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Eli Renate GrünerView profile

Professor

• Medical Physics
• Neuroimaging
• Biomedical Engineering
• +1 more

Eli Renate Grüner is a Professor at the University of Bergen's Department of Physics and Technology, with joint affiliations at Haukeland University Hospital. Her research focuses on advancing medical imaging techniques, particularly MRI-based methods for studying brain function, perfusion dynamics, and neurodegenerative disorders. She collaborates extensively with clinical researchers to translate imaging innovations into diagnostic applications. Grüner's interdisciplinary work bridges physics, neuroscience, and clinical medicine, developing analytical tools to quantify cerebral blood flow, neurotransmitter systems, and metabolic processes. Her neuroimaging research examines brain network dynamics, alcohol's effects on cognition, and pathological mechanisms in conditions like dementia and Tourette syndrome. Her publications demonstrate expertise in perfusion imaging algorithms, fMRI analysis, and spectroscopic methods. Recent work includes developing visualization tools for spectroscopy data and investigating network switching during cognitive processing.

Vladimir MironovView profile

Researcher

• Semantic Web Technologies
• Systems Biology
• Bioinformatics
• +3 more

Vladimir Mironov is a Senior Scientist at the Norwegian University of Science and Technology (NTNU), specializing in Semantic Web technologies and their application in systems biology and bioinformatics. His work focuses on knowledge graph integration, biological data modeling, and semantic network construction. Email: vladimir.mironov@ntnu.no Email: vladimir.n.moronov@gmail.com His research interests include: Development of Semantic Web tools for gene regulatory network analysis Ontology engineering for biological knowledge management Integration of chromosome location data with functional genomics Triple store evaluation for biological datasets Recent publications highlight his contributions to: Knowledge graph frameworks for gene regulation Network analysis tools in bioinformatics Ammonia-tolerant microbial communities in biotechnology Orthology prediction algorithms Semantic data modeling for biological datasets He has actively contributed to projects like DrugLogics and Crossover Research since 2008, developing semantic tools for biomedical research, including the Cytoscape BioGateway App and Cell Cycle Ontology (CCO).

Noeska SmitView profile

Professor

• Medical Visualization
• Computational Medicine
• Multimodal Imaging
• +3 more

Noeska Smit is a Professor in Medical Visualization at the Department of Informatics, University of Bergen , where she has held a tenure-track position funded by the Trond Mohn Foundation since 2017. She is also a senior researcher and member of the leadership team at the Mohn Medical Imaging and Visualization (MMIV) Centre . Her research focuses on novel interactive visualization techniques for exploring and communicating multimodal medical imaging data , particularly in multi-parametric MR acquisitions . She leads projects in gynecologic cancer imaging , Multiple Sclerosis neuroimaging , and human anatomy education through collaborations with institutions like UGent (Belgium) and HVL. 2019: Dirk Bartz Prize for Visual Computing in Medicine 2016: PhD at Delft University of Technology , Netherlands 2012: MSc in Computer Science (Computer Graphics & Visualization) , Delft University Recent publications highlight her work in MRI radiomics , narrative visualization , open-source anatomy platforms , and interactive clustering tools for tumor analysis. Her methods are applied in oncological pelvic surgery planning , neurological disease monitoring , and 3D learning environments . She supervises PhD candidates Eric Mörth and Sherin Sugathan , and has contributed to open-source medical visualization tools like RegistrationShop and Online Anatomical Human (OAH) . Her work bridges clinical practice and computer science through collaborations with radiologists, surgeons, and ML researchers.

Thomas Peter PlagemannView profile

Professor

• Data Stream Processing
• Sensors and AI for Health
• Internet of Things
• +3 more

Thomas Peter Plagemann is a Professor in the Department of Informatics at the University of Oslo , specializing in Data and Knowledge Systems . His research focuses on data stream processing , sensors and AI for health , Internet of Things , and mobile network applications . Current projects: Parrot (Privacy Engineering for IoT), Respire (Explainable ML for Sleep Disorders), and TRAMP (Real-Time Application Mobility) Editorial leadership: ACM Transactions on Multimedia Computing and past roles at Computer Communications and Multimedia Systems Recent publications analyze operator migration in stream processing , explainable AI for pediatric sleep apnea , and privacy-preserving healthcare systems . His work combines distributed computing , sensor networks , and machine learning applications in medical and mobile environments.

Martin Georg SkjævelandView profile

Researcher

• Semantic Web
• Ontology Engineering
• Data Integration
• +3 more

Martin Georg Skjæveland is a **Researcher (permanent position)** at the **Department of Informatics, University of Oslo**, and an **Adjunct Associate Professor in Data Science** at the **University of Stavanger**. His work focuses on semantic web technologies, ontology engineering, and data integration. He holds a PhD from the University of Oslo (2015) and has extensive postdoctoral and research experience in informatics. **Education**: PhD in Informatics, University of Oslo (2015) Master in Informatics, University of Oslo (2006) Bachelor in Natural Sciences, University of Bergen (2004) **Research Interests**: Skjæveland specializes in ontology engineering tools like **Lutra (OTTR)** and **Sgvizler**, which support ontology template instantiation and semantic data visualization. His work addresses challenges in data integration, semantic web applications, and industrial data management. Recent projects include developing frameworks for personal knowledge graphs (PKG) and ontology-based data access (OBDA). **Publications**: Over 30 peer-reviewed articles, with recent focus on ontology patterns (**OTTR**), personal knowledge graphs, and industrial data integration. His contributions span journals like *AI Open*, *Semantic Web Journal*, and conferences like ISWC and WWW. **Labs/Projects**: Leads the **Optique** project (ontology-based data access), co-develops **OTTR** templates, and contributes to tools like **Sgvizler** for SPARQL visualization.

