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.
Daniel M. Roy is a Full Professor at the University of Toronto, with cross-appointments in the Department of Computer Science, Department of Statistical Sciences, and Department of Electrical and Computer Engineering. He serves as Research Director at the Vector Institute and holds the CIFAR Canada AI Chair. Research Focus: Foundational principles of prediction, inference, and decision-making under uncertainty across machine learning, statistics, mathematical logic, applied probability, and computer science. Scientific Contributions: Key work in learning theory, statistical network analysis, probabilistic programming, and information-theoretic frameworks for generalization. Awards: ICML 2024 Best Paper Award for "Information Complexity of Stochastic Convex Optimization" and promotion to Full Professor in 2024. Student Advising: Actively mentors Ph.D. candidates and postdoctoral researchers with strong quantitative backgrounds, particularly at the intersection of machine learning, statistics, and computer science. Email: daniel.roy@utoronto.ca
Robert Jenssen is a Professor in the Machine Learning Research Group at UiT The Arctic University of Norway and serves as the Director of Visual Intelligence , an 8-year Research Council of Norway-funded SFI center. His research focuses on solving societal challenges in healthcare, marine mapping, energy, and Earth observation through collaborations with industry and public stakeholders. Director, Visual Intelligence (SFI) Center Professor, UiT Adjunct Professor, Pioneer Centre for AI (University of Copenhagen) and Norwegian Computing Center His methodological expertise spans neural networks, information-theoretic learning, self-learning, and explainable AI (XAI). Recent work emphasizes multimodal learning, uncertainty estimation, and medical image analysis. Scientific awards include: Best Paper, Pattern Recognition Letters (2024) Dissertation Award, Norwegian AI Society (2023) Best Paper, Color and Visual Computing Symposium (2022) IEEE GRS Society Letters Prize (2013) Prize for Young Researchers, University of Tromsø (2007) He contributes to international leadership as a member of the Scientific Advisory Board (SAB) for the Max Planck Institute for Intelligent Systems, France's SequoIA AI Excellence Cluster, and Denmark's DIREC center.
Johan Gustav Bellika is a Professor at the Department of Clinical Medicine, UiT The Arctic University of Norway, based in Tromsø. His work bridges clinical medicine with health informatics, focusing on practical applications that improve healthcare delivery and patient outcomes. Current Position: Professor, Department of Clinical Medicine Institution: UiT The Arctic University of Norway Location: Tromsø, Norway Contact: johan.gustav.bellika@uit.no Professor Bellika's research spans several critical areas in modern healthcare. His primary focus is on health informatics with particular emphasis on medical data privacy, electronic health records, and the application of artificial intelligence in clinical settings. He has made significant contributions to understanding chronic pain management through technology, primary care research networks, and patient-centered care models. His work often involves large population studies such as the Tromsø Study, examining how digital health tools impact healthcare utilization and patient outcomes. His research demonstrates a consistent thread connecting technological innovation with practical clinical applications. Bellika has been instrumental in developing privacy-preserving architectures for healthcare data analysis, which enable researchers to gain insights from sensitive health information without compromising patient privacy. His work on federated learning frameworks represents cutting-edge approaches to analyzing medical data while maintaining strict privacy controls. Health Informatics and Medical Data Privacy Chronic Pain Management through Technology Primary Care Research Networks (PraksisNett) Electronic Health Records and Clinical Decision Support Patient-Centered Digital Health Solutions Federated Learning Applications in Healthcare Professor Bellika's publication record shows a strong trend toward interdisciplinary research that combines clinical medicine, computer science, and public health. His recent work increasingly focuses on the intersection of artificial intelligence and healthcare, particularly how machine learning can be applied to chronic conditions while maintaining rigorous privacy standards. The geographical scope of his research extends across Norway with particular emphasis on Northern Norwegian populations, providing valuable insights into healthcare delivery in Arctic and remote regions. His collaborative approach is evident through numerous co-authored publications with researchers across multiple institutions and disciplines. While specific awards aren't detailed in the available information, his extensive publication record in reputable journals suggests recognition within his field. Professor Bellika has been actively involved in several major research initiatives including the Tromsø Study and PraksisNett, Norway's nationwide practice-based research network. His work on privacy-preserving architectures for healthcare data has significant implications for how medical research can be conducted while respecting patient confidentiality. He appears to be particularly focused on translating research findings into practical tools for clinicians, as evidenced by his work on audit and feedback systems for antibiotic prescribing.
