جست‌وجوی استاد و پژوهشگر

• Deep Learning
• Medical Multimedia
• Explainable AI
• +۵ مورد دیگر

Steven Hicks is a Senior Research Scientist in the Holistic Systems department at Simula Metropolitan. His work focuses on applying artificial intelligence techniques to medical and multimedia applications, with particular emphasis on explainability and transparency in AI systems. Dr. Hicks' research spans multiple domains including medical imaging, particularly in gastrointestinal endoscopy and reproductive medicine. His primary interests include Deep Learning , Medical Multimedia , Explainable AI , and Computer Vision . His work has significant applications in polyp detection, sperm analysis, ECG interpretation, and medical image segmentation, contributing to advancements in diagnostic accuracy and clinical decision support systems. Analysis of Dr. Hicks' recent publications reveals a strong focus on medical applications of AI with emphasis on transparency and explainability. His work frequently addresses challenges in gastrointestinal endoscopy, reproductive medicine, and cardiology. A notable trend is his involvement in creating benchmark datasets like Kvasir-VQA and VISEM-Tracking, and organizing challenges through MediaEval and ImageCLEF to advance the field of medical AI and establish standardized evaluation frameworks. Dr. Hicks actively collaborates with medical professionals and researchers across multiple institutions, contributing to the development of practical AI solutions for real-world medical problems. His work bridges the gap between theoretical AI research and clinical applications, with significant contributions to both technical innovations and the creation of essential resources for the medical AI community.

Erlend Hodnelandمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Radiomics
• Tumor Segmentation
• +۳ مورد دیگر

Erlend Hodneland is a Researcher at the University of Bergen's Department of Informatics, affiliated with the Mohn Medical Imaging and Visualization Centre (MMIV). His work bridges medical imaging, radiomics, and machine learning, focusing on tumor analysis and prognostication in cancers like cervical and endometrial cancers. He specializes in developing automated tumor segmentation techniques and tools for radiomic signature analysis, integrating visualization and computational methods to enhance clinical decision-making. His research interests include medical imaging analytics, radiomics for cancer prognosis, and interactive visualization frameworks for multiparametric medical data. Collaborations involve radiologists, oncologists, and computer scientists to translate imaging data into actionable insights. Key projects include the RadEx platform for radiomic tumor profiling and automated segmentation algorithms for cervical cancer studies. Notable contributions include MRI-based delta radiomics for tracking tumor responses during chemoradiotherapy and fully automatic whole-volume tumor segmentation. His work emphasizes precision medicine through quantitative imaging biomarkers and interdisciplinary approaches to medical data analysis.

• Recommender Systems
• Privacy Issues in AI
• Fake News Detection
• +۳ مورد دیگر

Özlem Özgöbek is an associate professor at the Department of Computer Science at NTNU, focusing on recommender systems, privacy in AI, and fake news detection. She holds a part-time role as a program manager at the Norwegian Research Center for AI Innovation (NorwAI) since November 2023. She coordinates the International Work-Integrated-Learning in Artificial Intelligence (IWIL AI) project and actively participates in organizing committees such as the INRA Workshop Series and the Norwegian Big Data Symposium (NOBIDS). She collaborates with the Center for Excellent IT Education (Excited) center, contributing to advancements in semantic web technologies and machine learning applications in education. Her research bridges academic and industrial challenges, emphasizing ethical AI deployment and user-centric systems. Key contributions include multimodal fake news detection frameworks, educational technology innovations, and sustainable wardrobe recommendation systems leveraging Linked Open Data. She has organized multiple international workshops (e.g., INRA 2021–2023) and contributed to benchmarking initiatives like MediaEval’s news image analysis tasks. Her academic work spans over 30 peer-reviewed publications since 2014, including studies on classroom interaction tools, privacy perceptions in recommender systems, and the Adressa news dataset for evaluation purposes. She maintains strong ties with collaborative projects such as the IWIL AI initiative, fostering international education-industry partnerships in AI.

• Machine Learning
• 3D Imaging
• Image and Video Processing
• +۷ مورد دیگر

Faouzi Alaya Cheikh is a Professor of Computer Science at NTNU, affiliated with the Faculty of Information Technology and Electrical Engineering. He holds a BSc in Electronics (ENIT, Tunisia, 1992), MSc in Signal Processing (TUT, 1997), and a Dr. Tech. in Signal Processing (TUT, 2004). His career includes roles as an Associate Professor at Gjøvik University College (2006–2015), researcher at TUT (1994–2006), and Electronics Engineer at Société Tunisienne de l'Éléctricité et du Gaz (1992–1993). He leads the Intelligent Systems and Analytics (ISA) research group at NTNU. His research focuses on machine learning, 3D imaging, video surveillance, biometrics, and healthcare applications. Notable projects include Alameda , HiPerNav , and INID . Key areas of innovation include liver surgery visualization, polyp segmentation, facial expression recognition, and IoT-driven healthcare solutions. His work bridges computer vision, medical imaging, and biomedical signal processing. Publications span 2018–2025, emphasizing biomedical applications, autonomous systems, and deep learning frameworks. Recent contributions include real-time liver resection planning, polyp segmentation networks, and emotion recognition for neurological care. Collaborations span industries and institutions globally, addressing challenges in healthcare technology, surgical navigation, and smart cities.

