A01 | Uncertainty Quantification and Analysis in Visualization

Prof. Daniel Weiskopf, University of Stuttgart
Email | Website

Prof. Oliver Deussen, University of Konstanz
Email | Website

Daniel Weiskopf
Oliver Deussen

Prof. Andreas Bulling, University of Stuttgart
Email | Website

Prof. Miriam Butt, University of Konstanz
Email | Website

Andreas Bulling
Miriam Butt

Project A01

David Hägele, University of Stuttgart – Email | Website

Patrick Paetzold, University of Konstanz – Email | Website

Michael Stroh, University of Konstanz – Email | Website

Sita Vriend, University of Stuttgart - Email | Website

Yumeng Xue, University of Konstanz, Email | Website

Our long-term goal is the modeling, handling, and quantification of uncertainty throughout the complete visual computing process, from visual-computing specific models of uncertainty to the visual representation of, and interaction with, uncertainty information. We aim to investigate models and methods for quantifying individual sources of uncertainty, the propagation of uncertainty through the visual computing pipeline, and the impact of uncertainty on the components of the visual computing process. Furthermore, we will advance the visualization and visual analytics of uncertainty, and investigate several different application examples.

Research Questions

How can we integrate and combine the different aspects of uncertainty in the various subareas of visual computing?

How can uncertainty be modeled and propagated through the different steps of the visualization process?

What are effective ways to communicate uncertainty to humans and how can they meaningfully interact with such displays?

Which methods are best suited to evaluate uncertainty visualization, and how can we quantify human understanding of uncertainty?

Fig. 1: Uncertainty-Aware Principal Component Analysis. The data consists of multivariate probability distributions, the plots shows the impact of different levels of variance.

Fig. 2: Bubble Treemap for visualizing uncertainty in hierarchical data. The uncertainty of the values in the hierarchy is visually encoded by the waviness of the outlines.