Maren Toft is a Professor in the Department of Sociology and Human Geography at the University of Oslo's Faculty of Social Sciences. Her academic career has progressed from Research Assistant (2013-2014) to PhD Candidate (2014-2018), Postdoctoral Fellow (2018-2022), Associate Professor (2022), and now Professor. Her research focuses on: Class theory and theory of elites Social mobility and class structuration Segregation and urban inequality Social capital and networks of power Advanced quantitative methods including social sequence analysis and correspondence analysis Toft's publication record shows a consistent focus on elite formation, wealth accumulation, and class reproduction. Her work frequently employs longitudinal approaches to examine how social origins influence life trajectories, particularly among the upper classes. She has made significant contributions to understanding how class structures persist in Scandinavian welfare states, challenging assumptions about equality in these societies. Her major scientific awards include: His Majesty the King's Gold Medal for her 2018 PhD dissertation 'Biographies of privilege' Norwegian sociological association's award for best international publication in 2020/2021 Norwegian sociological association's award for best international publication in 2017 Kunnskap Oslo's award for best Oslo-related Research in 2015 Norwegian sociological association's award for research dissemination in 2018 Toft actively supervises PhD students including Iselin Hewitt (studying urban planning and gentrification), Marte Lund Saga (examining business-politics networks), and Stian Uvaag (researching sibling similarities in mobility). She serves on the board of both the Department of Sociology and Human Geography and the Norwegian Sociological Association. She is involved in several major research projects including HISTCLASS, The Oslo Register Data Classification (ORDC), The Salmon Elite (SALELITE), and Weaving the fabric of Elite Bonds (WEB), which collectively examine various dimensions of elite formation, power structures, and social inequality in Norway and internationally.
Taina Bucher is a Professor at the Department of Media and Communication, University of Oslo, specializing in algorithmic studies, digital infrastructure, and human-machine communication. She leads the Screen Cultures research group and serves as Principal Investigator for HumAIn , a five-year humanities hub reimagining AI (2024-2029). Research Focus: Her work examines Algorithmic power and politics Cultural imaginaries of digital technology Temporalities of algorithmic media Feminist epistemologies in digital contexts Monitorial citizenship in platform societies Publications: Bucher has authored two monographs: Facebook (Polity Press, 2021) and IF…THEN: Algorithmic Power and Politics (Oxford University Press, 2018). Her recent articles explore facial recognition systems, algorithmic temporality, and digital divides. Academic Background: PhD in Media Studies (University of Oslo, 2012), MSc in Culture & Society (London School of Economics, 2007), with visiting scholar appointments at The New School, Ryerson University, and New York University. Teaching: She supervises and teaches topics including Digital infrastructure Algorithm studies Screen cultures Media theory Critical data studies Digitalization of public sectors Editorial Roles: Active on editorial boards for Journal of Communication , New Media & Society , and Information, Communication & Society .
