headed by Dr. Renata Raidou
Uncertainty quantification and visualization is a challenging and popular field of research. All data and processes happening around us involve several sources of uncertainty, which are often too complex to quantify and to analyze. Although some of these uncertainties can be minimized, others cannot be eliminated, and their analysis and communication are essential. Uncertainty visualization design is often not easy, as uncertainty tends to dominate over (certainty in) the data. Often, uncertainty comes as an additional channel of information, which needs to be visualized on top of other underlying data—increasing the complexity of the view and decreasing the understanding of implicated information. When approaching an uncertainty visualization problem, the choice of design methods is not an easy one. Within medical applications, uncertainty visualization is a controversial, but very important topic—especially, if it plays a role in clinical decision making.