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
Adam Zylbersztejn is an Assistant Professor (Maître de conférences HDR) at Université Lumière Lyon 2 and affiliated with GATE Lyon Saint-Etienne, a research group in economics. He holds a PhD from the Paris School of Economics (2013) and previously conducted postdoctoral research at WU Wien (Vienna University of Economics and Business). His research focuses on behavioral economics using experimental methods to study individual and interactive decision processes, particularly in contexts involving trust, strategic uncertainty, and cultural variability. Key research areas include behavioral game theory, experimental finance, and the impact of nonverbal communication in economic interactions. His work has been published in top journals such as Journal of Economic Psychology , Games and Economic Behavior , and Experimental Economics . Recent studies examine topics like anonymity effects in charitable giving, trust in market exchanges under oath, and cognitive processes in asset markets using eye-tracking technology. His ongoing projects include research on risk-incentive tradeoffs and ambiguity framing in decision-making. While no formal academic awards are listed, his work has received press coverage in outlets like Le Monde and CORTEX Mag . He collaborates widely with institutions such as GATE-LSE and the Paris School of Economics, contributing to both theoretical and applied economic inquiry.
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.
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 .
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.
Swati Aggarwal is a Professor in Artificial Intelligence at the Faculty of Logistics, Molde University College (HiMolde). Her research focuses on AI applications in healthcare, ethics, cognitive development, and neural networks. She holds a PhD in Neutrosophic Neural Networks, a Master's in Information Technology, and a Bachelor's in Computer Science and Engineering. Previously, she was a Marie Curie Postdoc Fellow at NTNU, working on AI models for cognitive assessment in infants (AIM_COACH project). Research Interests - AI in Health/Medicine - Ethics in AI and Societal Impact - EEG/BCI for Cognitive Assessment - Machine Learning and Deep Learning Publications Her recent work spans AI ethics, BCI applications, adversarial attacks, and healthcare diagnostics. Notable contributions include EEG-based infant perceptual monitoring (2025) and malaria detection via EfficientNet (2023). She also explores cross-lingual adversarial robustness and blockchain in hospitality systems. Labs/Teams - ABC-AI: Applied, Basic, and Conscientious AI Group - Virtual Technologies and Learning Research Group
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.
Marta Molinas is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). Her research spans multiple interdisciplinary domains with a focus on EEG technology and brain-computer interfaces. She actively supervises numerous Master's projects and maintains extensive international collaborations with institutions including Kavli Institute for Systems Neuroscience, RIKEN Center for Brain Science, University of Tsukuba, Juntendo University, and several European universities. Professor Molinas' research interests center on developing innovative EEG technologies, particularly her FlexEEG concept for reduced-channel EEG systems with brain imaging capabilities. Her work integrates signal processing, artificial intelligence, and neuroscience to create practical applications in mental health, sleep research, neurorehabilitation, and human-computer interaction. She specializes in EEG source imaging, machine learning for brain signal analysis, and the development of brain-computer interfaces for various applications including locked-in syndrome communication, ADHD treatment, and driver monitoring systems. Her publication portfolio demonstrates strong trends in interdisciplinary research combining neuroscience with electrical engineering and artificial intelligence. The work shows particular emphasis on developing practical EEG-based systems that minimize invasiveness while maintaining analytical power, with applications spanning healthcare, rehabilitation, and human augmentation. Her research bridges theoretical signal processing with real-world implementations through numerous student projects and international collaborations. Professor Molinas actively supervises a large team of Master's and PhD students across multiple projects, with each project typically requiring two students working collaboratively. Her research is supported through numerous international collaborations with institutions in Japan, India, and Europe, indicating substantial research funding and project leadership. She has developed a pipeline of student projects that build upon previous work, creating a cumulative knowledge base within her research group. She leads the EEG ITK research team at NTNU, which focuses on developing the FlexEEG headset prototype featuring flexible, wireless, dry electrodes designed to move across the scalp. This team works at the intersection of neuroscience, electrical engineering, and computer science, developing applications for sleep research, mental health monitoring, neurorehabilitation, and brain-computer interfaces. The team collaborates extensively with international partners including the Kavli Institute for Systems Neuroscience, the International Institute of Integrative Sleep Medicine at University of Tsukuba, and several engineering departments across Europe and Asia.
