Stefan Bruckner is a full professor in Visualization at the Department of Informatics of the University of Bergen, Norway. He received his master's degree (2004) and Ph.D. (2008), both in Computer Science, from the TU Wien, Austria, and was awarded the habilitation (venia docendi) in Practical Computer Science in 2012. Before his appointment in Bergen in 2013, he was an assistant professor at the Institute of Computer Graphics and Algorithms of the TU Wien.
His research interests include all aspects of data visualization, with a particular focus on interactive techniques for the exploration and analysis of spatial data. He has made significant contributions to areas such as illustrative visualization, volume rendering, smart visual interfaces, biomedical data visualization, and visual parameter space exploration. In addition to his contributions in basic research, he has successfully led industry collaborations with major companies such as GE Healthcare and Agfa HealthCare, and has 7 granted patents.
He is a recipient of the Eurographics Young Researcher Award, the Karl-Heinz-Höhne Award for Medical Visualization, and his research has received 9 best paper awards and honorable mentions at international events. He was program co-chair of EuroVis, PacificVis, the Eurographics Workshop on Visual Computing for Biology and Medicine, the Eurographics Medical Prize, and serves on the editorial board of Computers & Graphics. He is an ACM Distinguished Speaker and currently serves on the Eurographics Executive Committee. He is a member of ACM SIGGRAPH, Eurographics, and the IEEE Computer Society.
Publications
2020
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[Bibtex] @INPROCEEDINGS{Moerth-2020-CGI,
author = "Mörth, E. and Haldorsen, I.S. and Bruckner, S. and Smit, N.N.",
title = "ParaGlyder: Probe-driven Interactive Visual Analysis for Multiparametric Medical Imaging Data",
booktitle = "Accepted to appear at Computer Graphics International",
year = "2020",
abstract = "Multiparametric medical imaging describes approaches that include multiple imaging sequences acquired within the same imaging examination, as opposed to one single imaging sequence or imaging from multiple imaging modalities. Multiparametric imaging in cancer has been shown to be useful for tumor detection and may also depict functional tumor characteristics relevant for clinical phenotypes. However, when confronted with datasets consisting of multiple values per voxel, traditional reading of the imaging series fails to capture complicated patterns. Those patterns of potentially important imaging properties of the parameter space may be critical for the analysis. Standard approaches, such as transfer functions and juxtapositioned visualizations, fail to convey the shape of the multiparametric parameter distribution in sufficient detail. For these reasons, in this paper we present an approach that aims to enable the exploration and analysis of such multiparametric studies using an interactive visual analysis application to remedy the trade-offs between details in the value domain and in spatial resolution. Interactive probing within or across subjects allows for a digital biopsy that is able to uncover multiparametric tissue properties. This may aid in the discrimination between healthy and cancerous tissue, unravel radiomic tissue features that could be linked to targetable pathogenic mechanisms, and potentially highlight metastases that evolved from the primary tumor. We conducted an evaluation with eleven domain experts from the field of gynecological cancer imaging, neurological imaging, and machine learning research to confirm the utility of our approach.",
pdf = "pdfs/Moerth-2020-CGI-ParaGlyder.pdf",
images = "images/Moerth-2020-ParaGlyder.PNG",
thumbnails = "images/Moerth-2020-ParaGlyder-thumb.png",
youtube = "https://youtu.be/S_M4CWXKz0U",
publisher = "LNCS by Springer",
project = "ttmedvis"
}@article{StormFurru-2020-VGT,
author = {Syver Storm-Furru and Stefan Bruckner},
title = {VA-TRAC: Geospatial Trajectory Analysis for Monitoring, Identification, and Verification in Fishing Vessel Operations},
journal = {Computer Graphics Forum},
volume = {39},
number = {3},
pages = {},
keywords = {visual analytics, fisheries, monitoring},
doi = {},
url = {},
eprint = {},
abstract = {In order to ensure sustainability, fishing operations are governed by many rules and regulations that restrict the use of certain techniques and equipment, specify the species and size of fish that can be harvested, and regulate commercial activities based on licensing schemes. As the world’s second largest exporter of fish and seafood products, Norway invests a significant amount of effort into maintaining natural ecosystem dynamics by ensuring compliance with its constantly evolving sciencebased regulatory body. This paper introduces VA-TRAC, a geovisual analytics application developed in collaboration with the Norwegian Directorate of Fisheries in order to address this complex task. Our approach uses automatic methods to identify possible catch operations based on fishing vessel trajectories, embedded in an interactive web-based visual interface used to explore the results, compare them with licensing information, and incorporate the analysts’ domain knowledge into the decision making process. We present a data and task analysis based on a close collaboration with domain experts, and the design and implementation of VA-TRAC to address the identified requirements.},