• Machine Learning
• Recommender Systems
• Natural Language Processing
• +۳ مورد دیگر

Lemei Zhang is a Postdoctoral Fellow at the Norwegian University of Science and Technology (NTNU) in the Department of Computer Technology and Informatics. Their work focuses on advanced machine learning techniques for recommendation systems and social media analysis. Research interests include Deep learning architectures for news recommendation Aspect-based sentiment analysis in multilingual contexts Temporal and graph embedding methods for social recommendation Contextual augmentation in session-based systems Time series modeling for user interest prediction Creation of large-scale datasets like Adressa for media analytics Recent publications demonstrate a trajectory from foundational work on news recommendation datasets (2016-2017) to increasingly sophisticated neural approaches incorporating attention mechanisms, multimodal fusion, and real-time graph analysis across ACM Transactions, Machine Learning, and Human-Computer Studies venues.

Kari Beate Remmenمشاهده پروفایل

دانشیار

• Science Education
• Geography Education
• Outdoor Education
• +۶ مورد دیگر

Kari Beate Remmen is an Associate Professor in the Department of Teacher Education and School Research at the University of Oslo, where she has been employed since 2016. Her academic work focuses on science and geography education with particular emphasis on outdoor learning environments, field-based teaching approaches, and the integration of sustainability concepts into educational practices. Dr. Remmen's research interests include exploratory field teaching in geosciences, outdoor education in science education and teacher training, collaboration between school teachers and external science professionals, video observation of teaching and learning in earth sciences, and using representations in science teaching. She has developed frameworks for effective fieldwork design that support student learning processes throughout preparation, fieldwork, and follow-up activities. Her extensive publication record from 2013-2024 reveals consistent focus on practical applications of educational theory. Remmen's work frequently addresses how fieldwork and outdoor learning can enhance deep learning experiences, with particular attention to the transition between classroom instruction and outdoor activities. Her research shows strong commitment to understanding how students and teachers engage with scientific concepts in real-world settings. As an educator, Remmen teaches courses in geography didactics for practical pedagogical education programs, geoscience didactics for the Geosciences program, and research methods courses. She has served as supervisor for numerous master's students and is currently guiding several PhD candidates from multiple institutions. Dr. Remmen actively collaborates with institutions including the Climate House (NHM, UiO), OsloMet, University of Tromsø, School Laboratories in Geosciences at UiO and UiB, Science Centre (UiO), and University of Sherbrooke (Canada). She currently leads the National Network for Sustainability and Education (NABU) and serves on the board of the Norwegian Geographical Society. Her research has been conducted through projects such as 'Representation and Participation in Science (REDE)' and as part of the 'Challenges of Sustainability in Educational Research (COSER)' research group, reflecting her commitment to sustainable educational practices and meaningful science learning experiences.

• Empirical Software Engineering
• Software Quality
• Requirement Engineering
• +۳ مورد دیگر

Deepti Mishra is a Professor at the Department of Computer Technology and Informatics at NTNU, affiliated with the Faculty of Information Technology and Electrical Engineering. She leads the Intelligent Systems and Analytics Research Group and the Educational Technology Laboratory. Previously, she held positions as Associate Professor at Monash University (Malaysia) and Assistant Professor at Atilim University (Turkey). Her research focuses on Empirical Software Engineering, Human-Robot Interaction, and Sustainability. Current projects include collaborative research-based education for wind farms and marine plastic pollution solutions, funded by The Research Council of Norway and DIKU. Her research interests span Human-Computer Interaction, Software Quality, and Requirement Engineering. Recent work emphasizes social robotics applications in healthcare, stress mitigation, and educational environments. She actively contributes to the Excited Centre for Excellent IT Education and the NTNU Active Aging Team. Over 50 peer-reviewed publications demonstrate her expertise in AI-driven robotics, edge computing, and software ecosystem decision-making. Publications highlight innovations in gesture-controlled robotics frameworks, multi-pose action recognition systems, and ROS-based human-like robot actions. Key methodologies include systematic reviews, multi-criteria decision analysis, and deep learning architectures. Ongoing projects aim to bridge technology and societal challenges through interdisciplinary collaboration.

