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.
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
Norwegian University of Science and TechnologyNorway
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.
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.
Professor Mohan Lal Kolhe is a distinguished academic at the University of Agder , serving as a Full Professor in Smart Grid and Renewable Energy within the Faculty of Engineering and Science and the Department of Engineering Sciences . With over three decades of international academic experience, he has held positions at prestigious institutions including University College London, University of Dundee, and Hydrogen Research Institute in Canada. His career spans technical innovation, policy development (e.g., as a member of South Australia’s Renewable Energy Board), and extensive research leadership in sustainable energy systems. Research Leadership : Focus on Smart Grid integration, Electric Vehicles, Hydrogen Energy, Solar/Wind Systems, and Techno-Economic Energy Analysis. Global Recognition : Listed in the top 2% of scientists worldwide (2020-2023) by Stanford University, with 10 publications averaging 200+ citations. Recent publications emphasize advanced optimization techniques for renewable integration, EV charging infrastructure, hydrogen production, and power system stability. His work has secured competitive funding from entities like the Norwegian Research Council and EU programs. Awards and Expert Roles : Top 2% Global Scientist (Stanford, 2020-2023) Highly Cited Researcher (Top 10 publications, 200+ avg. citations) Expert evaluator for European Commission, Royal Society London, EPSRC, and Cyprus Research Foundation He actively contributes to international conferences as keynote speaker and editorial board member, with leadership roles in research groups like Autonomous and Cyber-Physical Systems and Energy Systems .
Heidi Johansen-Berg is Pro-Vice Chancellor (Strategic Initiatives) at the University of Oxford and Associate Head (Research and Innovation) in the Medical Sciences Division. She holds a Professorship in Cognitive Neuroscience and a Wellcome Principal Research Fellowship at the Nuffield Department of Clinical Neurosciences, where she leads the Plasticity Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). Her research centers on neuroplasticity mechanisms in the sensorimotor system, with emphasis on white matter plasticity, activity-dependent myelination, and implications for stroke rehabilitation and age-related brain decline. She integrates multimodal neuroimaging with behavioral studies to investigate how the brain adapts to learning, experience, and damage, translating findings into therapeutic interventions for neurological conditions. Recent publications reveal strong thematic trends in sleep-motor interactions post-stroke, exercise-induced neuroprotection in aging and adolescence, and experience-dependent white matter remodeling. Her work demonstrates how physical activity modulates brain structure-function relationships across the lifespan, with direct applications for neurorehabilitation protocols. Scientific recognition includes: Fellow of the Royal Society (FRS) Fellow of the Academy of Medical Sciences (FMedSci) Wellcome Principal Research Fellowship Professor Johansen-Berg directs the WIN Plasticity Group and co-leads the WIN Neuroplastics Network and Oxford University Centre for Integrative Neuroimaging (OxCIN). Her research program drives translational initiatives in stroke recovery and brain health maintenance, with ongoing projects examining digital sleep therapies, myelin dynamics, and exercise neuroscience through large-scale clinical trials and advanced imaging methodologies.
Kristine Beate Walhovd is a Professor at the Department of Psychology , University of Oslo, and co-leader of the Center for Lifespan Changes in Brain and Cognition (LCBC) . She is affiliated with the UiO:Life Science initiative and has led major projects like Lifebrain (€2.5M) and Neurocognitive Plasticity (ERC Starting Grant, €1.5M). Current roles: Professor (since 2016), Co-leader of LCBC Key affiliations: University of Oslo, Lifebrain Consortium, UiO:Life Science Her research focuses on lifespan brain and cognitive changes , examining both positive/negative developmental trajectories from age 4 to 90. She studies interactions between biomedical risks (e.g., Alzheimer's, fetal drug exposure) and cognitive outcomes, employing MRI , DTI , and ERP methodologies. Recent publications highlight work on Alzheimer's biomarkers , sleep-brain interactions , memory consolidation , and fetal brain influences . Her 2025 papers in Scientific Reports and Neurobiology of Aging examine hippocampal stability and brain network segregation. Awards include: 2015 University of Oslo Research Prize (shared with Anders Fjell) 2011 Member, Norwegian Academy of Sciences and Letters 2006 His Majesty the King's Gold Medal for best doctoral thesis She supervises doctoral candidates and teaches cognitive neuroscience , experimental methods , and thesis writing .
