Stefan Bruckner is a full professor of 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 complex heterogeneous data spaces. 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 currently serves on the Eurographics Executive Committee and is a member of ACM SIGGRAPH, Eurographics, and the IEEE Computer Society.
Publications
2021
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[Bibtex] @Article{Kristiansen-2021-SSG,
author = {Kristiansen, Y. S. and Garrison, L. and Bruckner, S.},
title = {Semantic Snapping for Guided Multi-View Visualization Design},
journal = {IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
volume = {},
pages = {},
doi = {},
abstract = {Visual information displays are typically composed of multiple visualizations that are used to facilitate an understanding of the underlying data. A common example are dashboards, which are frequently used in domains such as finance, process monitoring and business intelligence. However, users may not be aware of existing guidelines and lack expert design knowledge when composing such multi-view visualizations. In this paper, we present semantic snapping, an approach to help non-expert users design effective multi-view visualizations from sets of pre-existing views. When a particular view is placed on a canvas, it is “aligned” with the remaining views–not with respect to its geometric layout, but based on aspects of the visual encoding itself, such as how data dimensions are mapped to channels. Our method uses an on-the-fly procedure to detect and suggest resolutions for conflicting, misleading, or ambiguous designs, as well as to provide suggestions for alternative presentations. With this approach, users can be guided to avoid common pitfalls encountered when composing visualizations. Our provided examples and case studies demonstrate the usefulness and validity of our approach.},
note = {Accepted for publication, to be presented at IEEE VIS 2021},
project = {MetaVis,VIDI},
pdf = {pdfs/Kristiansen-2021-SSG.pdf},
vid = {vids/Kristiansen-2021-SSG.mp4},
thumbnails = {images/Kristiansen-2021-SSG.png},
images = {images/Kristiansen-2021-SSG.jpg},
keywords = {tabular data, guidelines, mixed initiative human-machine analysis, coordinated and multiple views},
doi = {10.1109/TVCG.2021.3114860},
}@InProceedings{Garrison-2021-EPP,
author = {Laura Garrison and Monique Meuschke and Jennifer Fairman and Noeska Smit and Bernhard Preim and Stefan Bruckner},
title = {An Exploration of Practice and Preferences for the Visual Communication of Biomedical Processes},
booktitle = {Proceedings of VCBM},
year = {2021},
pages = {},
doi = {},
abstract = {The visual communication of biomedical processes draws from diverse techniques in both visualization and biomedical illustration. However, matching these techniques to their intended audience often relies on practice-based heuristics or narrow-scope evaluations. We present an exploratory study of the criteria that audiences use when evaluating a biomedical process visualization targeted for communication. Designed over a series of expert interviews and focus groups, our study focuses on common communication scenarios of five well-known biomedical processes and their standard visual representations. We framed these scenarios in a survey with participant expertise spanning from minimal to expert knowledge of a given topic. Our results show frequent overlap in abstraction preferences between expert and non-expert audiences, with similar prioritization of clarity and the ability of an asset to meet a given communication objective. We also found that some illustrative conventions are not as clear as we thought, e.g., glows have broadly ambiguous meaning, while other approaches were unexpectedly preferred, e.g., biomedical illustrations in place of data-driven visualizations. Our findings suggest numerous opportunities for the continued convergence of visualization and biomedical illustration techniques for targeted visualization design.},
note = {Accepted for publication, to be presented at VCBM 2021},
project = {VIDI,ttmedvis},
pdf = {pdfs/Garrison-2021-EPP.pdf},
thumbnails = {images/Garrison-2021-EPP.png},
images = {images/Garrison-2021-EPP.jpg},
url = {https://github.com/lauragarrison87/Biomedical_Process_Vis},
keywords = {biomedical illustration, visual communication, survey},
}![[YT]](https://vis.uib.no/wp-content/papercite-data/images/youtube.png)
[Bibtex] @article{Trautner-2021-LWI,
author = {Trautner, Thomas and Bruckner, Stefan},
title = {Line Weaver: Importance-Driven Order Enhanced Rendering of Dense Line Charts},
journal = {Computer Graphics Forum},
volume = {40},
number = {3},
pages = {399--410},
keywords = {information visualization, visualization techniques, line charts},
doi = {10.1111/cgf.14316},
abstract = {Line charts are an effective and widely used technique for visualizing series of ordered two-dimensional data points. The relationship between consecutive points is indicated by connecting line segments, revealing potential trends or clusters in the underlying data. However, when dealing with an increasing number of lines, the render order substantially influences the resulting visualization. Rendering transparent lines can help but unfortunately the blending order is currently either ignored or naively used, for example, assuming it is implicitly given by the order in which the data was saved in a file. Due to the noncommutativity of classic alpha blending, this results in contradicting visualizations of the same underlying data set, so-called "hallucinators". In this paper, we therefore present line weaver, a novel visualization technique for dense line charts. Using an importance function, we developed an approach that correctly considers the blending order independently of the render order and without any prior sorting of the data. We allow for importance functions which are either explicitly given or implicitly derived from the geometric properties of the data if no external data is available. The importance can then be applied globally to entire lines, or locally per pixel which simultaneously supports various types of user interaction. Finally, we discuss the potential of our contribution based on different synthetic and real-world data sets where classic or naive approaches would fail.},
year = {2021},
