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.
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.
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.
Dr. Ryan B Graham is an Associate Professor in the School of Human Kinetics at the University of Ottawa , with cross-appointments to the Ottawa-Carleton Institute for Biomedical Engineering . He holds Adjunct Assistant Professor positions at Queen's University and University of Waterloo , reflecting his interdisciplinary collaborations in biomechanics and biomedical engineering. His research focuses on spine movement analysis , motion capture validation , and sensor technology applications in clinical and military contexts. Recent work includes developing markerless motion capture systems, assessing movement reliability, and modeling spine dynamics for injury prevention. Editorial Contributions: Associate Editor for Biomechanics and Control of Human Movement at Frontiers in Sports and Active Living Dr. Graham's academic network includes collaborations with institutions in Canada, the UK, and the Netherlands, with a focus on advancing biomechanical methodologies and their clinical translation.
Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.
Jon Andoni Duñabeitia is a Full Professor at the School of Languages and Education of Universidad Nebrija in Madrid. He serves as Director of the Centro de Investigación Nebrija en Cognición (CINC) and the International Chair in Cognitive Health . With an h-index of 43 (Scopus), he has published 170+ articles across psycholinguistics, multilingualism, cognitive training, and virtual reality applications in education. His research examines how language processing interacts with cognitive load, emotional modulation, and technological innovation. Principal Investigator for 8+ projects funded by Spanish Government, Basque Government, BBVA Foundation Associate Editor and Editorial Board Member of high-impact journals Recognized among Spain's top 3% scientists across all disciplines Recent publications span topics including: Second-language reading dynamics in VR environments Multilingual cognitive interactions in neurological conditions Emoji/typographic effects on word processing Computerized cognitive assessment and training systems He actively contributes to scientific meetings as invited speaker across Europe, Asia, and Americas. His work bridges basic research in psycholinguistics with applied technologies for cognitive health.
Andres Soler is a Lecturer at NTNU's Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering. His research focuses on EEG signal processing for applications in brain-computer interfaces (BCI), stress/health monitoring, and low-density electrode systems. He has published extensively on topics including EEG source imaging, artifact removal, and optimized channel selection techniques. His work bridges biomedical engineering and machine learning, with notable contributions to driver alcohol detection systems and motor imagery classification for neurorehabilitation. Teaching roles include serving as Guest Lecturer for Biomedical Instrumentation and Control (TTK4270) and Adaptive Data Analysis (TTK7), while acting as main lecturer for Industrial Electrotechnics (TTK4240). His research group collaborates internationally on projects like FlexEEG and has presented at conferences such as IEEE EMBC and Brain Informatics. Key research directions include advancing EEG-based systems for clinical and automotive applications, developing algorithms for real-time brain activity decoding, and optimizing EEG hardware configurations for cost-effective implementations. Current trends show focus on enhancing signal quality through artifact mitigation strategies and improving BCI communication systems for locked-in patients.
Eric Mörth is a PhD Scientist in Multimodal Medical Visualization at the University of Bergen's Department of Informatics, collaborating with the Mohm Medical Imaging and Visualization Center (MMIV). His research focuses on innovative medical data visualization techniques, such as MuSIC and ICEVis, which enhance clinical decision-making. Currently on a research stay at Harvard University's VCG Group, he holds a Master's in Medical Informatics from the Medical University of Vienna and a degree in Biomedical Engineering from the Technical University of Vienna. His awards include the Best Paper Honorable Mention (VCBM2022) and Best Short Paper (VINCI2022). Mörth's work spans cancer imaging, scrollytelling narratives, and interactive visualization tools, supported by grants from Trond Mohn Stiftelse. He advises through Team Smit and has contributed to projects like RadEx and ParaGlyder, advancing medical data exploration and communication.
Frode Eika Sandnes is a Professor at the Department of Information Technology , Faculty of Technology, Art and Design , Oslo Metropolitan University , focusing on Human-Computer Interaction and Universal Design of ICT . His research spans innovative interaction techniques, skill reuse, pattern recognition, image analysis, and intelligent systems. Current research trends include accessibility in digital education, lightweight deep learning models, and physical interface usability Recent publications analyze color picker efficiency, 3D-printed prosthetics, and authentication technologies His work integrates interdisciplinary approaches to enhance usability and inclusivity in technology. Notable projects involve automated readability assessments and AI applications in diverse domains like aquaculture and medical diagnostics.
Yücel Karabiyik is a Chief Engineer in the Digital Signal Processing and Image Analysis (DSB) Research Group at the University of Oslo. He holds a PhD in Medical Technology (2017, NTNU) and has postdoctoral expertise in ultrasound elastography. His responsibilities include managing ultrasound research projects, maintaining scanners, coordinating lab resources, and supporting machine learning initiatives. Education: PhD in Medical Technology (NTNU, 2017) Research interests focus on ultrasound blood flow imaging, adaptive spectral methods, and elastography. His work bridges electrical and biomedical engineering, advancing medical imaging and ultrasound technology applications. Key contributions include advancements in shear wave speed estimation, Doppler spectral analysis, and 3D echocardiography visualization for mixed reality applications. Collaborative projects emphasize clinical translation of imaging techniques. Publications span IEEE journals and conferences, addressing topics like autocorrelation-based elastography and adaptive Doppler techniques. Technical leadership includes equipment coordination and algorithm development for medical imaging systems.