Publications

  1. S. A. Vriend, D. Hägele, and D. Weiskopf, “Two Empirical Studies on Audiovisual Semiotics of Uncertainty,” in Proceedings of the 20th International Audio Mostly Conference, ACM, Ed., New York, NY, USA: ACM, 2026, pp. 107–123. doi: 10.1145/3771594.3771604.
  2. D. Blumberg, P. Paetzold, M. Stroh, O. Deussen, D. A. Keim, and F. L. Dennig, “FluidMap: Proportional and Spatially Consistent Layout Enrichments in Multidimensional Projections,” Computer Graphics Forum, 2026, doi: 10.1111/cgf.70293.
  3. B. Chen, Y. Xue, P. Paetzold, and O. Deussen, “Demystifying UMAP artifacts: An interactive study on diagnosis and steering using 3D probes,” Information Visualization, 2026, doi: 10.1177/14738716261434908.
  4. S. A. Vriend and D. Weiskopf, “User Study on the Influence of Prior Beliefs on Gaze Behavior in Scatterplots,” in Proceedings of the 2026 Symposium on Eye Tracking Research and Applications, New York, NY, USA: ACM, May 2026, pp. 1–6. doi: 10.1145/3797246.3805720.
  5. M. Evers et al., “Uncertainty-Aware Visual Analysis of Force Networks in 2D Granular Materials,” Computer Graphics Forum, vol. 45, Art. no. 3, May 2026, doi: 10.1111/cgf.70438.
  6. C. Wu et al., “Probabilistic Inclusion Depth for Fuzzy Contour Ensemble Visualization,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–11, 2026, doi: 10.1109/tvcg.2026.3694451.
  7. P. Paetzold, R. Kehlbeck, Y. Xue, B. Chen, Y. Wang, and O. Deussen, “Neighborhood-Preserving Voronoi Treemaps,” IEEE Transactions on Visualization and Computer Graphics, vol. 32, Art. no. 1, 2026, doi: 10.1109/tvcg.2025.3633905.
  8. M. Stroh et al., “Using Saliency for Semantic Image Abstractions in Robotic Painting,” Computer Graphics Forum, vol. 44, Art. no. 7, 2025, doi: 10.1111/cgf.70259.
  9. T. Krake, D. Klötzl, D. Hägele, and D. Weiskopf, “Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess,” IEEE Transactions on Visualization and Computer Graphics, vol. 31, Art. no. 2, 2025, doi: 10.1109/tvcg.2024.3364388.
  10. R. Bauer, M. Evers, Q. Q. Ngo, G. Reina, S. Frey, and M. Sedlmair, “Voronoi Cell Interface‐Based Parameter Sensitivity Analysis for Labeled Samples,” Computer Graphics Forum, May 2025, doi: 10.1111/cgf.70122.
  11. M. Evers and D. Weiskopf, “Uncertainty-Aware Spectral Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 31, Art. no. 10, 2025, doi: 10.1109/tvcg.2025.3542898.
  12. S. A. Vriend, S. Vidyapu, K.-T. Chen, and D. Weiskopf, “Which Experimental Design is Better Suited for VQA Tasks?: Eye Tracking Study on Cognitive Load, Performance, and Gaze Allocations,” in Proceedings of the 2024 Symposium on Eye Tracking Research and Applications, ACM, Ed., New York, NY, USA: ACM, Jun. 2024, pp. 77:1–77:7. doi: 10.1145/3649902.3653519.
  13. P. Paetzold, D. Hägele, M. Evers, D. Weiskopf, and O. Deussen, “UADAPy: An Uncertainty-Aware Visualization and Analysis Toolbox.” IEEE, pp. 48–50, 2024. doi: 10.1109/uncertaintyvisualization63963.2024.00011.
  14. Y. Xue et al., “Reducing Ambiguities in Line-Based Density Plots by Image-Space Colorization,” IEEE Transactions on Visualization & Computer Graphics, vol. 30, Art. no. 1, Jan. 2024, [Online]. Available: https://www.computer.org/csdl/journal/tg/2024/01/10297597/1RyY1MBMcIo
  15. F. L. Dennig et al., “The Categorical Data Map: A Multidimensional Scaling-Based Approach,” in 2024 IEEE Visualization in Data Science (VDS), IEEE, 2024, pp. 25–34. doi: 10.1109/vds63897.2024.00008.
  16. D. Saupe, K. Rusek, D. Hägele, D. Weiskopf, and L. Janowski, “Maximum Entropy and Quantized Metric Models for Absolute Category Ratings,” IEEE Signal Processing Letters, vol. 31, pp. 2970–2974, 2024, doi: 10.1109/lsp.2024.3480832.
  17. N. Rodrigues, C. Schulz, S. Döring, D. Baumgartner, T. Krake, and D. Weiskopf, “Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, Art. no. 1, Jan. 2023, doi: 10.1109/TVCG.2022.3209429.
  18. M. Xue et al., “Taurus: Towards a Unified Force Representation and Universal Solver for Graph Layout,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, Art. no. 1, 2023, doi: 10.1109/TVCG.2022.3209371.
  19. P. Paetzold, R. Kehlbeck, H. Strobelt, Y. Xue, S. Storandt, and O. Deussen, “RectEuler: Visualizing Intersecting Sets using Rectangles,” Computer Graphics Forum, vol. 42, Art. no. 3, 2023, doi: 10.1111/cgf.14814.
  20. D. Hägele et al., “Uncertainty Visualization: Fundamentals and Recent Developments,” it - Information Technology, vol. 64, pp. 121–132, 2022, doi: 10.1515/itit-2022-0033.
  21. F. Schreiber and D. Weiskopf, “Quantitative Visual Computing,” it - Information Technology, vol. 64, pp. 119–120, 2022, doi: 10.1515/itit-2022-0048.