Aleksander Madsen is a Researcher at the University of Oslo (UiO), affiliated with the Department of Sociology and Human Geography under the Faculty of Social Sciences. His primary research interests include social stratification, immigrant assimilation in labor markets, welfare state policies, and statistical methods. He holds a PhD from Oslo Metropolitan University and an MA from UiO. Education: PhD in Sociology, Centre for the Study of Professions, Oslo Metropolitan University (2014–2018) MA in Sociology, University of Oslo (2013) His research explores labor market integration of immigrants, gender disparities in male-dominated workplaces, and the impact of education-occupation mismatch on health outcomes. He has conducted longitudinal studies on long-term sickness absence and labor market trajectories using sequence analysis. Dr. Madsen has held postdoctoral and research positions at UiO and NIFU, and was a visiting scholar at Stanford University's SCANCOR (2024) funded by the Fulbright Foundation. His work spans interdisciplinary collaborations, including projects on workplace diversity, ethnic stratification, and policy evaluations. Awards: U.S. - Norway Fulbright Foundation Grant (2023–2024) His research group affiliations include the Social Inequalities and Population Dynamics team and the ORGMIGRANT project, investigating work organizations and immigrant assimilation. Recent publications address residential networks among immigrants, workplace ethnic diversity, and women’s attrition from male-dominated sectors.
Dag Henrik Tuastad is a Professor of Middle East Studies at the Department of Culture Studies and Oriental Languages (IKOS) at the University of Oslo. His research focuses on kinship dynamics in Palestinian politics, rebel governance (notably Hamas rule in Gaza 2007–2023), political representation crises, and the intersection of sports and politics in the Middle East. Current projects include senior research at Football and Religion in the Middle East (FORM) , funded by the Norwegian Research Council (2021–2026) Former project leader for Rebel Governance in the Middle East (2017–2021) PhD in Social Anthropology (2008) on kinship in Palestinian politics His recent publications analyze Hamas’s symbolic authority, football’s role in Jordanian-Palestinian identity formation, and the erosion of legal frameworks in the West Bank. He critiques media bias (e.g., in Qatar World Cup coverage) and explores how football culture reflects broader political tensions. Teaching: Courses on Middle East history, cultural anthropology, and conflict resolution Regional Focus: Palestine, Jordan, Gaza, and the Levant
Hasan Cavusoglu is a Professor of Management Information Systems at the Sauder School of Business, University of British Columbia. He holds a Ph.D. in Management Science with a specialization in MIS from the University of Texas at Dallas. His research focuses on IT risks, cybersecurity, governance, and privacy, particularly in the context of online social networks and organizational data protection. Education: Ph.D., Management Science (UT Dallas); M.Sc., Management & Administrative Sciences (UT Dallas) Roles: Associate Editor of Management Information Systems Quarterly (MIS Quarterly) His work addresses challenges in cybersecurity preparedness, data breach management, and regulatory frameworks like GDPR. Cavusoglu has contributed to high-profile discussions on cybersecurity risks and policy, including analyses of the Mueller Report and the LifeLabs data breach. He frequently engages with media, offering expert insights on privacy, data security, and organizational IT governance strategies. Key areas of focus include risk-based security architectures, employee training against social engineering, and the strategic role of cybersecurity in business differentiation. His research has appeared in top-tier journals such as Management Science and Information Systems Research.
Marianne Nordli Hansen is a Professor at the Department of Sociology and Human Geography , University of Oslo. With a career spanning decades, her research focuses on social stratification , class and inequality , labor market conditions , economic inequality , and elites . She has taught courses like Sociological Theory (SOS4001), Theory Specialization in Sociology (SOS4011), and Inequality: Class, Gender and Ethnicity (SOS4100). Education: MA in Sociology (University of Oslo, 1984), Dr. philos in Sociology (University of Oslo, 1996) Positions: Associate Professor (ISS, UiO, 1996), Professor (ISS, UiO, 1999) Her research explores wealth accumulation , intergenerational mobility , social networks , and educational policy . Key collaborators include Professor Olav Korsnes and Associate Professor Johannes Hjellbrekke at the University of Bergen. Current projects focus on social inequality in Norway , with publications analyzing elite professions, wealth distribution, and class-based educational disparities. She has contributed to significant works on Nordic welfare models and public sector impacts on segregation and equality.
