High-dimensional data is ubiquitous today and used in various domains from insurance fraud detection to biology. For the explorative analysis of such data, visualization is an important tool. Living in a three-dimensional world, we as humans have a hard time imagining four or five dimensions, let alone hundreds of dimensions. So how can we visually explore such data and what do we have to consider when doing so?
This team focusses on researching methods and applications for the visualization of multi- and high-dimensional data. The main focus is on dimensionality reduction techniques, their purpose, strengths and weaknesses, and when and how to use them. We are investigating how to make such methods more scalable, adapt them to various data, and improve the interpretability when visualizing their output. We test the applicability of the results in various application domains, with a focus on biological applications, such as visual exploration of single-cell data.