year = {2020},
pdf = "pdfs/StormFurru-2020-VGT.pdf",
thumbnails = "images/StormFurru-2020-VGT.png",
images = "images/StormFurru-2020-VGT.jpg",
project = "MetaVis"
}![[DOI]](http://vis.uib.no/wp-content/plugins/papercite/img/external.png){kind=small}
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[Bibtex] @article{Trautner-2020-SunspotPlots,
author = {Trautner, T. and Bolte, F. and Stoppel, S. and Bruckner, S.},
title = {Sunspot Plots: Model-based Structure Enhancement for Dense Scatter Plots},
journal = {Computer Graphics Forum},
volume = {39},
number = {3},
pages = {551--563},
keywords = {information visualization, scatterplots, kernel density estimation},
doi = {10.1111/cgf.14001},
url = {},
eprint = {},
abstract = {Scatter plots are a powerful and well-established technique for visualizing the relationships between two variables as a collection of discrete points. However, especially when dealing with large and dense data, scatter plots often exhibit problems such as overplotting, making the data interpretation arduous. Density plots are able to overcome these limitations in highly populated regions, but fail to provide accurate information of individual data points. This is particularly problematic in sparse regions where the density estimate may not provide a good representation of the underlying data. In this paper, we present sunspot plots, a visualization technique that communicates dense data as a continuous data distribution, while preserving the discrete nature of data samples in sparsely populated areas. We furthermore demonstrate the advantages of our approach on typical failure cases of scatter plots within synthetic and real-world data sets and validate its effectiveness in a user study.},
year = {2020},
pdf = "pdfs/Trautner_2020_SunspotPlots_PDF.pdf",
thumbnails = "images/Trautner_2020_SunspotPlots_thumb.png",
images = "images/Trautner_2020_SunspotPlots_thumb.png",
vid = "vids/Trautner_2020_SunspotPlots_video.mp4",
youtube = "https://youtu.be/G6l-y6YGjzQ",
project = "MetaVis"
}@INPROCEEDINGS {Bolte-2020-ONC,
author = "Fabian Bolte and Stefan Bruckner",
title = "Organic Narrative Charts",
booktitle = "Proceedings of Eurographics 2020 (Short Papers)",
year = "2020",
month = "may",
abstract = "Storyline visualizations display the interactions of groups and entities and their development over time. Existing approaches have successfully adopted the general layout from hand-drawn illustrations to automatically create similar depictions. Ward Shelley is the author of several diagrammatic paintings that show the timeline of art-related subjects, such as Downtown Body, a history of art scenes. His drawings include many stylistic elements that are not covered by existing storyline visualizations, like links between entities, splits and merges of streams, and tags or labels to describe the individual elements. We present a visualization method that provides a visual mapping for the complex relationships in the data, creates a layout for their display, and adopts a similar styling of elements to imitate the artistic appeal of such illustrations.We compare our results to the original drawings and provide an open-source authoring tool prototype.",
pdf = "pdfs/Bolte-2020-ONC.pdf",
images = "images/Bolte-2020-ONC.jpg",
thumbnails = "images/Bolte-2020-ONC.png",
event = "Eurographics 2020",
keywords = "narrative charts, storylines, aesthetics",
project = "MetaVis",
git = "https://github.com/cadanox/orcha"
}@article{bolte2020splitstreams,
author= {Bolte, Fabian and Nourani, Mahsan and Ragan, Eric and Bruckner, Stefan},
journal= {IEEE Transactions on Visualization and Computer Graphics},
title= {SplitStreams: A Visual Metaphor for Evolving Hierarchies},
year= {2020},
keywords= {Information Visualization, Trees, Data Structures and Data Types, Visualization Techniques and Methodologies},
doi= {10.1109/TVCG.2020.2973564},
url= {https://arxiv.org/pdf/2002.03891.pdf},
note= {This paper is accepted and will be published soon.},
abstract= {The visualization of hierarchically structured data over time is an ongoing challenge and several approaches exist trying to solve it. Techniques such as animated or juxtaposed tree visualizations are not capable of providing a good overview of the time series and lack expressiveness in conveying changes over time. Nested streamgraphs provide a better understanding of the data evolution, but lack the clear outline of hierarchical structures at a given timestep. Furthermore, these approaches are often limited to static hierarchies or exclude complex hierarchical changes in the data, limiting their use cases. We propose a novel visual metaphor capable of providing a static overview of all hierarchical changes over time, as well as clearly outlining the hierarchical structure at each individual time step. Our method allows for smooth transitions between tree maps and nested streamgraphs, enabling the exploration of the trade-off between dynamic behavior and hierarchical structure. As our technique handles topological changes of all types, it is suitable for a wide range of applications. We demonstrate the utility of our method on several use cases, evaluate it with a user study, and provide its full source code.},
pdf= {pdfs/Bolte-2020-SplitStreams.pdf},
images= {images/Bolte-2020-SplitStreams.png},
thumbnails= {images/Bolte-2020-SplitStreams_thumb.png},
project = "MetaVis",
