Uncertainty-aware Visualization Techniques

I am happy to share that I have received my doctoral degree (Dr., similar to a Ph.D.) for the thesis Uncertainty-aware Visualization Techniques.

My work presents methods for visualizing uncertainty-afflicted networks, trees, point data, sequences, and time series—a fair amount of coverage in terms of data types.

My focus lies on modeling, propagation, and display of uncertainty. As an overarching approach for adapting existing visualization methods for uncertain information, I identify the layout process (the placement of objects).

The main difficulty is that these objects are not simple points but distribution functions or convex hulls. For visual encoding of uncertainty, I propose wave-like splines and sampling-based transparency. Also, I would like to point out two stippling-based methods for rendering that utilize the ability of the human visual system to cope with uncertainty, which indirectly shows that we as humans are uncertainty-aware.

I want to thank all the people I had the pleasure to work with and in particular, Oliver Deussen, Daniel Weiskopf, and Jochen Görtler.