on bat ecology and conservation, pollination dynamics, and biodiversity monitoring in boreal and agricultural landscapes. Her recent publications reveal strong trends in climate change impacts on bat morphology and distribution, pollinator-plant interactions under environmental change, and the ecological consequences of infrastructure development. These works integrate field ecology with advanced modeling and policy-relevant assessments. She has played leading roles in key scientific committees: Chair, Mammal Committee, Norwegian Red List for Species (2021) Chair, Mammal Committee, Norwegian Alien Species List (2023) Member, Norwegian Scientific Committee for Food and Environment (VKM), CITES Expert Panel Norway’s representative, UNEP/Eurobats Advisory Committee Eldegard has supervised numerous research projects and collaborated with government agencies and private partners on applied ecology. She teaches courses including NATF200 Vern og forvaltning av norsk natur and the upcoming NATF300 Conservation Science. Her work is supported by extensive fieldwork, interdisciplinary collaboration, and integration of ecological theory with practical conservation. She leads BatLab Norway, a research group dedicated to advancing knowledge on bat ecology, behavior, and conservation through innovative methods including telemetry, acoustic monitoring, and landscape analysis.
Marco Heurich serves as the head of the 'Monitoring and Open-Air Wildlife Enclosures' department at the Bavarian Forest National Park. He is also a Professor of Wildlife Ecology and Wildlife Management at the University of Freiburg and the Inland Norway University of Applied Sciences. Research Focus: Combines modern technologies (e.g., telemetry, remote sensing, automated image processing) with classical ecological methods to study near-natural ecosystems and enhance their conservation. Expertise: Recognized as one of Europe's leading authorities in wildlife and forest ecology, contributing to large-scale nature conservation and wildlife monitoring advancements. Academic Contributions: Authored or edited over 350 peer-reviewed articles and six books, emphasizing ecological research and conservation strategies. Actively promotes knowledge transfer into practice through co-founding national and international networks in wildlife management and nature conservation.
Professor Anne Burmeister is a full Professor of Organizational Behavior at the University of Cologne, Faculty of Management, Economics, and Social Sciences. She specializes in knowledge transfer, age diversity, inclusion, and interpersonal dynamics in workplaces. Her research bridges organizational psychology with practical applications in aging and diverse teams. Education: PhD in Psychology (summa cum laude), Leuphana University of Lueneburg (2016) MSc in Management and Organizational Analysis, Warwick Business School (2011) BSc in Business Psychology, Leuphana University of Lueneburg (2010) Research Interests: Her work focuses on work and aging, age diversity, inclusion, knowledge transfer, and workplace friendships. She investigates how social interactions influence productivity and organizational health, particularly in multigenerational environments. Recent Trends: Recent publications analyze DEI initiative responses, knowledge sharing dynamics, and interventions for aging workforces. Themes include cognitive load theory, intergenerational collaboration, and ambivalent employee reactions to diversity policies. Scientific Recognition: Awarded 40 leading HR minds 2025 by HR magazine Contact: Email: burmeister@wiso.uni-koeln.de Phone: +49 221-470-5887
Ali Ramezani-Kebrya is an Associate Professor with tenure in the Department of Informatics at the University of Oslo (UiO), where he leads research in machine learning theory. He holds dual Principal Investigator roles at the Norwegian Center for Knowledge-driven Machine Learning (Integreat) and SFI Visual Intelligence, and is an active member of the European Laboratory for Learning and Intelligent Systems (ELLIS) Society. His service includes Area Chair positions for NeurIPS and AISTATS, and Action Editor for Transactions on Machine Learning Research. His research focuses on theoretical foundations of deep learning with emphasis on understanding input data distribution encoding in neural network layers. Key themes include minimizing statistical risk under resource constraints, addressing distribution shifts in distributed settings, and developing practical tools for robust federated learning. Current applications span emotion recognition, marine data analysis, and neuroscience, reflecting his commitment to real-world machine learning challenges as evidenced by his FRIPRO-funded Machine Learning in Real World (MLReal) project. Recent publication trends reveal three dominant threads: (1) label/covariate shift mitigation in distributed systems through entropy regularization and density ratio estimation; (2) communication-efficient optimization via layer-wise quantization and adaptive compression techniques achieving 150% speedups; and (3) robustness guarantees against tailored attacks and distribution shifts. These works consistently bridge theoretical bounds with empirical validation across domains from GAN training to federated settings. Scientific recognition includes: FRIPRO Grant for Early Career Scientists (2025) for MLReal project SFI Visual Intelligence Spotlight Publication award (2023) for federated learning work He actively mentors 11 graduate students across Oslo and Tromsø universities, with recent PhD placements at Apple and NVIDIA. Current grant portfolio features the FRIPRO Early Career award and leadership roles in two major Norwegian research centers. His lab maintains strong industry collaborations through Vector Institute and EPFL, with recent hiring for PhD and postdoc positions in physics-informed machine learning.
