Nowadays, most successful machine learning (ML) techniques for the analysis of complex interdisciplinary data use significant amounts of measurements as input to a statistical system. The domain expert knowledge is o en only used in data preprocessing. The subsequently trained technique appears as a “black box”, which is difficult to interpret and rarely allows insight into the underlying natural process. Especially in cri cal domains such as medicine and engineering, the analysis of dynamic data in the form of sequences and me series is o en difficult.

Swarm collaborative Multi-Agent cybeR physical sysTems with shAred sensinG modalitiEs, 5G commuNication and micro-elecTromechanical Sensor arrays (SMART-AGENTS) The demand for mobile agents in industrial environments to perform various tasks is growing tremendously in recent years. However, changing environments, security considerations and robustness against failure are major persistent challenges autonomous agents have to face when operating alongside other mobile agents. Currently, such problems remain largely unsolved. Collaborative multi-platform Cyber-Physical-Systems (CPSs) in which different agents flexibly contribute with their relative equipment and capabilities forming a symbiotic network solving multiple objectives simultaneously are highly desirable.

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.

Adaptive dissimilarity measures, dimension reduction and visualization Learning Vector Quantization (LVQ) for supervised dimension reduction LVQ with an adaptive metric and limited rank (LiRaM LVQ) provides a supervised tool for dimension reduction. The left picture shows the global linear transformation of the 7 class UCI segmentation data to two dimension with a different number of prototypes per class learned by LiRaM LVQ. Supervised Nonlinear dimension reduction The localized version of LiRaM LVQ learns local linear projections attached to each prototype.

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.