My research interests are focused in the area of Computer Graphics and Visualization. My current area of research is Information Visualization with a focus on the visualization of multi-dimensional data and of graphs in the areas of Biology, Software Engineering, Safety, and Land Use Planning (Urban Planning, Regional Planning). Information Visualization is key to analyzing and understanding an increasing amount of large multi-dimensional data and large graphs in these application areas. The goal of my research is to advance current techniques to enable users to conveniently and interactively analyze their data. It is based on the needs and workflow of the analysts, their data, and their tasks. Realizing the visualization and interaction ideas in software is crucial for this. The approaches, that I use, are based on mathematical and on computational methods, like projection, clustering, and graph drawing, and on design guidelines for perception.
In the area of multi-dimensional data visualization, I am currently exploring the combination of clustering algorithms and dimensionality reduction techniques to obtain an overview of large data sets (> 800.000 data items, > 10 dimensions). The clustering algorithms include k-means and consensus clustering, while the currently used dimensionality reduction technique is the Principal Component Analysis. Concerning graphs, I am interested in graph layout and graph drawing based on the topology of the graphs. A main feature of graphs in Software Engineering is that entities (e.g., Java classes) are connected by different types of edges (e.g., inheritance, method call, and aggregation). Adapted graph drawing approaches are necessary to handle this graph drawing instance.
In the future, these topics will stay in the focus of my research. Additionally, new application areas will provide new tasks, data, and problems to be solved. Depending on the problems of the cooperation partners from the application disciplines, new approaches will be developed allowing an efficient and effective analysis of their data.
To achieve these goals, third party funding will be acquired. Existing cooperations with Universities in Germany and abroad will be continued to support a rich exchange of research ideas.