Espen Storli is a Professor at the Department of Modern History and Society, Norwegian University of Science and Technology (NTNU). His work bridges business history, political economy, and resource governance, focusing on international business development in the 20th century. He holds a PhD from NTNU (2010) and has held prestigious positions like the Harvard-Newcomen Fellowship in Business History (2011-2012). His research includes projects such as "The hidden companies of the global economy: the development of international commodity traders, 1945-2015" , funded by the Norwegian Research Council. He co-founded the History and Strategic Raw Materials Initiative , a network examining raw material politics. Research Interests : Business-political intersections Commodity trading history Strategic resource governance State-corporate dynamics Globalization processes Scientific Awards : Harvard-Newcomen Fellow in Business History (2011-2012) Teaching : Courses include environmental history, modern European history, and global political economy of resources. He has contributed to academic outreach through lectures at institutions like Harvard Business School and the World Economic History Congress.
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.
Andreas Görgen is a Professor in the Department of Physics at the University of Oslo, Norway, specializing in experimental nuclear physics. He has held this position since 2012, following an Associate Professorship at the same institution from 2010 to 2012. His prior experience includes a Staff Scientist role at CEA Saclay (2002-2010) and a Postdoctoral Fellowship at Lawrence Berkeley National Laboratory (2000-2002). His educational background is rooted at the Universität Bonn, where he earned his Diplom-Physiker in 1996 and Dr. rer. nat. in 2000. During his doctoral studies, he served as a Research Assistant at the Institut für Strahlen- und Kernphysik. Görgen's research centers on experimental nuclear physics, with a focus on nuclear structure, nuclear reactions, exotic nuclei, and resonances in atomic nuclei. He employs advanced techniques such as Coulomb excitation, in-beam spectroscopy, and gamma-ray spectroscopy to investigate nuclear shapes, shell evolution, and the properties of nuclei far from stability. Analysis of his recent publications (2016-2023) reveals a consistent emphasis on nuclear structure phenomena, particularly shape coexistence in neutron-rich isotopes (e.g., Sr, Zr, Sm), shell evolution near 78Ni, and the spectroscopy of exotic copper isotopes. His work also spans nuclear astrophysics (e.g., the Hoyle state in carbon-12) and medical physics applications (e.g., proton-dynamic therapy). No scientific awards were mentioned in the provided text. Details regarding student advising and research grants were not specified in the available information. However, Görgen is a key member of the Oslo Cyclotron Laboratory (OCL) team and maintains active collaborations with CERN/ISOLDE and GANIL/SPIRAL2, contributing to international nuclear physics research efforts. Görgen's primary research facility is the Oslo Cyclotron Laboratory (OCL), which provides experimental capabilities for nuclear structure studies using radioactive ion beams. His collaborative network extends to major European facilities, enhancing the scope and impact of his research program in fundamental and applied nuclear physics.
Michael Riegler is a Researcher at the AI Department, Simula Research Laboratory , focusing on interdisciplinary applications of Artificial Intelligence in healthcare, sports analytics, and multimedia systems. His work bridges Machine Learning , AI Alignment , and Applied AI across clinical and real-world domains. Key Affiliations: Simula Research Laboratory (AI Department Head) Research Themes: Explainable AI in medicine, multimodal data analysis, and AI-driven health monitoring Research Interests include: Developing AI/ML algorithms for medical imaging (e.g., polyp detection, embryo analysis) Addressing missing data challenges in healthcare through novel imputation techniques Creating multimodal virtual avatars for investigative interview training Designing edge AI systems for sports analytics and sustainable fishing Recent Publications highlight collaborations with institutions in Norway and globally, with a focus on: Medical Applications: Polyp segmentation, ECG analysis, and explainable models for disease detection Sports Analytics: Athlete performance prediction and soccer video processing Data Infrastructure: Lifelogging datasets (ScopeSense), semantic representation frameworks Labs & Teams include leadership in Simula’s AI Department and participation in projects like Medico Multimedia Task , ImageCLEF , and MediaEval workshops. His work emphasizes responsible AI innovation in public sectors and privacy-preserving systems for edge environments.
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 .
