Research topics

Application examples & reference research

TreePOD - Generation of decision trees

Designing decision trees requires both domain knowledge and knowledge of the purpose of the model, which is why this process is very difficult to automate. TreePOD successfully takes a new approach and automatically generates several decision tree candidates with different parameter settings, allowing the most appropriate decision tree to be selected. TreePOD can be used for a variety of tasks and can also be integrated into existing workflows for creating and selecting decision trees. Click here to watch a video about TreePOD.

Weightlifter - visual exploration of weighting spaces for decision making

WeightLifter is a novel, interactive visualization technique that facilitates the exploration of weighting spaces with up to ten criteria. Our technique makes it possible to better understand the sensitivity of a decision to changes in weights, to efficiently locate weight ranges where a particular solution ranks high, and to filter out solutions that are not ranked high enough for a plausible combination of weights. Click here to watch a video about Weightlifter.

Intelligent simulations to improve interactive lighting concepts

In our research work within the HILITE and Sharc projects we develop tools and methods that optimize simulation processes for interactive lighting design. Since the processing of enormous amounts of data from different sources - quality of light sources, angles, spatial situation, etc. - must be taken into account, we use principles of visual parameter space analysis to accelerate the workflow massively and to calculate automatically generated suggestions for improvement of lighting situations. Such research projects emphasize the necessity of the interaction between spatial and abstract data worlds to make such and similar analyses possible in the first place.

Visual Analytics for Life Sciences and Medicine

We successfully apply Visual Analytics methods to help physicians and scientists to explore complex, heterogeneous data in the fields of Medicine and Life Sciences. These data come, for example, from studies on patient cohorts and observations, behavioral data from animal experiments, genetic data, but also, for example, from large collections of spatial measurement, image and network data on the brain. Our Visual Analytics solutions help when initial hypotheses need to be developed based on large amounts of data, but also when there is not enough data available to develop meaningful statistical results and models. Examples of our work include a Visual Analytics framework that supports the search for biomarkers for very rare cancers in children (see also: Visual Analytics and Data Science for the Healthcare System and Medical Research and our research project VISIOMICS) and comprehensive data management, data mining and Visual Analytics solutions for the Neurosciences, some of which are used worldwide (see also: Neuroscience - Visual Computing, Data Science and Big Data and Brain*).