  22. R. Kehlbeck, J. Görtler, Y. Wang, and O. Deussen, “SPEULER: Semantics-preserving Euler Diagrams,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, Art. no. 1, 2022, [Online]. Available: https://www.computer.org/csdl/journal/tg/2022/01/09552459/1xibZ9AqsLu
  23. D. Hägele, T. Krake, and D. Weiskopf, “Uncertainty-Aware Multidimensional Scaling,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, Art. no. 1, 2022, doi: 10.1109/TVCG.2022.3209420.
  24. D. Weiskopf, “Uncertainty Visualization: Concepts, Methods, and Applications in Biological Data Visualization,” Frontiers in Bioinformatics, vol. 2, 2022, doi: 10.3389/fbinf.2022.793819.
  25. J. Görtler et al., “Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels,” in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, in CHI ’22. New York, NY, USA: Association for Computing Machinery, 2022, pp. 1–13. doi: 10.1145/3491102.3501823.
  26. S. Dosdall, K. Angerbauer, L. Merino, M. Sedlmair, and D. Weiskopf, “Toward In-Situ Authoring of Situated Visualization with Chorded Keyboards,” in 15th International Symposium on Visual Information Communication and Interaction, VINCI 2022, Chur, Switzerland, August 16-18, 2022, M. Burch, G. Wallner, and D. Limberger, Eds., ACM, Aug. 2022, pp. 1–5. doi: 10.1145/3554944.3554970.
  27. Y. Wang, M. Koch, M. Bâce, D. Weiskopf, and A. Bulling, “Impact of Gaze Uncertainty on AOIs in Information Visualisations,” in 2022 Symposium on Eye Tracking Research and Applications, ACM, Jun. 2022, pp. 1–6. doi: 10.1145/3517031.3531166.
  28. Y. Zhang, K. Klein, O. Deussen, T. Gutschlag, and S. Storandt, “Robust Visualization of Trajectory Data,” it - Information Technology, vol. 64, pp. 181–191, 2022, doi: 10.1515/itit-2022-0036.
  29. C. Schulz et al., “Multi-Class Inverted Stippling,” ACM Trans. Graph., vol. 40, Art. no. 6, Dec. 2021, doi: 10.1145/3478513.3480534.
  30. K. Schatz et al., “2019 IEEE Scientific Visualization Contest Winner: Visual Analysis of Structure Formation in Cosmic Evolution,” IEEE Computer Graphics and Applications, vol. 41, Art. no. 6, 2021, doi: 10.1109/MCG.2020.3004613.
  31. T. Müller, C. Schulz, and D. Weiskopf, “Adaptive Polygon Rendering for Interactive Visualization in the Schwarzschild Spacetime,” European Journal of Physics, vol. 43, Art. no. 1, 2021, doi: 10.1088/1361-6404/ac2b36/meta.
  32. K. Gadhave et al., “Predicting intent behind selections in scatterplot visualizations,” Information Visualization, vol. 20, Art. no. 4, 2021, doi: 10.1177/14738716211038604.
  33. M. Sondag, W. Meulemans, C. Schulz, K. Verbeek, D. Weiskopf, and B. Speckmann, “Uncertainty Treemaps,” in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), 2020, pp. 111–120. [Online]. Available: https://ieeexplore.ieee.org/document/9086235
  34. N. Brich et al., “Visual Analysis of Multivariate Intensive Care Surveillance Data,” in Eurographics Workshop on Visual Computing for Biology and Medicine, B. Kozlíková, M. Krone, N. Smit, K. Nieselt, and R. G. Raidou, Eds., The Eurographics Association, 2020.
  35. P. Balestrucci et al., “Pipelines Bent, Pipelines Broken: Interdisciplinary Self-Reflection on the Impact of COVID-19 on Current and Future Research (Position Paper),” in 2020 IEEE Workshop on Evaluation and Beyond-Methodological Approaches to Visualization (BELIV), IEEE, 2020, pp. 11–18. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9307759
  36. N. Rodrigues, C. Schulz, A. Lhuillier, and D. Weiskopf, “Cluster-Flow Parallel Coordinates: Tracing Clusters Across Subspaces,” in Proceedings of the Graphics Interface Conference (GI) (forthcoming), Canadian Human-Computer Communications Society / Société canadienne du dialogue humain-machine, 2020, pp. 0:1–0:11. doi: 10.20380/GI2020.38.
  37. K. Schatz et al., “Visual Analysis of Structure Formation in Cosmic Evolution,” in Proceedings of the IEEE Scientific Visualization Conference (SciVis), 2019, pp. 33–41. doi: 10.1109/scivis47405.2019.8968855.
  38. Y. Wang, Z. Wang, C.-W. Fu, H. Schmauder, O. Deussen, and D. Weiskopf, “Image-Based Aspect Ratio Selection.,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, Art. no. 1, 2019, [Online]. Available: https://ieeexplore.ieee.org/document/8440843
  39. J. Görtler, M. Spicker, C. Schulz, D. Weiskopf, and O. Deussen, “Stippling of 2D Scalar Fields,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, Art. no. 6, 2019, [Online]. Available: https://ieeexplore.ieee.org/document/8667696