git = "https://github.com/cadanox/SplitStreams"
}@article{bolte2019visavis,
author= {Bolte, Fabian and Bruckner, Stefan},
journal= {IEEE Transactions on Visualization and Computer Graphics},
title= {Vis-a-Vis: Visual Exploration of Visualization Source Code Evolution},
year= {2020},
keywords= {Visualization System and Toolkit Design;User Interfaces;Integrating Spatial and Non-Spatial Data Visualization;Software Visualization},
doi= {10.1109/TVCG.2019.2963651},
issn= {2160-9306},
url= {https://arxiv.org/pdf/2001.02092.pdf},
note= {This paper is accepted and will be published soon.},
abstract= {Developing an algorithm for a visualization prototype often involves the direct comparison of different development stages and design decisions, and even minor modifications may dramatically affect the results. While existing development tools provide visualizations for gaining general insight into performance and structural aspects of the source code, they neglect the central importance of result images unique to graphical algorithms. In this paper, we present a novel approach that enables visualization programmers to simultaneously explore the evolution of their algorithm during the development phase together with its corresponding visual outcomes by providing an automatically updating meta visualization. Our interactive system allows for the direct comparison of all development states on both the visual and the source code level, by providing easy to use navigation and comparison tools. The on-the-fly construction of difference images, source code differences, and a visual representation of the source code structure further enhance the user's insight into the states' interconnected changes over time. Our solution is accessible via a web-based interface that provides GPU-accelerated live execution of C++ and GLSL code, as well as supporting a domain-specific programming language for scientific visualization.},
pdf= {pdfs/Bolte-2019-Visavis.pdf},
images= {images/Bolte-2019-Visavis.png},
thumbnails= {images/Bolte-2019-Visavis_thumb.png},
youtube= {https://www.youtube.com/watch?v=5XO6BU4j1KQ},
project = "MetaVis"
}@ARTICLE{Palenik-2019-Splatting,
author={J. P\'{a}lenik and J. By\v{s}ka and S. Bruckner and H. Hauser},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={Scale-Space Splatting: Reforming Spacetime for Cross-Scale Exploration of Integral Measures in Molecular Dynamics},
year={2020},
volume={26},
number={1},
pages={643--653},
keywords={Data visualization;Computational modeling;Time series analysis;Atmospheric measurements;Particle measurements;Analytical models;Kernel;Scale space;time-series;scientific simulation;multi-scale analysis;space-time cube;molecular dynamics},
doi={10.1109/TVCG.2019.2934258},
ISSN={1077-2626},
month={},
pdf = "pdfs/scale-space-splatting.pdf",
images = "images/scale-space-teaser.png",
thumbnails = "images/scale-space-teaser-thumb.png",
abstract = "Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space/time localization of the studied phenomena. This leads to degenerate cases where the measures fail to capture distinct behaviour. In order to drill into these aggregate values, we propose a multi-scale visual exploration technique. Our novel representation, based on partial domain aggregation, enables the construction of a continuous scale-space for discrete datasets and the simultaneous exploration of scales in both space and time. We link these two scale-spaces in a scale-space space-time cube and model linked views as orthogonal slices through this cube, thus enabling the rapid identification of spatio-temporal patterns at multiple scales. To demonstrate the effectiveness of our approach, we showcase an advanced exploration of a protein-ligand simulation.",
}@incollection {Bolte-2019-MVS,
author = {Bolte, Fabian and Bruckner, Stefan},
title = {Measures in Visualization Space},
booktitle = {Foundations of Data Visualization},
chapter = {3},
publisher = {Springer},
year = {2020},
pdf = {pdfs/Bolte-2019-MVS.pdf},
images = {images/Bolte-2019-MVS.png},
thumbnails = {images/Bolte-2019-MVS.png},
abstract = {Measurement is an integral part of modern science, providing the fundamental means for evaluation, comparison, and prediction. In the context of visualization, several different types of measures have been proposed, ranging from approaches that evaluate particular aspects of individual visualization techniques, their perceptual characteristics, and even economic factors. Furthermore, there are approaches that attempt to provide means for measuring general properties of the visualization process as a whole. Measures can be quantitative or qualitative, and one of the primary goals is to provide objective means for reasoning about visualizations and their effectiveness. As such, they play a central role in the development of scientific theories for visualization. In this chapter, we provide an overview of the current state of the art, survey and classify different types of visualization measures, characterize their strengths and drawbacks, and provide an outline of open challenges for future research.},
note = {This is a preprint of a chapter for a planned book that was initiated by participants of the Dagstuhl Seminar 18041 ("Foundations of Data Visualization") and that is expected to be published by Springer. The final book chapter will differ from this preprint.},
url = {https://arxiv.org/abs/1909.05295},
project = "MetaVis"
}