Helge Langseth is a Professor at the Department of Computer Technology and Informatics , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). His research focuses on Artificial Intelligence , Machine Learning , and Probabilistic Graphical Models , particularly Bayesian Networks and their applications in Decision Support Systems . Langseth's work addresses Explainable AI (XAI) , Reinforcement Learning , and Recommender Systems . He has contributed to Bayesian Optimization , Probabilistic Modeling , and Robotic Control in oceanic environments. His recent publications emphasize transparency , fairness , and scalability in AI systems, with applications spanning maritime trade, migraine diagnosis, and power grid management. He is affiliated with the Intelligent Systems Research Group at NTNU and actively mentors doctoral and master's students. Co-authored works with Yanzhe Bekkemoen , Sverre Herland , and Jørgen Hanssen reflect his role in advising the next generation of AI researchers.
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.
Ola Andres Erstad is a tenured Professor and Dean of the Faculty of Educational Sciences at the University of Oslo , with over three decades of academic leadership and research excellence. Specializing in Digital Literacy , Educational Technology , and Learning Sciences , his work bridges formal/informal learning and explores the sociocultural impacts of digital platforms on education. Academic Tenure: Professor (2008–present), Visiting Professor (UC San Diego, 2010–2011) Leadership: Institute Director (2015–2020), National PhD School Leader (2012–2014) Research Themes include: Digital Competence: Analyzing consequences of digitalization on youth education Learning Lives: Tracking cross-contextual educational trajectories in Groruddalen Platformization: Investigating digital platforms' impact on pedagogical structures Recent Publications (2022–2025) focus on dialogic learning in polarized societies, digital family ecosystems , and multimodal literacy . His international collaborations span Portugal, Denmark, and the U.S., with projects funded by NFR , COST Actions , and Science Europe . Currently leading the PlatFams initiative, Erstad explores cross-generational digital integration.
Jianhua Zhang is a Professor of Computer Science and founding deputy head of the AI Lab at the Department of Computer Science, OsloMet - Oslo Metropolitan University, Norway. He holds affiliations with the Faculty of Technology, Art and Design. His career includes roles as Scientific Director at Vekia (France), Head of Machine Learning Lab, and Professorships at East China University of Science and Technology and Beijing University of Technology. He has held visiting positions at TU Berlin, TU Dresden, and the University of Catania. Educations: PhD in Electrical Engineering and Information Sciences (Ruhr University Bochum, 2005), Postdoctoral Research at the University of Sheffield (2005-2006). Research focuses on artificial intelligence, computational intelligence, cognitive human-machine systems, neuroergonomics, affective computing, and AI-driven neuroergonomics. Applications span engineering, biomedicine, finance, and business. He has led over 20 large-scale projects and published extensively (4 books, 13 chapters, ~200 papers). Leadership roles include Chair of IFAC Technical Committee on Human-Machine Systems (2017-2023), Vice Chair of IEEE Norway Section, and editorial roles at journals like Frontiers in Neuroscience and Cognitive Neurodynamics . He organized major conferences like IFAC HMS2025 (Beijing) and ICMLT 2024 (Oslo). Awards: Stanford/Elsevier Top 2% Scientists (2023/2024), Senior Research Fellowship (CSC, 2012), Max Planck Fellowship (2011), Shanghai Pujiang Talent (2007), DAAD Scholarship (2002-2004). Grants and advising: PI for 20+ projects, advising PhD students in AI, machine learning, and control systems. Teaching includes courses on computational intelligence, IoT, and fuzzy systems at both undergraduate and graduate levels. Labs/Teams: AI Lab at OsloMet, Machine Learning Lab (Vekia), and collaborations with institutions globally. Current work emphasizes AI ethics, neuroergonomics in smart cities, and adaptive human-machine systems.
