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{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{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"
}2020
[Bibtex] @article{Garrison-2020-IVE,
author = {Garrison, Laura and Va\v{s}\'{i}\v{c}ek, Jakub and Craven, Alex R. and Gr\"{u}ner, Renate and Smit, Noeska and Bruckner, Stefan},
title = {Interactive Visual Exploration of Metabolite Ratios in MR Spectroscopy Studies},
journal = {Computers \& Graphics},
volume = {92},
pages = {1--12},
keywords = {medical visualization, magnetic resonance spectroscopy data, information visualization, user-centered design},
doi = {10.1016/j.cag.2020.08.001},
abstract = {Magnetic resonance spectroscopy (MRS) is an advanced biochemical technique used to identify metabolic compounds in living tissue. While its sensitivity and specificity to chemical imbalances render it a valuable tool in clinical assessment, the results from this modality are abstract and difficult to interpret. With this design study we characterized and explored the tasks and requirements for evaluating these data from the perspective of a MRS research specialist. Our resulting tool, SpectraMosaic, links with upstream spectroscopy quantification software to provide a means for precise interactive visual analysis of metabolites with both single- and multi-peak spectral signatures. Using a layered visual approach, SpectraMosaic allows researchers to analyze any permutation of metabolites in ratio form for an entire cohort, or by sample region, individual, acquisition date, or brain activity status at the time of acquisition. A case study with three MRS researchers demonstrates the utility of our approach in rapid and iterative spectral data analysis.},
year = {2020},
pdf = "pdfs/Garrison-2020-IVE.pdf",
thumbnails = "images/Garrison-2020-IVE.png",
images = "images/Garrison-2020-IVE.jpg",
project = "VIDI",
}@article{Kristiansen-2020-VIV,
author = {Yngve Sekse Kristiansen and Stefan Bruckner},
title = {Visception: An Interactive Visual Framework for Nested Visualization Design},
journal = {Computers \& Graphics},
volume = {92},
pages = {13--27},
keywords = {information visualization, nested visualizations, nesting},
doi = {10.1016/j.cag.2020.08.007},
abstract = {Nesting is the embedding of charts into the marks of another chart. Related to principles such as Tufteâs rule of utilizing micro/macro readings, nested visualizations have been employed to increase information density, providing compact representations of multi-dimensional and multi-typed data entities. Visual authoring tools are becoming increasingly prevalent, as they make visualization technology accessible to non-expert users such as data journalists, but existing frameworks provide no or only very limited functionality related to the creation of nested visualizations. In this paper, we present an interactive visual approach for the flexible generation of nested multilayer visualizations. Based on a hierarchical representation of nesting relationships coupled with a highly customizable mechanism for specifying data mappings, we contribute a flexible framework that enables defining and editing data-driven multi-level visualizations. As a demonstration of the viability of our framework, we contribute a visual builder for exploring, customizing and switching between different designs, along with example visualizations to demonstrate the range of expression. The resulting system allows for the generation of complex nested charts with a high degree of flexibility and fluidity using a drag and drop interface.},
year = {2020},
pdf = "pdfs/Kristiansen-2020-VIV.pdf",
thumbnails = "images/Kristiansen-2020-VIV.png",
images = "images/Kristiansen-2020-VIV.jpg",
project = "MetaVis"
}@article{RadEx,
author = {M\"{o}rth, E. and Wagner-Larsen, K. and Hodneland, E. and Krakstad, C. and Haldorsen, I. S. and Bruckner, S. and Smit, N. N.},
title = {RadEx: Integrated Visual Exploration of Multiparametric Studies for Radiomic Tumor Profiling},
journal = {Computer Graphics Forum},
volume = {39},
number = {7},
year = {2020},
pages = {611--622},
abstract = {Better understanding of the complex processes driving tumor growth and metastases is critical for developing targeted treatment strategies in cancer. Radiomics extracts large amounts of features from medical images which enables radiomic tumor profiling in combination with clinical markers. However, analyzing complex imaging data in combination with clinical data is not trivial and supporting tools aiding in these exploratory analyses are presently missing. In this paper, we present an approach that aims to enable the analysis of multiparametric medical imaging data in combination with numerical, ordinal, and categorical clinical parameters to validate established and unravel novel biomarkers. We propose a hybrid approach where dimensionality reduction to a single axis is combined with multiple linked views allowing clinical experts to formulate hypotheses based on all available imaging data and clinical parameters. This may help to reveal novel tumor characteristics in relation to molecular targets for treatment, thus providing better tools for enabling more personalized targeted treatment strategies. To confirm the utility of our approach, we closely collaborate with experts from the field of gynecological cancer imaging and conducted an evaluation with six experts in this field.},
pdf = "pdfs/Moerth-2020-RadEx.pdf",
images = "images/Moerth-2020-RadEx.jpg",
thumbnails = "images/Moerth-2020-RadEx-thumb.jpg",
project = "ttmedvis",
doi = {10.1111/cgf.14172}
}