pdf = "pdfs/Trautner-2021-LWI.pdf",
thumbnails = "images/Trautner-2021-LWI-thumb.png",
images = "images/Trautner-2021-LWI-thumb.png",
vid = "vids/Trautner_2021_LineWeaver_video.mp4",
youtube = "https://youtu.be/-hLF5XSR_ws",
project = "MetaVis",
git = "https://github.com/TTrautner/LineWeaver"
}@article{Diehl-2021-HTC,
author = {Alexandra Diehl and Rodrigo Pelorosso and Juan Ruiz and Renato Pajarola and Meister Eduard Gr\"{o}ller and Stefan Bruckner},
title = {Hornero: Thunderstorms Characterization using Visual Analytics},
journal = {Computer Graphics Forum},
volume = {40},
number = {3},
pages = {},
keywords = {visual analytics, weather forecasting, nowcasting},
doi = {},
abstract = {Analyzing the evolution of thunderstorms is critical in determining the potential for the development of severe weather events. Existing visualization systems for short-term weather forecasting (nowcasting) allow for basic analysis and prediction of storm developments. However, they lack advanced visual features for efficient decision-making. We developed a visual analytics tool for the detection of hazardous thunderstorms and their characterization, using a visual design centered on a reformulated expert task workflow that includes visual features to overview storms and quickly identify high-impact weather events, a novel storm graph visualization to inspect and analyze the storm structure, as well as a set of interactive views for efficient identification of similar storm cells (known as analogs) in historical data and their use for nowcasting. Our tool was designed with and evaluated by meteorologists and expert forecasters working in short-term operational weather forecasting of severe weather events. Results show that our solution suits the forecasters workflow. Our visual design is expressive, easy to use, and effective for prompt analysis and quick decision-making in the context of short-range operational weather forecasting.},
year = {2021},
pdf = "pdfs/Diehl-2021-HTC.pdf",
thumbnails = "images/Diehl-2021-HTC.png",
images = "images/Diehl-2021-HTC.jpg",
vid = "vids/Diehl-2021-HTC.mp4",
project = "MetaVis"
}@ARTICLE {Garrison-2021-DimLift,
author = {Garrison, Laura and M\"{u}ller, Juliane and Schreiber, Stefanie and Oeltze-Jafra, Steffen and Hauser, Helwig and Bruckner, Stefan},
title = {DimLift: Interactive Hierarchical Data Exploration through Dimensional Bundling},
journal={IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
abstract = {The identification of interesting patterns and relationships is essential to exploratory data analysis. This becomes increasingly difficult in high dimensional datasets. While dimensionality reduction techniques can be utilized to reduce the analysis space, these may unintentionally bury key dimensions within a larger grouping and obfuscate meaningful patterns. With this work we introduce DimLift, a novel visual analysis method for creating and interacting with dimensional bundles. Generated through an iterative dimensionality reduction or user-driven approach, dimensional bundles are expressive groups of dimensions that contribute similarly to the variance of a dataset. Interactive exploration and reconstruction methods via a layered parallel coordinates plot allow users to lift interesting and subtle relationships to the surface, even in complex scenarios of missing and mixed data types. We exemplify the power of this technique in an expert case study on clinical cohort data alongside two additional case examples from nutrition and ecology.},
volume = {27},
number = {6},
pages = {2908--2922},
pdf = {pdfs/garrison-2021-dimlift.pdf},
images = {images/garrison_dimlift.jpg},
thumbnails = {images/garrison_dimlift_thumb.jpg},
youtube = {https://youtu.be/JSZuhnDyugA},
doi = {10.1109/TVCG.2021.3057519},
git = {https://github.com/lauragarrison87/DimLift},
project = {VIDI},
}@ARTICLE {Mueller-2021-IDA,
author = {M\"{u}ller, Juliane and Garrison, Laura and Ulbrich, Philipp and Schreiber, Stefanie and Bruckner, Stefan and Hauser, Helwig and Oeltze-Jafra, Steffen},
title = {Integrated Dual Analysis of Quantitative and Qualitative High-Dimensional Data},
journal={IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
abstract = {The Dual Analysis framework is a powerful enabling technology for the exploration of high dimensional quantitative data by treating data dimensions as first-class objects that can be explored in tandem with data values. In this work, we extend the Dual Analysis framework through the joint treatment of quantitative (numerical) and qualitative (categorical) dimensions. Computing common measures for all dimensions allows us to visualize both quantitative and qualitative dimensions in the same view. This enables a natural joint treatment of mixed data during interactive visual exploration and analysis. Several measures of variation for nominal qualitative data can also be applied to ordinal qualitative and quantitative data. For example, instead of measuring variability from a mean or median, other measures assess inter-data variation or average variation from a mode. In this work, we demonstrate how these measures can be integrated into the Dual Analysis framework to explore and generate hypotheses about high-dimensional mixed data. A medical case study using clinical routine data of patients suffering from Cerebral Small Vessel Disease (CSVD), conducted with a senior neurologist and a medical student, shows that a joint Dual Analysis approach for quantitative and qualitative data can rapidly lead to new insights based on which new hypotheses may be generated.},
volume = {27},
number = {6},
pages = {2953--2966},
pdf = {pdfs/Mueller_2020_IDA.pdf},
images = {images/Mueller_2020_IDA.jpg},
thumbnails = {images/Mueller_2020_IDA.png},
doi = {10.1109/TVCG.2021.3056424},
git = {https://github.com/JulianeMu/IntegratedDualAnalysisAproach_MDA},
project = {VIDI},
}@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= {2021},
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},
volume = {27},
number = {8},
doi = {10.1109/TVCG.2020.2973564},
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= {2021},
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},
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},
volume = {27},
number = {7},
pages = {3153--3167},
project = "MetaVis"
}