Colormaps are a vital method for users to gain insights into data in a visualization. With a
good choice of colormaps, users are able to acquire information in the data more effectively …

Massive simulations and arrays of sensing devices, in combination with increasing
computing resources, have generated large, complex, high-dimensional datasets used to …

The goal of visualization is to effectively and accurately communicate data. Visualization
research has often overlooked the errors and uncertainty which accompany the scientific …

Various case studies in different application domains have shown the great potential of
visual parameter space analysis to support validating and using simulation models. In order …

Capturing an enclosing volume of moving subjects and organs using fast individual image
slice acquisition has shown promise in dealing with motion artefacts. Motion between slice …

We propose InSituNet, a deep learning based surrogate model to support parameter space
exploration for ensemble simulations that are visualized in situ. In situ visualization …

Regression models play a key role in many application domains for analyzing or predicting
a quantitative dependent variable based on one or more independent variables. Automated …

A common strategy in Multi-Criteria Decision Making (MCDM) is to rank alternative solutions
by weighted summary scores. Weights, however, are often abstract to the decision maker …

The ability to rapidly assess microtubule number in 3D image stacks from electron
tomograms is essential for collecting statistically meaningful data sets. Here we implement …

Balancing accuracy gains with other objectives such as interpretability is a key challenge
when building decision trees. However, this process is difficult to automate because it …