Markus Hovd is a Senior Lecturer at the Section for Pharmacology and Pharmaceutical Biosciences within the Department of Pharmacy at the University of Oslo's Faculty of Mathematics and Natural Sciences. He also maintains an affiliation with the Neurosurgical Department. His work focuses on pharmacokinetic research with applications in clinical practice and personalized medicine. Philosophiae Doctor (PhD), Department of Pharmacy, University of Oslo, 2024 Master of Pharmacy, Department of Pharmacy, University of Oslo, 2019 Hovd's primary research interest centers on the mechanisms behind pharmacokinetic inter-individual variability and the development of models to aid in personalization of medicine. His research methodology includes population- and physiology-based pharmacokinetic models, in vitro-in vivo extrapolation, and clinical trials. His work spans multiple therapeutic areas including kidney transplantation, obesity-related pharmacokinetics, and cerebrospinal fluid dynamics. Analysis of Hovd's publications reveals a strong focus on pharmacokinetic applications in specialized patient populations, particularly kidney transplant recipients and patients who have undergone bariatric surgery. His research demonstrates expertise in population pharmacokinetic modeling, drug transport mechanisms, and the impact of physiological changes on drug disposition. A significant portion of his work addresses the challenges of drug dosing in complex clinical scenarios where standard pharmacokinetic assumptions may not apply. Hovd serves as a representative for temporary teaching staff on both the Teaching Committee and the Department Board at the Department of Pharmacy. His teaching portfolio includes courses in Pharmaceutical Biochemistry, Pharmacotherapy, Pharmaceutical Microbiology, Research Preparation in Biology, and Applied Pharmacokinetics and Dosing in Clinical Practice. Hovd is actively involved in multiple research groups including Pharmacokinetics, Kidney Transplant Medicine, and the KG Jebsen Center for Cerebrospinal Fluid Research, where he contributes his pharmacokinetic expertise to interdisciplinary neuroscience research.
Alvaro Fernandez Quilez is an Associate Professor in Artificial Intelligence at the Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, University of Stavanger. He leads the Stavanger AI Laboratory (SAIL), fostering interdisciplinary AI research with a focus on healthcare and education applications. Research Interests: His work centers on responsible AI, emphasizing ethics, fairness, transparency, and uncertainty in AI systems. He applies deep learning and machine learning techniques to medical imaging, particularly in prostate cancer and neurodegenerative diseases like Alzheimer’s and Parkinson’s. His research integrates algorithmic innovation with clinical relevance, addressing challenges in data scarcity, bias, and model interpretability. The recent publications highlight a strong trend in developing and evaluating AI models for diagnostic support in radiology and neurology. Key themes include uncertainty quantification, self-supervised learning, synthetic data generation via GANs, and fairness analysis across gender and centers. The work spans from foundational AI methods to their clinical translation in multi-center studies. Teaching and Academic Leadership: He coordinates the course DAT105 - AI for everyone and has contributed as a guest lecturer in bioinformatics, technological foundations, and PhD ethics, particularly on AI and ethics. He is also enrolled in a PhD supervisory qualification program, underscoring his growing role in graduate education. Advising and Grants: While specific students and grants are not listed in the text, his leadership of SAIL and active publication record suggest involvement in research supervision and project funding. His collaborations span multiple institutions and disciplines, indicating strong team-based research efforts. Laboratories and Teams: He leads the Stavanger AI Laboratory (SAIL), which serves as the central hub for AI research at the University of Stavanger, promoting collaboration across departments and with external partners in healthcare and technology.
Efthymios Papatzikis is a Professor of Infant Brain Development at Oslo Metropolitan University’s Faculty of Education and International Studies. He leads the Advanced Health Intelligence and Brain-Inspired Technologies (ADEPT) Research Group, focusing on brain development in the first 1500 days of life using neuroimaging, behavioral analysis, and AI-driven tools. Research Focus: Multimodal neuroimaging (qEEG, ABR, aEEG), computational neuroscience, AI in NICU diagnostics, and personalized sound/music interventions. Collaborations: Harvard University, Martinos Center for Biomedical Imaging, UCL, and Bergen University. Editorial Roles: Associate Editor for Frontiers in Pediatric Psychology, Guest Editor for Frontiers in Pediatric Neurology. His work bridges neuroscience with clinical neonatal care, emphasizing family-centered medicine and precision diagnostics. Recent projects include EU Cost Action CA22111 on real-world environments’ impact on brain development. Scientific Awards: Fellow of the Higher Education Academy (FHEA), UK Certified Specialist in Social Prescribing by the World Health Organization He contributes to journals as reviewer and editor, co-develops computational tools for NICU EEG analysis, and advises international foundations on maternal/child health and early childhood education.
Anis Yazidi is a Professor at Oslo Metropolitan University, affiliated with the Faculty of Technology, Art and Design and the Department of Information Technology. His research focuses on Artificial Intelligence, Machine Learning, Medical Technology, and IoT Security, with a particular emphasis on Applications of AI in Healthcare, EEG Signal Processing, and Digital Transformation. Active research projects include AI Mind (dementia diagnostics), Glycopathology in dry eyes, and Pain and mental distress analysis Completed projects: Digital hate speech analysis, AI in reproductive technology, Nano-antibiotics development His recent publications (2023-2025) demonstrate expertise in: Tsetlin Automaton algorithms for concept learning EEG classification using visibility graphs and vision transformers AI ethics frameworks for medical practice Deepfake detection methodologies Collaborative work spans institutions in Norway, Czech Republic, and international AI research communities.
