Juraj Pálenik is a researcher at the Department of Informatics , University of Bergen , specializing in scientific visualization. He earned an MSc in Computer Graphics and a BSc in Physics from Masaryk University in Brno. Current research focuses on mathematical methods in visualization, including implicit surfaces and parameter space analysis for atmospheric convection modeling. Collaborates with the Geophysical Institute and contributes to the gLidar Project ( glidar-project.github.io ), combining lidar systems and paraglider flight data for environmental studies. Key Research Themes : His work bridges visualization techniques with applications in atmospheric sciences and molecular dynamics. Notable publications explore: IsoTrotter (2021): Interactive modeling of atmospheric convection using isocontours. Scale-Space Splatting (2020): Multi-scale exploration of molecular dynamics data through spacetime reformulation.
Laura Ann Garrison is an Associate Professor in the Department of Informatics at the University of Bergen, focusing on visualization and human-data interaction in health sciences. She holds a PhD from UiB and an MSc in Biomedical Visualization from the University of Illinois at Chicago. Her research examines narrative and rhetorical strategies in medical visualization, with a focus on improving audience engagement and decision-making through design principles. Affiliations: Mohn Medical Imaging & Visualization Centre (MMIV), Visualization Group at UiB Education: PhD (UiB), MSc (UIC), BA (Northern Michigan University) Research interests include critical visualization, biomedical illustration, and the ethical implications of data representation in healthcare. She teaches courses such as INF 252 (Visualization) and co-chairs the Bio+MedVis Challenge workshop. Recipient of awards including the Dirk Bartz Prize (2023) and Karl-Heinz Höhne Award (2021). Active in outreach, delivering talks on visualization best practices and accessibility in Norway and internationally.
Jan Byška is an Adjunct Associate Professor at the University of Bergen's Department of Informatics and an Assistant Professor at Masaryk University in Brno, Czech Republic. He is affiliated with Visitlab and VisGroup research laboratories, focusing on molecular and time-dependent data visualization. His research addresses challenges in molecular dynamics simulations, protein structure analysis, and interactive visualization techniques for biological systems. Education: While specific educational details are not provided, his professional roles indicate advanced academic training in computer science and bioinformatics. Research Interests: Byška's work spans molecular visualization, bioinformatics tool development, and computational methods for analyzing protein structures and dynamics. Key areas include protein tunnel analysis, ligand transportation visualization, and immersive virtual reality applications in molecular modeling. Publications Trends: Recent work emphasizes VR-based molecular design platforms, automated snow height extraction from time-lapse data, and tools for chromatin architecture analysis. His contributions often bridge visualization techniques with domain-specific challenges in biology and medicine. Labs/Teams: Active member of Visitlab and VisGroup, collaborating on projects like LoopGrafter (protein loop engineering) and InVADo (molecular docking analysis). These groups focus on developing open-access visualization tools for scientific research.
Svein Brekke is a Senior Researcher at the University of Bergen's Department of Informatics. His work focuses on advancing visualization techniques for medical imaging, particularly ultrasound diagnostics. He contributes to the VisGroup, a research team dedicated to real-time visualization solutions. Research Interests: Ultrasound visualization pipelines Real-time medical imaging Segmentation and registration techniques Augmented reality applications in diagnostics His notable publication analyzes the ultrasound visualization workflow, categorizing techniques across pre-processing, rendering, and AR integration. No scientific awards are explicitly listed. Current roles include participation in VisGroup's research initiatives and academic hiring efforts.
Anne-Kristin Stavrum is a Researcher in the Department of Informatics at the University of Bergen. Her work focuses on innovative visualization techniques for biological systems, combining molecular modeling, animation, and interactive design to enhance scientific communication and education. Her research interests center on molecular visualization, biomedical illustration, and the development of tools for conveying complex biological processes. Notably, she contributes to projects like the PhysioIllustration initiative, funded by the Norwegian Research Council (NFR), which aims to create educational animations and visualizations of cellular and molecular mechanisms. Stavrum's publications emphasize interdisciplinary approaches, integrating computer graphics with biological data to address challenges in visualizing physiological processes such as polymerization and DNA repair. She collaborates with experts in visualization, bioinformatics, and molecular biology to advance methods for illustrating dynamic biological systems.
Helge Langseth is Professor at NTNU's Department of Computer Science, researching computational structures for decision-making under uncertainty. His expertise includes Bayesian networks, probabilistic graphical models, decision support systems, and machine learning. He leads research on scalable learning algorithms and probabilistic AI methods. He teaches courses in artificial intelligence, deep learning, and probabilistic methods. Langseth has published extensively on Bayesian inference methods, mixture models, and applications in reliability engineering and intelligent systems.
