Research projects

With the still growing availability of computational power for simulation, the availability of novel imaging technologies and sensors of increasing spatial and time resolution, novel molecular techniques, and the increase of digitized patient data, we face an ongoing explosion of available data describing a specific process or incident in a machine, a patient, an animal or a plant. An integrated examination and analysis of these data can provide holistic insight but poses ongoing challenges on data integration, visualization, and data analytics. This challenge is further increasing if not only one of such data sets is available, but many, like from patient cohorts, animal experiments, co-simulations, or ensemble simulations.

Application goals of this project are to:

• Enhance and improve AI-based decision-making in digital radiology based on heterogeneous data.
• Enable intuitive access to and integrative visualization and analytics of ensembles of multi-scale multi-temporal imaging data and/or related multi-omics data for life science research.
• Support the comparison of complex events and processes in medicine and life sciences captured/described by sets of heterogeneous, multi-scale and/or multi-temporal data.
• Research on integrative visualization of co-simulations of different granularity.
• Support interactive exploration and analysis of ensembles of co-simulations of different granularity by novel integrative visual computing solutions with a special focus on comparison.