Ole Petter Ottersen is a Professor at the University of Oslo’s Faculty of Medicine, affiliated with the Institute of Basic Medical Sciences. He previously served as Rector of the University of Oslo (2009–2017) and President of Karolinska Institutet (2017–2023). Ottersen holds dual roles as a Visiting Professor at Charité – Universitätsmedizin Berlin’s Global Health Institute and an affiliated member of Karolinska’s Department of Neuroscience and Department of Global Public Health. He is also Vice President of the Virchow Foundation and chairs the Lancet Commission on Global Governance for Health 2.0. His career spans academic leadership, research coordination, and international health policy advocacy. Education: He earned an M.D. (1980) and Ph.D. in Neuroscience (1982) from the University of Oslo. Ottersen has held academic positions including Research Fellow (1978–1983), Associate Professor (1983–1992), and has coordinated prestigious EU and Nordic grants. His research priorities include academic quality, internationalization, innovation, and dissemination. Research interests focus on molecular neuroscience (e.g., aquaporin-4 channels in brain water dynamics) and global health governance. His work bridges basic science and policy, addressing health inequity, pandemic preparedness, and sustainable healthcare. Recent publications emphasize systemic approaches to global crises, vaccine equity, and interdisciplinary solutions for health challenges. Ottersen has received the Anders Jahre Medical Prize (twice), the Lundbeck Nordic Research Prize, and honorary doctorates from multiple institutions. His awards include France’s Ordre national du Mérite (2019) and Japan’s Order of the Rising Sun (2023). He has led prize committees, including the Kavli Prize in Neuroscience. He has coordinated major national and international grants, such as Norway’s Functional Genomics Program (FUGE) and the EU’s Biomed and NeuroImage projects. His advisory roles include the European Research Council (ERC), WHO-related initiatives, and boards like Oslo University Hospital and NordForsk. Ottersen’s teaching spans medical students since 1976, covering neurobiology, histology, and anatomy. Key initiatives include founding the Guild of European Research-Intensive Universities and leading the Lancet Commission on Global Governance for Health. He collaborates with institutions like EMBL-Norway, Africa CDC, and the Global Virus Network, emphasizing intersectoral action and equitable global health systems.
Jan G. Bjaalie is Professor of Neuroinformatics and Dean of Research and Innovation at the University of Oslo Faculty of Medicine . Since 2023 he heads the faculty’s research and innovation strategy, while directing the Neural Systems and Graphics Computing Laboratory at the Institute of Basic Medical Sciences. Education: 1990 Ph.D. in Neuroanatomy, University of Oslo 1986 M.D., University of Oslo Research interests revolve around collaborative and open neuroscience, digital brain atlasing, and the cyber-infrastructures that enable data sharing. He leads efforts to build next-generation atlases that integrate multi-scale brain architecture and connectivity data, and to develop ontologies and FAIR-compliant platforms for global neuroscience. Recent work emphasizes in silico integration of rodent and human imaging datasets, leveraging machine-learning registration tools and cloud-based services such as EBRAINS. The goal is to transform how brain data are stored, visualised and reused across laboratories worldwide. Scientific output & impact: A scan of publications from 2023-2025 reveals a strong focus on digital atlas frameworks, automated image registration (DeepSlice, DeMBA), open data standards (AtOM ontology), and large-scale analyses of genetic influences on brain structure in Alzheimer’s models. These works collectively advance reproducible, high-throughput neuroanatomy and cross-species translation. Grants & leadership roles: Coordinator/Partner in EU Flagships EBRAINS 2.0, BRAIN Health, Human Brain Project (2013-2026) Infrastructure Director, Human Brain Project (2018-2023) Leader of Neuroinformatics Platform & EBRAINS Data Services (2017-2023) Head of Institute of Basic Medical Sciences (2009-2016) Executive Director, International Neuroinformatics Coordinating Facility (INCF) (2006-2008) Chair, International Brain Initiative (2021-) Editorial & governance service: Founding Chief Editor Frontiers in Neuroinformatics (2007-), Section Editor Brain Structure and Function (2002-2020), member of the INCF Governing Board and EBRAINS AISBL Management Board, and numerous international advisory panels on data governance and ethics. His laboratory hosts the Norwegian Neuroinformatics Node and collaborates closely with global consortia to deliver open-access atlases, software pipelines and FAIR data standards that underpin modern neuroscience.
Astrid Gramstad is an Associate Professor at the Department of Health and Care Sciences, UiT The Arctic University of Norway, where she also serves as Vice Dean of Education. Her research focuses on occupational therapy in primary health care , rehabilitation practices , and learning environments in higher education . She supervises PhD students Morten Nikolaisen (main), Anniken Bogstrand, and Lina Forslund (co-supervision). University: UiT The Arctic University of Norway Department: Department of Health and Care Sciences Email: astrid.gramstad@uit.no Her recent publications examine topics such as study approaches in occupational therapy education , person-centered rehabilitation , and barriers in post-stroke care . She is affiliated with the Centre for Care Research North and leads the RehabLos project, focusing on co-innovation in rehabilitation service design. Key research areas include: Occupational therapy in primary health care Learning environment dynamics in health professions Rehabilitation models for acquired brain injury
