Working with Uncertainty Workshop at IEEE VisWeek 2011

October 24 at IEEE VisWeek 2011, Providence, Rhode Island


Uncertainty is a certainty when working with real world data. This is true whether one is grappling with exascale data sets or squeezing out information from every bit of sparse data. This is also true whether one is working with spatio-temporal data or with multivariate relational data. Tools, techniques and methodologies are needed in every facet of dealing with uncertainty from representation, quantification, propagation, and visualization. The domain of expertise and applications that have a stake in addressing uncertainty is not limited to the visualization community. In fact, if we examine the ''uncertainty pipeline'', we need to consider how uncertainty is represented e.g. as a scalar quantity such as standard deviation, as a pair of scalar quantities such as min/max range, as a multivariate representing higher order statistics, or perhaps use the data distribution itself. Then, we need to consider how uncertainties are quantified e.g. is it via fuzzy logic, evidence theory, Bayesian methods, polynomial chaos theory, etc.? An important aspect in working with uncertainty is not only in identifying and quantifying the different types and sources of uncertainty, but also in tracking how those quantities propagate in numerical simulations or tree/graph based reasoning. A basic problem is how to do arithmetic operations on variables and quantities that have an associated uncertainty. Techniques can range from incorporating uncertainty into numerical models e.g. stochastic PDE's to reasoning about uncertainty in belief networks. Then, to make sense out of all these, uncertainty visualization plays a key role in depicting both data and their associated uncertainty in a clear, unbiased fashion. Depending on who the target audience is, the visualization task may also extend to risk communication e.g. for health concerns, for severe weather warnings, etc.

This workshop will bring together researchers and practitioners from different fields who have a strong interest for the proper treatment of uncertainty. It will provide a venue for describing and identifying open problems, current best practices, and discussions on challenges and long term directions.

Workshop Organizers:

  • Chris Johnson (University of Utah)
  • Alex Pang (University of California, Santa Cruz)

Program Committee:

  • Don Estep (Colorado State University)
  • Hans-Christian Hege (Zuse Institute Berlin)
  • Roger Ghanem (University of Southern California)
  • Omar Ghattas (University of Texas at Austin)
  • Eduard Gröller (Technische Universität Wien)
  • George Karniadakis (Brown University)
  • Gordon Kindlmann (University of Chicago)
  • Joe Kniss (University of New Mexico)
  • Robert Kosara (UNC Charlotte)
  • Robert Laramee (Swansea University)
  • Kwan-Liu Ma (UC Davis)
  • Raghu Machiraju (Ohio State University)
  • Torsten Moeller (Simon Fraser University)
  • Kristi Potter (University of Utah)
  • Penny Rheingans (University of Maryland, Baltimore County)
  • Gerik Scheuermann (University of Leipzig, Germany)
  • Holger Theisel (Otto von Guericke University of Magdeburg, Germany)
  • Rudiger Westermann (Technical University of Munich (TUM), Germany)
  • Nicholas Zabaras (Cornell University)
  • Song Zhang (Mississippi State University)


  1. Uncertainty Analysis for Complex Systems: Algorithms and Challenges (Dongbin Xiu, Purdue University)
  2. Adaptive Sampling with Topological Scores (Dan Maljovec, Bei Wang, Ana Kupresanin, Gardard Johannesson, Valerio Pascucci and Peer-Timo Bremer)
  3. Uncertainty Classification of Molecular Interfaces (Aaron Knoll, Kah Chun Lau, Bin Liu, Aslihan Sumer, Maria K.Y. Chan, Lei Cheng, Julius Jellinek, Jeffrey Greeley, Larry Curtiss, Mark Hereld, and Michael Papka)
  4. Non-Gaussian Data Assimilation with Stochastic PDEs: Visualizing Probability Densities of Ocean Fields (Pierre Lermusiaux, MIT)
  5. Summary Visualizations for Coastal Spatial-Temporal Dynamics (Sidharth Thakur, Laura Tateosian, Helena Mitasova, and Eric Hardin)
  6. Approximate Level-Crossing Probabilities for Interactive Visualization of Uncertain Isocontours (Kai Pöthkow, Christoph Petz, and Hans-Christian Hege)
  7. Invited Presentation: Visualizing Uncertainty in Health Care: Present Needs and Future Directions (Paul Han)
  8. The Mutual Information Diagram for Uncertainty Visualization (Carlos Correa and Peter Lindstrom)
  9. A Topology Based Visualization for Exploring Data with Uncertainty (Keqin Wu and Song Zhang)
  10. Predictability-Based Adaptive Mouse Interaction for Visual Flow Exploration [Video Clip] (Marcel Hlawatsch, Filip Sadlo, and Daniel Weiskopf)