Giampiero Salvi is a Professor at the Department of Electronic Systems, Norwegian University of Science and Technology (NTNU). He is affiliated with the Signal Processing research group and holds academic qualifications from La Sapienza University of Rome (Civil Engineering) and the Royal Institute of Technology (Dr.Scient). His research focuses on artificial intelligence, machine learning, speech processing, and human-machine interaction. Key contributions include advancements in speech recognition systems, neural network architectures for video prediction, and applications in healthcare analytics and cybersecurity. His work bridges theoretical foundations with practical implementations, such as developing pronunciation assessment frameworks for children, Parkinson’s disease detection via speech analysis, and real-time speaker diarization systems. He has contributed to foundational research in acoustic-to-articulatory mapping, explainable AI for clinical prediction, and multimodal dialogue systems. His research often addresses challenges in low-resource languages and clinical settings, emphasizing interdisciplinary collaboration. Salvi’s recent publications highlight trends in foundational models, generative AI for video and speech, and ethical AI applications in healthcare. He actively participates in international conferences and collaborates with institutions like KTH Royal Institute of Technology and the University of Tartu, reflecting his global academic network.
Heri Ramampiaro is a Professor and Head of the Department of Computer Science and Informatics at NTNU. He directs the Norwegian Open AI-Lab and holds roles on multiple national boards, including the Norwegian Data Protection Board and the National Geodata Council. His research focuses on text analysis, information retrieval, and machine learning applications in healthcare, energy markets, and smart cities. Education: PhD in Computer Science (2001), MSc in Information Systems (unspecified). Research interests include algorithm design for pattern mining, anomaly detection in time series, and deep learning for medical imaging and recommendation systems. Recent work emphasizes infant movement analysis for cerebral palsy prediction and fraud detection in electricity markets. Publications span over 50 peer-reviewed articles, with key contributions in IEEE Access , ACM Transactions , and Computerized Medical Imaging and Graphics . His work integrates machine learning with real-world challenges like labor inspection efficiency and urban smart city systems. Leadership roles include directing NTNU's AI-Lab and leading government-appointed expert groups on industrial data sharing (2021-2022). Grant activities include funding from the Research Council of Norway and EU Horizon projects. Labs/Teams: Norwegian Open AI-Lab, Data and Artificial Intelligence research group (former leadership).
Riccardo De Bin is an Associate Professor at the University of Oslo, affiliated with the Department of Mathematics and the Statistics and Data Science group. His research focuses on statistical methodology, including high-dimensional data analysis, survival analysis, boosting methods, and resampling techniques. He has contributed to interdisciplinary applications in biomedical research, energy systems, and aviation safety. Research Interests : Asymptotic theory and resampling techniques Variable selection and penalized regression Statistical learning methods for survival analysis Integration of clinical and omics data Applications in energy storage and environmental engineering Recent Work : Recent publications highlight advancements in survival modeling using boosted first-hitting-time approaches, degradation analysis for lithium-ion batteries, and Bayesian methods for nonlinear models. He also addresses methodological challenges in high-dimensional biomedical data and publication bias in statistical practice. Teaching : Teaches advanced courses in statistical learning, including STK2100 (Machine Learning for Prediction), STK4030 (Statistical Learning: Advanced Regression), and STK-IN4300 (Statistical Learning Methods in Data Science).
Ingrid Kristine Glad is a Professor at the Department of Mathematics, University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. She serves as co-director of the Integreat Centre of Excellence and BigInsight Centre for Research-Based Innovation, and chairs the Abel Board (2022–2026). Her research focuses on statistical and machine learning methodologies for high-dimensional data, particularly in genomics, sensor systems, and maritime applications. She has pioneered methods like monotone regression, tailored graphical lasso, and Shapley-value-based explainability frameworks. Research Interests: Glad’s work integrates theoretical statistics with practical applications in anomaly detection, change-point analysis, and predictive modeling. She develops novel algorithms for analyzing large-scale datasets from genomics (e.g., gene networks) and industrial sensor streams (e.g., battery degradation in maritime batteries). Her methods emphasize interpretability and scalability, addressing challenges in both supervised and semi-supervised learning contexts. Publications: Her recent work includes advancements in Shapley-value explanations, maritime battery health monitoring, and biofouling impact analysis. Key themes across her articles are statistical methodology development, machine learning applications in engineering, and computational tools for genomic data integration. Over 50 peer-reviewed papers span journals like Expert Systems with Applications , Journal of Machine Learning Research , and BMC Bioinformatics . Grants & Leadership: Leads interdisciplinary projects funded by the Norwegian Research Council and EU initiatives. Her roles in major centers highlight her influence in shaping statistical research agendas. Supervises active PhD students focused on topics like lifetime analysis models and maritime system analytics. Labs & Collaborations: Central to the Genomic HyperBrowser platform and collaborations with maritime industry partners. Active in both theoretical statistics (e.g., penalized regression) and applied domains (e.g., autonomous ship safety modeling).
