Research Group
Practical computer science

Our research deals with the processing of sensor data in the broadest sense. The work is interdisciplinary and takes place in close cooperation with the engineering sciences, electrical and microsystems engineering, production engineering, and even the social sciences. The focus is on very small computers that are smaller than a pinhead. We try to map and scale classical concepts of computer science and machine learning to these small computers, often supported by strong networking. Virtualization on and with these microcomputers is an important link for broad usability and robustness and is to be understood as a modern operating system. Finally, we are interested in integrating these computers into materials and technical components (material-integrated sensing and intelligent systems), for example, to enable structural health monitoring (SHM) as an application. New additive manufacturing technologies (printing technologies) allow the production of electronic components on a small scale and printing on any materials, and therefore we are also dealing with analog computers that can be designed with such (organic) electronic components. Unfortunately, these electronic circuits with printed transistors are difficult to design classically due to unreliability and parameter variation, and we want to use modern algorithms such as evolutionary and genetic algorithms, as well as simulative methods to design and integrate these analog computers meaningfully and reliably into digital systems (as co-processors). Here, too, virtualization plays a central role as abstraction.

In teaching, we offer the classic courses "Fundamentals of Operating Systems" and "Algorithms and Data Structures", which together represent a pillar of computer science and are the basis for computer systems used every day. In addition to formal methods, the practical implementation and programming of operating systems will be taught on the basis of experiments and laboratory exercises.