• Digital Health
• Wearable Sensors
• Artificial Intelligence
• +۶ مورد دیگر

Olga Korostynska is a Professor at Oslo Metropolitan University within the Faculty of Technology, Art and Design, Department of Mechanical, Electrical and Chemical Engineering. Her research spans digital health, wearable sensors, and robotics, with a focus on AI-integrated medical devices and environmental monitoring systems. Digital Health Technology Implementation Wearable Sensor Development Biomedical and Environmental Applications Recent publications highlight her work on microwave sensors for blood lactate monitoring, robotic meat processing platforms, and gamified rehabilitation devices. Her projects often bridge interdisciplinary domains, including EQUALS-EU initiatives for women in technology and AI-driven healthcare solutions. Korostynska contributes to sensor technology conferences and collaborates with teams like ADEPT (Advanced Health Intelligence and Brain-Inspired Technologies) to advance non-invasive diagnostics and rehabilitation tools.

• Missing Data Imputation
• Machine Learning
• Healthcare Informatics
• +۳ مورد دیگر

Thu Nguyen is a Research Fellow in the Department of Holistic Systems at Simula Metropolitan, a leading Norwegian research institute specializing in data science and computing. Her work focuses on developing robust methodologies for handling missing data across critical domains including healthcare diagnostics and environmental monitoring systems. Her primary research interests include: Advanced missing data imputation techniques Machine learning explainability under incomplete data Healthcare informatics and medical data analysis Environmental data processing for air quality prediction Multimodal data integration challenges Analysis of her publication record reveals a strong trajectory in developing computationally efficient imputation algorithms with real-world applications. Key trends include the application of principal component analysis and generative models to healthcare datasets (sperm tracking, medical imaging), environmental monitoring (air quality prediction), and multimodal systems. Her work consistently bridges theoretical statistical foundations with practical implementation needs, emphasizing model transparency and performance validation. Dr. Nguyen actively collaborates within Simula Metropolitan's data science ecosystem, contributing to interdisciplinary projects that address fundamental challenges in data completeness and usability across scientific domains.

Frode Guribyeمشاهده پروفایل

استاد

• Human-Computer Interaction (HCI)
• AI in Healthcare
• Tangible Interaction
• +۴ مورد دیگر

Frode Guribye is a Professor at the Department of Information Science and Media Studies, University of Bergen. His research focuses on human-computer interaction (HCI), emerging technologies like AI, VR, and tangible interfaces, with applications in healthcare, mental health, and media technologies. He is affiliated with research groups in HCI and Journalism Studies. Key research interests include the design of interventions for ADHD and emotion dysregulation, nurse-AI collaboration in emergency care, VR training for industrial safety, and tangible tools for pain assessment. His work emphasizes critical and constructive evaluations of technology's potential and limitations. Recent studies highlight blended digital interventions for mental health, AI's role in psychological treatment, and collaborative tools in video editing and media production. His projects often involve interdisciplinary collaborations with healthcare institutions and industry partners. Supervised doctoral research includes Pavel Okopnyi's work on collaborative video editing. He has presented at leading conferences such as DIS, CHI, and ISCRAM, and his work is supported by grants like the Norwegian Research Council's 2022-2026 project on ADHD interventions. Frode's contributions span academic publications, workshops on AI design, and practical applications in healthcare and industry training.

• Recommender Systems
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Lemei Zhang is a Research Fellow at the Department of Computer Science, Norwegian University of Science and Technology (NTNU). Her work focuses on developing advanced recommendation systems, particularly in the context of news and social media analysis. She actively contributes to both academic journals and conferences, with a strong emphasis on machine learning and neural network-based solutions. Her research interests include but are not limited to recommender systems, session-based recommendations, attention mechanisms, and multimodal data fusion. She has also contributed to the development of benchmark datasets like the Adressa dataset for news recommendation. Recent publications highlight her exploration of multilingual systems, explainable AI, and real-time social recommendation algorithms. Her work bridges theoretical advancements with practical applications, such as improving content curation in traditional media through AI integration. In teaching, she instructs the course TDT4215 - Recommender Systems, reflecting her expertise in this domain. She has presented at major conferences including ACM Hypertext, ECML-PKDD, and UMAP, showcasing her commitment to advancing interdisciplinary research.

• Deep Learning
• Speech Analysis
• Image and Video Analysis
• +۶ مورد دیگر

Ali Shariq Imran is an Associate Professor at the Department of Computer Science, Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. He holds a Ph.D. from the University of Oslo and a Master's from NUST, Pakistan. His research focuses on deep learning applications in speech analysis, image/video processing, semantic web technologies, and eLearning. He has published extensively in journals like IEEE Access, Engineering Applications of Artificial Intelligence, and Frontiers in Computational Neuroscience, with notable work on hate speech detection, biomedical signal analysis, and sentiment analysis in social media contexts. His awards include the HEC and UIUC scholarships and a 2015 Best Paper Award at HCI Intl. He serves on the board of the HCI International Conference and is an IEEE member. Teaching includes courses on coding/compression, software engineering, and web development. His competencies span AI, digital signal processing, and multimedia systems. Research interests include contextual understanding, OCR, and object identification in multimedia, alongside eLearning innovations like MOOCs and LMS integration. He has co-edited special issues on healthcare systems and software standards in journals like JMIHI and IEEE Access. His work frequently employs explainable AI (XAI) to enhance transparency in medical diagnostics and social media analysis.