Frank MelandsøView profile

Professor

• Ultrasound
• Microwaves
• Optics
• +5 more

Frank Melandsø is a Professor at the Department of Physics and Technology, UiT The Arctic University of Norway. His research focuses on ultrasound, microwaves, and optics, particularly in nondestructive testing and acoustic imaging technologies. Key research interests include: Development of advanced ultrasound and acoustic microscopy techniques Finite element modeling for wave propagation and transducer design Image processing algorithms for noise reduction and defect visualization Applications in materials science, marine biology, and biomedical imaging Recent publications highlight trends in deep learning applications, tilt compensation methods, and 3D imaging of complex materials. His collaborative work spans institutions and disciplines, with extensive contributions to sensors, imaging systems, and computational analysis. Professor Melandsø is actively involved in the Ultrasound, Microwaves and Optics research group and the VirtualStain project, based at Teknologibygget Tromsø 2.053.

Aditya GuptaView profile

Research Fellow

• Machine Learning
• Deep Neural Networks
• Image and Signal Processing
• +5 more

Aditya Gupta is a Postdoctoral Research Fellow at the University of Agder's Department of Information and Communication Technology. His research focuses on Machine Learning, Deep Neural Networks, Smart Water Management, and IoT applications. He has held adjunct faculty positions at the College of Engineering Pune (2019-2021) and temporary faculty roles at National Institute of Technology Raipur (2018-2019). His work integrates AI with domains such as water resource optimization, video surveillance systems, and healthcare monitoring. Recent projects include AI-driven solutions for road quality analysis, fall detection, and aquatic health management. Key areas: Smart Water Systems, Video Compression, Autonomous Driving, Healthcare AI Publications span journals like Energies , IEEE Access , and Applied Sciences , with a focus on applying deep learning to real-world challenges such as water distribution optimization and fish health diagnostics. Collaborations include projects on IoT security and biomedical signal processing.

Stefan BrucknerView profile

Professor

• Data Visualization
• Interactive Visualization Techniques
• Biomedical Data Visualization
• +4 more

Stefan Bruckner is a Professor of Visualization, currently heading the Chair of Visual Analytics at the University of Rostock, Germany, after previously serving as a full Professor at the University of Bergen, Norway. He holds a master's (2004) and PhD (2008) in Computer Science from TU Wien, Austria, and an habilitation (2012). His research focuses on interactive visualization techniques, particularly in biomedical and data-driven contexts, with contributions spanning illustrative visualization, volume rendering, and narrative medical storytelling. He has led industry collaborations with GE Healthcare and Agfa HealthCare, resulting in 7 patents. His academic roles include program co-chair for EuroVis, PacificVis, and the Eurographics Medical Prize, and editorial board membership in IEEE Transactions on Visualization and Computer Graphics and Computers & Graphics. Awards include the Eurographics Young Researcher Award and Karl-Heinz-Höhne Award for Medical Visualization, alongside 11 best paper awards. Research emphasizes narrative visualization for disease communication, user-centric medical data exploration, and innovative visual metaphors like Honeycomb Plots and ScrollyVis. His work bridges academic research with practical applications, fostering interdisciplinary collaborations in healthcare and computational biology.

Ivan ViolaView profile

Professor

• Medical Visualization
• Molecular Visualization
• Data Visualization
• +3 more

Ivan Viola is a Professor at the University of Bergen's Department of Informatics. His research focuses on medical and molecular visualization, with a particular emphasis on biomedical applications such as fetal ultrasound analysis, protein interactions, and biomolecular dynamics. He leads the VisGroup, developing tools like COZOID for protein-protein interaction analysis and The Vitruvian Baby for fetal ultrasound standardization. His work spans interdisciplinary collaborations, integrating computer science with biology and medicine. Key contributions include advancements in visualization techniques for physiological data, molecular dynamics simulations, and real-time data processing. He has published extensively in top journals and conferences, addressing challenges in data exploration, abstraction, and interactive visualization. Viola's research impacts fields such as prenatal diagnostics, drug discovery, and medical imaging, with tools like Watergate for water trajectory analysis and cellVIEW for multi-scale biomolecular rendering. His projects emphasize user-driven design and the development of intuitive visualization solutions for complex scientific data.

