The award winners Julius Pfrommer from the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, André Homeyer from the Fraunhofer Institute for Digital Medicine MEVIS and Konstantin Böttinger from the Fraunhofer Institute for Applied and Integrated Security AISEC were able to convince the jury in their work not only by achieving remarkable results, but also by enriching developments in highly topical research areas.

Julius Pfrommer

»Distributed Planning for Self-Organizing Production Systems«

The work describes an automatic adaptation of the control of production plants to changing orders and framework conditions. The principle of self-organization through distributed planning is applied.

The evaluation of the thesis with the rating "with distinction", the numerous publications of the author and the high citation rates (h-index: 13) underline the high scientific level of the work. At the same time, the work is of very high practical relevance for the manufacturing industry and thus has a strong economic and thus also social impact.

André Homeyer

»Automated analysis of necrosis and steatosis in histological images – Practical solutions for coping with heterogeneity and variability«

The dissertation makes important contributions to the improvement of automated histological image analysis with a focus on the quantification of necrosis or the quantification of steatosis in histological sections of liver tissue. Newly developed scores enable reliable measurement of heterogeneously distributed tissue properties. Interactive training leads to reduced effort while still providing accurate results. Finally, methods are presented that enable accurate yet rapid automated analysis of even large image data sets.

Due to the thematic focus, the work has a very high societal relevance and also exhibits a high degree of interdisciplinarity, innovation as well as transferability and generalizability.

Konstantin Böttinger

»Fuzzing with Stochastic Feedback Processes«

In the context of the thesis, new AI-supported testing methods are presented to support modern software development processes and thus to improve the security of software. For this purpose, results from probability theory are translated into algorithms for testing software.

The work provides relevant new methods to secure complex software systems against cyber attacks. This aspect will become immensely important in view of the advancing digitalization and networking and the increasing importance of software in the industrial, governmental and private sectors. Since potentially vulnerable software is used to control critical infrastructures, industrial plants, production processes, medical devices and automated driving vehicles, practical approaches to solutions for increasing software and IT security have an enormous economic and social impact. In view of the fundamental and still increasing importance of IUK technologies for society, high attention should be paid to cognitive security.