Will be announced at the beginning of the semester.

Visualization plays an important role for data analysis as well as for the communication of findings in biology and medicine. As the data in these fields is complex and diverse---ranging from abstract data like gene expression, biological networks, or electronic health records to spatial data like molecular structures or medical imaging data---a wide range of different visualization methods have been developed. In the last decade, however, advances in visualization not only focus on creating meaningful representations of these data, but also on the development of novel visual analytics applications, which combine visualization with data analysis methods (e.g., by applying methods from machine learning for feature extraction). This "computer-assisted human-in-the-loop'' approach provides more comprehensive information and allows users to interactively explore their data.

In this seminar, we will discuss seminal methods and recent advances in the field of data visualization for biology and medicine. Special focus will be on interactive visualization and visual analytics techniques and how methods from one field can be applied in the other one.

Students will know current visualization methods for biological and medical data.
They will learn and understand how modern visual analytics applications are designed.
They will also learn how to critically judge existing visualization approaches.
This expertise will allow them applying their knowledge to create suitable visualization solutions for new challenges and data.

Course prerequisites: No formal requirements, but background knowledge in scientific/information visualization, computer graphics, or data science is helpful.

Literature: Will be announced at the beginning of the semester.