I was part of the Innovative Training Network SUNDIAL, which combines training in computer science and astrophysics. The young scientists in computer science will study topics such as detecting ultrafaint galaxy signals, developing automated models for galaxy recognition and classification, and developing new methods to compare observations and galaxy simulations as well as visualization. My project will focus on galaxy simulations and visualization. With galaxy simulations continuously becoming larger and more detailed, comparison with observation data is far from trivial.

To date most successful machine learning techniques for the analysis of complex interdisciplinary data predominantly use significant amounts of vectorial measurements as input to a statistical system. The domain expert knowledge is often only used in data preprocessing and the subsequently trained technique appears as a black-box, which is difficult to interpret or judge and rarely allows insight into the underlying natural process. However, in many bio-medical applications the underlying biological process is complex and the amount of measurements is limited due to the costs and inconvenience for the patient.