Mona Baker is an Affiliate Professor at the Centre for Sustainable Healthcare Education (SHE) , University of Oslo, and a renowned scholar in translation studies. Her work bridges political, cultural, and social dimensions of translation, with a focus on conflict, activism, and corpus-based methodologies. She holds a BA in English and Comparative Literature from the American University in Cairo (1976), an MA in Special Applications of Linguistics from the University of Birmingham (1987), and a Higher Doctorate from UMIST (1999). Baker has also held prestigious roles, including founding the Baker Centre for Translation and Intercultural Studies at Jiao Tong University and Beijing Foreign Studies University. Her research interests span translation and conflict , activist translation , corpus-based studies , and the role of translation in social movements . Key projects include the Genealogies of Knowledge initiative, which examines conceptual evolution through translation, and studies on subtitling during the Egyptian Revolution. Baker's recent publications highlight the intersection of technology and activist translation , including analyses of evidence-based medicine narratives and solidarity in global protest movements. She has received notable scientific awards, such as the Kuwait Foundation for the Advancement of Sciences Award (2015) and the Abdullah Bin Abdulaziz International Award (2011). Her academic leadership includes advisory roles at institutions like the Qatar Foundation and Shanghai International Studies University , as well as participation in critical research groups such as the Antibiotic Resistance and Complexity in Health Education network. Baker continues to influence debates on global health, citizen media, and the ethics of translation through her work and mentorship.
Rune Svarverud is a Professor at the Department of Culture, Religion, Asian and Middle Eastern Studies, Faculty of Arts, University of Oslo. His research spans two major periods in Chinese history: early Chinese philosophy (pre-Qin to Han dynasties) and modern China (post-Opium War cultural encounters). Key research areas include conceptual history, environmental history, political philosophy, and cross-cultural translation studies. Permanent employee since 2004 Head of Department since 2021 Former PhD committee member and research leader (2007-2014) Active in international collaborations with Fudan, Nanjing, Kansai, and Zhejiang Universities His academic work focuses on: History of concepts and textual analysis in Chinese thought Environmental history and terminology evolution in modern China Qing dynasty legal and political philosophy Translation of Western knowledge into Chinese Interactions between Chinese and Western modernity Individualism in 20th-century Chinese society Recent publications examine environmental terminology, cross-cultural scientific translation, and conceptual frameworks in Qing reform. His interdisciplinary approach connects philosophy, law, and environmental studies through historical analysis. Co-edited works on standard languages and cultural communication Contributor to ACTA ORIENTALIA and PNAS Recipient of multiple research grants for Transsustain project
Roger Flage is a Professor of Risk Management at the University of Stavanger, affiliated with the Faculty of Science and Technology and the Department of Security, Economics and Planning. His research focuses on foundational and applied aspects of risk analysis, uncertainty quantification, and decision-making under uncertainty, with applications in critical infrastructure, environmental systems, and offshore energy. Roger Flage's research interests lie at the intersection of risk science, safety engineering, and decision theory. He investigates how uncertainty—especially epistemic uncertainty and assumptions—affects risk assessments, and advocates for more transparent and robust frameworks. His work spans theoretical advances, such as the treatment of 'black swan' events and the concept of 'real risk', as well as practical applications in offshore safety, power systems, and geohazards. He emphasizes the integration of data-driven methods, AI, and digital twins while critically assessing their limitations and associated security risks. His recent publications show a strong trend toward integrating dynamic, data-rich, and interdisciplinary approaches to risk analysis. Themes include the role of time in risk, AI applications, infrastructure interdependencies, and environmental risk in the oil and gas sector. He frequently publishes in top-tier journals like Risk Analysis , Reliability Engineering & System Safety , and Safety Science , often in collaboration with leading scholars such as Terje Aven and Seth Guikema. No scientific awards are mentioned in the provided text. Roger Flage has supervised or collaborated with several researchers, though no formal list of advisees is provided. His work is supported through academic collaborations and institutional affiliations rather than explicit grant mentions. He is actively involved in advancing risk science methodology, particularly in the treatment of assumptions and uncertainty, and contributes to both theoretical foundations and real-world applications in safety-critical domains. He is associated with research groups and collaborative networks at the University of Stavanger, particularly within the Department of Security, Economics and Planning. His work often involves interdisciplinary teams focusing on risk in complex engineered systems, including energy, transportation, and environmental systems.