• Human-Computer Interaction
• Artificial Intelligence
• Applied Machine Learning
• +۱۲ مورد دیگر

Kshitij Kshitij is an Associate Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), with additional affiliation to the Department of Language and Literature. His research bridges Human-Computer Interaction, Artificial Intelligence, and Educational Technology, focusing on multimodal data to understand learning and collaboration. His research interests include Applied Machine Learning, Multimodal Learning Analytics, Human-Computer Interaction, and Educational Technology. He investigates how physiological and behavioral data—such as eye-tracking, EEG, facial expressions, and system logs—can reveal insights into user expertise, motivation, and performance. His work applies empirical and mixed-methods approaches to contexts like MOOCs, children’s coding activities, e-commerce, and business process modeling. He has pioneered the use of Extreme Value Theory for analyzing collaborative behavior in time-series data. His recent publications demonstrate a strong trend toward integrating AI and multimodal sensing in educational settings, particularly in understanding children’s collaborative and cognitive states, designing intelligent feedback systems, and enhancing teacher dashboards. His work increasingly explores generative AI, embodied learning, and neuro-adaptive environments. Eye-tracking and AI for motivation and learning (Smart Learning Environments, 2020) Wearable sensing for learning experience (IJHCS, 2020) Extreme Value Theory for collaboration prediction (Journal of Learning Analytics, 2017) Multimodal effort profiling in children (JCAL, 2025) Neuro-adaptive business process modeling (CEUR, 2024) Kshitij actively mentors students and early-career researchers, including Serena Lee-Cultura, Sofia Papavlasopoulou, and Jennifer Olsen. His collaborative work often involves interdisciplinary grants related to learning analytics, educational technology, and human-centered AI. He contributes to major initiatives such as the Multimodal Learning Analytics Handbook (Springer, 2022). He is a core member of research teams focused on multimodal data in education, working closely with the MULTIS group at NTNU and international collaborators. His lab integrates wearable sensors, interactive surfaces, and AI to build intelligent, adaptive learning environments.

Øystein Giljeمشاهده پروفایل

استاد

• Digitalization and Learning
• Multimodal Learning
• Educational Technology
• +۴ مورد دیگر

Øystein Gilje is a Professor at the Department of Teacher Education and School Research (University of Oslo). His work bridges digital learning, multimodal analysis, and educational technology. He leads projects like MulVu (multimodal assessment) and GEPP (one-to-one classroom practices). Current: Agile EDU, LAT (Learning in Algorithmic Age) Past: Ark&App (teaching materials), Knowledge in Motion (2012–2015), Learning Lives (2010–2013) Research focuses on multimodal texts , digital pedagogy , and ICT integration . Funded by NFR, Norwegian Directorate of Education, and applied media research councils. His publications analyze semiotic technologies, educational chronotopes, and youth media practices. Teaching history includes PhD courses at Southern Danish University (2010) and master’s seminars (2006–2008). He served on the Complaints Board for Film and Videograms (2010–2012) and as a union representative (2007–2008). Background: General teacher (1989–2000), journalist (2002–2005), PhD in media education (2004–2009).

• Child-Computer Interaction
• Computational Thinking
• Design-Based Research
• +۶ مورد دیگر

Sofia Papavlasopoulou is an Associate Professor at the Department of Computer Science, Norwegian University of Science and Technology (NTNU), since 2021. Her research focuses on designing and evaluating educational technologies to enhance learning experiences in Computing, STEM, Maker Education, and Coding across kindergarten to undergraduate levels. She leads interdisciplinary projects funded by Horizon 2020, Horizon Europe, and the Research Council of Norway. Current Projects: Surrounded by Science (Co-PI, Horizon 2020, 2021–2024) Exten.DT.² (PI, Horizon Europe, 2022–2025) DiCoTe (PI, Research Council of Norway, 2021–2025) Her research spans Child-Computer Interaction, Computational Thinking, Emerging Technologies, and Gamification. She employs Design-Based Research and Multimodal Learning Analytics to study children's engagement and learning outcomes. Her work involves collaborations with industry partners and public sector organizations through master-level courses. Scientific awards include: 2024 Best student paper award at ENASE 2023 Best educational paper at AIS 2022 Best Paper at IDC 2017 Best Paper at IFIP I3E’17