  40. C. Schulz et al., “A Framework for Pervasive Visual Deficiency Simulation,” in Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 2019, pp. 1852–1857. [Online]. Available: https://ieeexplore.ieee.org/document/9044164
  41. V. Bruder, C. Schulz, R. Bauer, S. Frey, D. Weiskopf, and T. Ertl, “Voronoi-Based Foveated Volume Rendering,” in Proceedings of the Eurographics Conference on Visualization - Short Papers (EuroVis), J. Johansson, F. Sadlo, and G. E. Marai, Eds., Eurographics Association, 2019, pp. 67–71. doi: 10.2312/evs.20191172.
  42. J. Görtler, R. Kehlbeck, and O. Deussen, “A Visual Exploration of Gaussian Processes,” in Proceedings of the Workshop on Visualization for AI Explainability (VISxAI), 2018. [Online]. Available: https://distill.pub/2019/visual-exploration-gaussian-processes/
  43. J. Görtler, C. Schulz, O. Deussen, and D. Weiskopf, “Bubble Treemaps for Uncertainty Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, Art. no. 1, 2018, doi: 10.1109/TVCG.2017.2743959.
  44. C. Schulz, K. Schatz, M. Krone, M. Braun, T. Ertl, and D. Weiskopf, “Uncertainty Visualization for Secondary Structures of Proteins,” in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), IEEE, 2018, pp. 96–105. [Online]. Available: https://ieeexplore.ieee.org/document/8365980
  45. C. Schulz, A. Zeyfang, M. van Garderen, H. Ben Lahmar, M. Herschel, and D. Weiskopf, “Simultaneous Visual Analysis of Multiple Software Hierarchies,” in Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT), IEEE, 2018, pp. 87–95. [Online]. Available: https://ieeexplore.ieee.org/document/8530134/
  46. T. Spinner, J. Körner, J. Görtler, and O. Deussen, “Towards an Interpretable Latent Space: An Intuitive Comparison of Autoencoders with Variational Autoencoders,” in Proceedings of the Workshop on Visualization for AI Explainability (VISxAI), IEEE VIS, 2018. [Online]. Available: https://thilospinner.com/towards-an-interpretable-latent-space/
  47. C. Schulz, M. Burch, F. Beck, and D. Weiskopf, “Visual Data Cleansing of Low-Level Eye Tracking Data,” in Eye Tracking and Visualization: Foundations, Techniques, and Applications. ETVIS 2015, M. Burch, L. L. Chuang, B. Fisher, A. Schmidt, and D. Weiskopf, Eds., Springer International Publishing, 2017, pp. 199–216. doi: 10.1007/978-3-319-47024-5_12.
  48. K. Srulijes et al., “Visualization of Eye-Head Coordination While Walking in Healthy Subjects and Patients with Neurodegenerative Diseases,” in Poster (reviewed) presented on Symposium of the International Society of Posture and Gait Research (ISPGR), 2017.
  49. C. Schulz, N. Rodrigues, K. Damarla, A. Henicke, and D. Weiskopf, “Visual Exploration of Mainframe Workloads,” in Proceedings of the SIGGRAPH Asia Symposium on Visualization, ACM, 2017, pp. 4:1–4:7. doi: 10.1145/3139295.3139312.
  50. P. Gralka, C. Schulz, G. Reina, D. Weiskopf, and T. Ertl, “Visual Exploration of Memory Traces and Call Stacks,” in Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT), IEEE, 2017, pp. 54–63. doi: 10.1109/VISSOFT.2017.15.
  51. C. Schulz, A. Nocaj, J. Görtler, O. Deussen, U. Brandes, and D. Weiskopf, “Probabilistic Graph Layout for Uncertain Network Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, Art. no. 1, 2017, doi: 10.1109/TVCG.2016.2598919.
  52. K. Kurzhals, B. Fisher, M. Burch, and D. Weiskopf, “Eye Tracking Evaluation of Visual Analytics,” Information Visualization, vol. 15, Art. no. 4, 2016, doi: 10.1177/1473871615609787.
  53. D. Weiskopf, M. Burch, L. L. Chuang, B. Fischer, and A. Schmidt, Eye Tracking and Visualization: Foundations, Techniques, and Applications. Berlin, Heidelberg: Springer, 2016. [Online]. Available: https://www.springer.com/de/book/9783319470238
  54. K. Kurzhals, M. Hlawatsch, M. Burch, and D. Weiskopf, “Fixation-Image Charts,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), ACM, Ed., ACM, 2016, pp. 11–18. doi: 10.1145/2857491.2857507.
  55. T. Blascheck, F. Beck, S. Baltes, T. Ertl, and D. Weiskopf, “Visual analysis and coding of data-rich user behavior,” in IEEE Conference on Visual Analytics Science and Technology, IEEE, 2016, pp. 141–150. doi: 10.1109/vast.2016.7883520.
  56. C. Schulz et al., “Generative Data Models for Validation and Evaluation of Visualization Techniques,” in Proceedings of the Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV), ACM, 2016, pp. 112–124. doi: 10.1145/2993901.2993907.
  57. C. Schulz, M. Burch, and D. Weiskopf, “Visual Data Cleansing of Eye Tracking Data,” in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS), 2015. [Online]. Available: http://etvis.visus.uni-stuttgart.de/etvis2015/papers/etvis15_schulz.pdf

