I’m currently working as a research scientist and research software engineer at the German Aerospace Center (DLR).
My research activities are twofold.
On the one hand, I develop training material evolving around the broad topic of Research Software Engineering.
I’m also involved in teaching these topics to PhD students, researches, and others within DLR and in the HIFIS project.
Here, I’m part of the Consulting Service which supports researchers with software engineering, open source, and software licenses.
On the other hand, I research and design software applications and algorithms evolving around the research topic of Provenance and W3C standard PROV. In this context, I worked in several research projects from different technical domains. These domains include Software Engineering, Space Situational Awareness and Space Debris as well as Energy Systems.
Download my resumé.
MSc in Computer Science, 2017
BEng in Communication and Information Technology, 2014
Hochschule für Telekommunikation Leipzig (HfTL)
Communication and Electronics Technician, 2001
Karstadt Warenhaus AG
Assessments about the quality, reliability, and trust-worthiness of data used and generated in mission critical soft-ware systems are important. The Backbone Catalogue for Relational Debris information (BACARDI) provides a database related to orbit information about active and inactive objects in Earth orbit. It exports data products which are used in mission planning and to provide collision warnings. The criticality of these products highlights the importance of identifying invalid data. Additionally, legal frameworks necessitate clear attribution of data to its original contributors. We present a provenance model defined using the W3C PROV data model and describe how it is integrated into the BACARDI system, making the software provenance-aware. Our provenance model, which is tailored to specific use cases, allows for efficient graph database queries. It is designed to be reusable and compatible with other PROV models. We applied the provenance model to a highly scalable provenance recording architecture blueprint. While the presented provenance model is reusable and integrates with other PROV models, the architecture blueprint makes other applications provenance-aware and enables users to assess data quality and reliability.