A01 | Uncertainty Quantification and Analysis in Visual Computing

Prof. Daniel Weiskopf, Universität Stuttgart
Email | Website

Prof. Oliver Deussen, Universität Konstanz
Email | Website

Daniel Weiskopf
Oliver Deussen

Prof. Ulrik Brandes, Universität Konstanz
Email | Website

Prof. Thomas Ertl, Universität Stuttgart
Email | Website

Ulrik Brandes
Thomas Ertl

Christoph Schulz, Universität Stuttgart – Email | Website

Jochen Görtler, Universität 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?

Can we model the flow and transformation of uncertainty through the visual computing pipeline?

Can we incorporate uncertainty in the visual computing stages and represent their effects on those stages?

Can we support visual analytics of uncertainty?

Texture-based technique to visualize uncertainty of time dependend particle positions along streamlines.

Flow Radar Glyphs.


  1. C. Schulz, M. Burch, and D. Weiskopf, Visual Data Cleansing of Eye Tracking Data. 2015.
  2. K. Kurzhals, M. Hlawatsch, M. Burch, and D. Weiskopf, “Fixation-Image Charts,” in ETRA, 2016, vol. Proceedings of the Symposium on Eye Tracking Research & Applications, no. 1, pp. 11–18.
  3. C. Schulz, A. Nocaj, M. El-Assady, S. Frey, M. Hlawatsch, M. Hund, G. K. Karch, R. Netzel, C. Schätzle, M. Butt, D. A. Keim, T. Ertl, U. Brandes, and D. Weiskopf, “Generative Data Models for Validation and Evaluation of Visualization Techniques.,” in BELIV Workshop 2016, 2016, pp. 112–124.
  4. D. Weiskopf, M. Burch, L. L. Chuang, B. Fischer, and A. Schmidt, Eye Tracking and Visualization: Foundations, Techniques, and Applications. Berlin, Heidelberg: Springer, 2016.
  5. T. Blascheck, F. Beck, S. Baltes, T. Ertl, and D. Weiskopf, “Visual Analysis and Coding of Data-Rich User Behavior,” 2016.
  6. J. Goertler, C. Schulz, O. Deussen, and D. Weiskopf, “Bubble Treemaps for Uncertainty Visualization,” IEEE Transactions on Visualization and Computer Graphics, 2018.
  7. C. Schulz, M. Burch, F. Beck, and D. Weiskopf, “Visual Data Cleansing of Low-Level Eye Tracking Data,” in Extended Papers of ETVIS 2015, 2016.
  8. C. Schulz, A. Nocaj, J. Goertler, O. Deussen, U. Brandes, and D. Weiskopf, “Probabilistic Graph Layout for Uncertain Network Visualization,” vol. 23, no. 1, 2017.
  9. K. Srulijes, C. Schulz, D. J. Mack, R. Jarosch, J. Klenk, L. Schwickert, M. Schwenk, W. Maetzler, D. Weiskopf, and C. Becker, “Visualization of eye-head coordination while walking in healthy subjects and patients with neurodegenerative diseases.” 2017.
  10. K. Kurzhals, B. Fisher, M. Burch, and D. Weiskopf, “Eye Tracking Evaluation of Visual Analytics,” 2015.
  11. P. Gralka, C. Schulz, G. Reina, D. Weiskopf, and T. Ertl, “Visual Exploration of Memory Traces and Call Stacks,” VISSOFT 2017, 2017.