Anders Helgeland is an Associate Professor at the University of Oslo's Section for Autonomous Systems and Sensor Technologies (20% position at ITS). He holds a concurrent 100% research position at the Norwegian Defence Research Establishment (FFI). His work focuses on computational fluid dynamics, biomechanical modeling of cerebrospinal fluid (CSF) dynamics, turbulence visualization, and medical imaging techniques. Key research areas include analyzing CSF flow in Chiari malformation patients using numerical simulations and developing advanced visualization frameworks for 3D data analysis. Helgeland's publications span neurosurgical modeling, flow noise simulations, and turbulence analysis. His interdisciplinary approach bridges biomedical engineering with computational mechanics, contributing to both clinical and engineering applications. He has collaborated extensively with institutions like the University of Utah (Victor Haughton) and the Simula Research Laboratory (Kent-Andre Mardal). His research trends emphasize medical fluid dynamics, visualization tool development, and turbulence mechanics. Notable contributions include modeling CSF pulsation effects in spinal canals and creating multi-field visualization systems for complex datasets.
Steven Yves Le Moan is an Associate Professor at the Department of Computer Technology and Informatics, Norwegian University of Science and Technology (NTNU), Gjøvik campus. His research focuses on spectral imaging, hyperspectral data processing, machine learning applications in image quality assessment, and visual perception studies. Email: steven.lemoan@ntnu.no His research spans multiple domains including: Spectral and hyperspectral imaging systems Machine learning for environmental monitoring Color science and display technology Human visual perception modeling Image and video compression optimization Recent publications highlight advancements in dual-scale hyperspectral imaging, daylight spectral estimation, and energy-efficient HDR video encoding. His work frequently appears in journals like Sensors , IEEE Journal on Emerging and Selected Topics , and Optics Express . Teaching includes courses on computer graphics, vision, and project work for exchange students. No scientific awards or student advising information is currently available in the dataset.
Di Wu is an Associate Professor at the Norwegian University of Science and Technology (NTNU) , affiliated with the Department of ICT and Natural Sciences and the Department of Language and Literature. Located at the Ålesund campus, Wu's academic work bridges computational methods with environmental and linguistic applications. Research interests include: AI-driven solutions for urban water supply systems Marine environmental monitoring through citizen science platforms Fluid simulation techniques Deep learning architectures Epidemiological modeling Financial data analysis Recent publications demonstrate expertise in computer vision, multi-source remote sensing analysis, and multimodal sentiment processing. Wu's work spans applications in medical diagnostics, environmental science, and industrial automation. Key projects include: PlastOPol marine litter monitoring system COSE ocean simulation environment Computational risk modeling for sustainable urban water supply
Noeska Natasja Smit is a Professor at the Department of Informatics , University of Bergen (UiB). Her research bridges computer science and medical imaging , focusing on visualization , visual analytics , and virtual reality applications in healthcare. Research Interests: Smit specializes in creating interactive 3D environments for medical education and clinical decision-making. Her work leverages radiomics for cancer prognosis real-time visualization tools virtual reality integration in medicine Her expertise spans signal processing , image analysis , and human-computer interaction . Publications highlight her contributions to medical visualization and radiomics across multiple institutions (University of Bergen, Haukeland University Hospital, NTNU). Notable trends include: harnessing AI for tumor analysis innovative VR-based anatomy teaching dynamic visual storytelling for clinical data Projects: Currently leads a Tenure Track Position in Medical Visualization at UiB, collaborates on Imaging Biomarkers for Precision Medicine in AML , and contributes to the Center for Data Science (CEDAS) .
Andrea Raffo serves as a Researcher in the Department of Molecular Biosciences at the University of Oslo's Faculty of Mathematics and Natural Sciences, affiliated with the Computational 3D Genomics research group led by Paulsen. His work bridges computational mathematics and biological applications through advanced geometric analysis techniques. His educational background includes a PhD in Mathematics from the University of Oslo, preceded by Bachelor's and Master's degrees in Mathematics from the University of Genoa (Italy). This strong mathematical foundation enables his specialized research at the intersection of computational geometry and life sciences. Research Focus: Development of computational methods for geometric pattern recognition in biological systems Core Methodologies: 3D point cloud analysis, shape characterization, and algorithmic pattern detection Application Domains: Chromosome conformation capture data analysis, protein channel dynamics, and super-resolution microscopy image processing His publication record demonstrates consistent contributions to computational geometry with direct applications in biosciences. Recent work shows increasing specialization in translating geometric pattern recognition techniques to solve concrete problems in genomics and structural biology, particularly through participation in international benchmarking initiatives like SHREC. While no formal awards are documented in current records, his research has been consistently published in high-impact venues including Computer-Aided Design, Computer Aided Geometric Design, and Frontiers in Molecular Biosciences. His collaborative approach is evident through extensive co-authorship networks across European and Asian research institutions. Raffo actively contributes to the Computational 3D Genomics group, focusing on developing analytical tools for chromosome architecture analysis and protein structure characterization. His current projects involve advanced segmentation techniques for HP1α condensate structures in super-resolution microscopy, representing the cutting edge of computational bioscience methodology development.
/andershe/anders.jpg)