Project Group A

Models and Measures

A01 | Uncertainty Quantification and Analysis
A03 | Quantification of Visual Explainability
A07 | Visual Attention Modeling
A08 | Learning and Explaining Dimensionality Reduction
A09 | Scalability and Complexity in Immersive Analytics

 

Completed

A02 | Quantifying Visual Computing Systems
A04 | Quantitative Models for Visual Abstraction
A05 | Visual Quality Assessment
A06 | Computational Photography

 

Project Group B

Adaptive Algorithms

B01 | Adaptive Self-Consistent Visualization
B04 | Motion Estimation
B06 | Adaptive Algorithms for Graph Views and View Transitions

 

Completed

B02 | Adaptive Network Visualization
B05 | Large Scale Variational 3D Reconstruction
B07 | Computational Uncertainty Quantification

 

Project Group C

Interaction

C01 | Evaluating Mixed Reality Experiences
C05 | Human-Machine Interaction with Adaptive Multisensory Systems
C06 | User-Adaptive Mixed Reality
C07 | Optimization for Dynamic XR User Interfaces

 

Completed

C02 | Physiologically Based Interaction
C03 | Immersive Virtual Environments
C04 | Mobile Visualization and Interaction
Quantifying interactions with adaptable multisensory systems

 

Project Group D

Applications

D02 | Visual Analytics in Linguistics
D04 | Immersive Analytics for the Life Sciences
D05 | Visual Computing for ERP-based Brain Activity

 

Completed

D01 | Modeling of 3D Virtual Cities
D03 | Exploration and Analysis of Provenance Data

 

Central Services

INF | Collaboration Infrastructure
Ö | Public Relations

FOR SCIENTISTS

Projects
People
Publications
Graduate School
Equal Opportunity

FOR PUPILS

BOGY
Events for the Public

PRESS AND MEDIA

Press releases
Images

SOCIAL MEDIA

© SFB-TRR 161 | Quantitative Methods for Visual Computing | 2019.