Paolo AngelelliView profile

Researcher

• Medical Visualization
• Computer Graphics
• Scientific Visualization
• +3 more

Paolo Angelelli is a Researcher in the Department of Informatics at the University of Bergen, specializing in medical visualization, computer graphics, and scientific visualization. His work focuses on advancing techniques for medical imaging, particularly in ultrasound-based blood flow analysis, data fusion, and interactive visual exploration of complex medical datasets. He holds a PhD in Informatics from the University of Bergen (2012) and has contributed to projects involving real-time ultrasound processing, cohort study data analysis, and visualization of fluid dynamics. Education : PhD in Informatics, University of Bergen (2012) Research Interests : Medical visualization techniques for ultrasound and perfusion analysis Interactive visual analytics for heterogeneous medical data Real-time rendering and illumination optimization Applications in cardiovascular and biomedical imaging Key Contributions : His research bridges computational methods with clinical applications, addressing challenges in visualizing blood flow dynamics, integrating cohort study data, and improving diagnostic accuracy through advanced visualization pipelines.

Andrea BrambillaView profile

Researcher

• Flow Visualization
• Scientific Visualization
• Computer Graphics
• +3 more

Andrea Brambilla is a researcher at the Department of Informatics, University of Bergen, focusing on flow visualization and scientific visualization techniques. Her work emphasizes improving the understanding of complex flow phenomena through innovative visualization methods, including integral surfaces, occlusion management, and comparative visualization strategies. She completed her PhD in 2014, titled 'Visibility-oriented Visualization Design for Flow Illustration.' Her research has been applied to fluid dynamics, molecular visualization, and biomedical data analysis. Key contributions include expressive seeding strategies for stream surfaces, hierarchical splitting schemes for occluded integral surfaces, and fast molecular surface representation techniques. She actively collaborates on projects like the SemSeg initiative, advancing visualization tools for scientific data exploration. Her expertise spans computational fluid dynamics, volume rendering, and interactive visualization methods. Her presentations and lectures, such as 'Video Visualization: An Overview,' highlight her commitment to bridging visualization theory and practical applications. She has contributed to conferences and workshops, including PacificVis, IEEE Visualization, and the EuroGraphics STARs series.

• Medical Imaging
• Neuroscience
• Radiology
• +3 more

Lea Starck is a Researcher affiliated with the Department of Informatics at the University of Bergen. Her work focuses on advanced medical imaging techniques, particularly in neuro-oncology and radiology applications. She contributes to projects involving dynamic susceptibility contrast MRI (DSC-MRI) for evaluating brain metastases and other clinical conditions. Her research integrates quantitative MRI methods with clinical outcomes, emphasizing tumor angiogenesis, treatment response prediction, and pediatric joint imaging. Lea has been involved in academic recruitment efforts through the VisGroup, including roles in hiring for visualization and data science positions. Her publications span topics from tumor characterization to motion correction in MRI, reflecting her interdisciplinary approach to biomedical engineering challenges. No specific scientific awards are documented, though her active involvement in visualization and medical imaging research is evident through her publications and project leadership roles. Her work often bridges clinical and technical domains, with a focus on improving diagnostic accuracy and treatment planning through advanced imaging analytics. While no formal advising or grant details are provided, her contributions to the VisGroup highlight her role in fostering academic and applied research initiatives.

Zhirong YangView profile

Professor

• Machine Learning
• Information Visualization
• Data Representation Learning
• +6 more

Zhirong Yang is a Professor at the Department of Computer Science at the Norwegian University of Science and Technology (NTNU). He is affiliated with the Norwegian Open AI Lab and focuses on machine learning, information visualization, and data representation learning. His work integrates techniques like neural attention models, nonnegative matrix factorization, and dimensionality reduction algorithms such as Neighbor Embedding (NE) for visualization. Yang's research has applications in bioinformatics, medical imaging, and engineering systems. His educational background includes advanced studies in pattern recognition and computational methods, though specific details are not provided. Research collaborations span institutions globally, including work on bacterial population genomics and chromosome classification. Key contributions include scalable optimization frameworks for visualization (NE) and novel clustering algorithms using graph random walks and low-rank matrix decompositions. Yang has developed software tools like ChordMixer for sequence processing and HSSNE for robust data visualization. His recent publications emphasize advancements in attention mechanisms, sparse factorization, and medical diagnostics. He actively contributes to open-source projects and collaborates with industry on applications like subsea pipeline inspection and energy scheduling systems.

• Machine Learning
• 3D Imaging
• Image and Video Processing
• +7 more

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.

• Medical Informatics
• Artificial Intelligence in Healthcare
• Medical Education Technology
• +4 more

Svein Ivar Lillehaug is an Assistant Professor at the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences. He specializes in interdisciplinary research at the intersection of medical informatics, artificial intelligence, and healthcare education technology. His research interests focus on: Adaptive technology applications in mental health care Visual analytics for clinical decision support Computer-based medical education Simulation systems for ventilator therapy training Human-computer interaction in healthcare settings Although no formal awards or students are listed in the available information, his work demonstrates a long-standing commitment to improving medical education through technology. His publications span from foundational work in critical care simulation (1992-1998) to contemporary applications in digital mental health platforms.