Pedro Lind is a Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, where he serves in the Department of Information Technology with a focus on Artificial Intelligence. His academic appointments include active participation in research groups for Applied Artificial Intelligence and Mathematical Modeling. Dr. Lind's research spans interdisciplinary domains including: Biomedical AI applications (EEG classification, ECG analysis, eye tracking) Stochastic processes and complex systems modeling Trustworthy machine learning for security/privacy Physics-inspired computational methods Renewable energy statistics and modeling His recent publications demonstrate strong focus on developing novel AI methodologies for medical diagnostics (2024-2025), particularly using generative models and interpretable AI approaches for physiological data analysis. He leads significant research initiatives including the AI-Mind project developing diagnostic tools for dementia. Additional projects include international technology transfer collaborations with Czech Republic institutions. Dr. Lind maintains active research teams and labs focused on computational neuroscience and applied AI.
Dilip K. Prasad is a Professor at the Department of Informatics, UiT The Arctic University of Norway. His work bridges Artificial Intelligence and Medical Imaging , with a focus on Interpretable AI , Scalable AI , and Life Science Applications . He has contributed to Maritime Technology and Biomedical Engineering . Ph.D. and B.Tech from Nanyang Technological University and IIT Dhanbad Senior Research Fellow at NTU (2015-2019), Research Fellow at NUS (2012-2015) Industry experience at IBM, Infosys, Mediatek, Philips His research explores Image Processing , Machine Learning , and AI Applications in Biomedicine . Recent work includes Dense Video Captioning , 3D Mitochondrial Modeling , and Physics-Guided Loss Functions . Articles span Neurocomputing , Optics Express , and top AI conferences like CVPR and NeurIPS . Prasad has received the Rolls-Royce Inventor Award (2016) and Best Paper Award (IJCIE 2017) . He has reviewed for 50+ journals and 30+ conferences, serving as Area Chair for NeurIPS 2022-23 and Organizer Chair for ICCV Workshop 2023 .
Hugo Lewi Hammer er professor ved Oslo Metropolitan University, tilhørende Faculty of Technology, Art and Design og Department of Information Technology – Mathematical Modeling . Hans forskning fokuserer på forbedring av pålitelighet og transparens i maskinlæring, forsterkende læring og dyb læringsmodeller gjennom metodikk innen modelltolkning, usikkerhetskvantifisering, robust statistikk og kausal inferens. Hans nylige arbeid inkluderer: AI-drevet optimering i assistert reproduksjonsteknologi (embryoutvalg og sædcelleanalyse) Medisinsk bildebehandling (polypdeteksjon, meibomkertutgang) Neural nettverkstolkning og usikkerhetsmodellering i EEG-analyse Biomekanisk prediksjon av muskelutmatting Hans publikasjoner viser mangfoldige anvendelser av AI i medisin og teknologi, med spesialvekt på: Explainable AI (XAI) i diagnostikk og behandling Usikkerhetskvantifisering i dyb læring Automatisering av medisinske prosedyrer (ICSI, embryoanalyse) Stokastisk simulering og kausal inferens Hammer er engasjert i forskningsgruppene Applied Artificial Intelligence og Mathematical Modeling og har publisert over 130 vitenskapelige artikler og 7 forskningsrapporter.
Ruth Jane Prince is Professor in Medical Anthropology at the Department of Community Medicine and Global Health, Institute of Health and Society, University of Oslo. She holds a PhD from the University of Oxford and has trained at UCL and the University of Copenhagen. Her research is centered on global health, care practices, citizenship, and state formation in East Africa, particularly Kenya. Her academic interests include medical anthropology, global health, care and chronic disease, citizenship and the state, health systems, epidemics, anthropology of health insurance, environmental anthropology, chemical ethnography, anthropology of toxicity, and postcolonial studies. She has conducted extensive ethnographic and historical research on universal health coverage, health insurance, and bureaucratic labor in Kenya, often in collaboration with African institutions such as the University of Nairobi, Maseno University, and KEMRI. Prince’s recent publications reveal a strong focus on universal health coverage, the moral economies of care, bureaucratic labor in health systems, and the legacies of Cold War medical aid in Kenya. Her work combines critical theory with deep ethnographic engagement, exploring how global health policies are interpreted and transformed in local contexts. She frequently examines the tensions between aspiration and failure, solidarity and inequality, and public good and privatization in African health systems. Co-winner, Royal Anthropological Institute’s Amaury Talbot Prize (2010) for The Land is Dying: Contingency, Creativity and Conflict in Western Kenya Winner, RAI Documentary Film Student Prize (2003) for Adhiambo - Born in the Evening She has received major research grants including an ERC Starting Grant (2018–2023) and Norwegian Research Council grants (FRIPRO/Global Health 2021–2025, FRISAM 2013–2017). She has supervised numerous PhD students to completion and continues to mentor current candidates. Her work is deeply collaborative, involving partnerships in Kenya, Tanzania, and Uganda. She teaches courses in medical anthropology, global epidemics, and global health, and contributes actively to public scholarship through platforms like Somatosphere and Africa Is a Country.
Michael Alexander Riegler is a full-time Professor at Oslo Metropolitan University's Faculty of Social Sciences, specifically in the Department of Social Work, Child Welfare and Social Policy. While his formal academic affiliation focuses on social sciences, his research interests span interdisciplinary domains including computer technology, information and communication systems, medical technology, and mathematics/natural sciences. Current research projects: Strengthening solidarity for democratic unity across border (SOLIDEM) addressing trust erosion in European welfare states, and Artificial intelligence in assisted reproduction technology improving embryo/sperm selection Recent publications (2025) focus on AI applications in healthcare (wearable sensors, ECG reconstruction), anomaly detection in time-series data, multimodal healthcare data analysis, and psychiatric motor activity datasets
