Stuttgart, Baden-Württemberg, Germany
I am a Project Manager and Data Engineer at ZEISS Semiconductor Manufacturing Technology, where I focus on modelling and automating process data to drive efficiency and quality in EUV manufacturing. Currently, I am managing a project developing data manipulation UIs, enabling teams to interact with and leverage process data more intuitively. My overarching goal is to foster digitalization and FAIR (Findable, Accessible, Interoperable, Reusable) data management, ensuring that data becomes a sustainable and strategic asset across the organization. With a doctoral background in Systems Biology, I specialized in modeling and quantifying uncertainty using Bayesian statistics across biochemical and medical systems. In that time, I discovered how much I enjoy bringing together stakeholders from diverse backgrounds to solve challenging problems collaboratively, something I’m grateful to continue and expand in my current role. I value that my position allows me to learn continuously from experts across disciplines while translating complex data and technology topics into practical solutions. I have a strong affinity for AI, Data Science, and emerging digital technologies, and I’m passionate about bridging the gap between data, people, and decision-making. My work is driven by curiosity, precision, and the conviction that good data and smart automation are key to innovation. I enjoy being in nature, hiking at great heights frees my mind, and allows me to leave everything else behind. So, if you have any ideas or questions about data/leading projects, or if you know an extraordinary hiking trail, I would be happy to get in touch with you.
Data scientist in the research group Quantifying Liver Perfusion-Function Relationship in Complex Resection – A Systems Medicine Approach (QuaLiPerF) . - Supervision of students and lectures - Organization of international workshops and conferences - Stochastic and deterministic modelling - Statistical analysis and uncertainty quantification - Handling of biomedical data - Optimal experimental design - Classification of data using AI
Cooperation on Bayesian uncertainty quantification for enzymatic models. The aim was to show a significant difference of enzyme metabolization dynamics in different crowding conditions. This could be accomplished by using FAIR formats and software.
Extension of an existing ODE model of the Epithelial-to-Mesenchymal Transition (EMT) with an intreresting tumour suppressor candidate. The model was simulated deterministically and stochastically to make model predictions and to investigate the role of the protein of interest in EMT. Therefore, parameters were estimated with experimental data. Further parameter identifiabiliy analysis was performed with this model.
Development of a statistical method for quality monitoring of antigen batches in EliSpot diagnostics.