Postdoctoral Researcher
at Bielefeld University
About Me
I am a postdoctoral researcher in computer science working at the Semantic Computing Group at Bielefeld University, Germany. My research focuses on Federated Learning, particularly within the healthcare domain and with a strong emphasis on dynamic participation and explainability. Previously, I contributed to the KINBIOTICS project funded by the German Federal Ministry of Health, which concluded in 2024. Since then, I have continued exploring Federated Learning with a specific focus on its applications in healthcare.
Beyond Federated Learning, I am broadly interested in enhancing medical treatment through data-driven Clinical Decision Support Systems and the use of explainable Artificial Intelligence. My goal is to leverage data science and machine learning techniques to address real-world challenges in healthcare and improve patient outcomes.
Originally from a small town of North Rhine-Westphalia, Germany, I received my PhD at Bielefeld University, my B.Sc. in Business Information Systems at FHDW Paderborn, Germany, and my Master's degree (M.Sc. Business Information Systems, focusing on Data Science) at Paderborn University. During that time, I worked at Bertelsmann SE & Co. KGaA for four years and joined the Social Computing Group of Paderborn University as Student Assistant. Finally, I joined the Semantic Computing Group in October 2021.
My research interests include:
We share some research interests? Feel free to contact me to exchange some ideas!
Through an extensive user study, we investigate to what extent algorithmic evaluation metrics for counterfactual explanations, e.g., Sparsity and Proximity, reflect and predict human assessment.
We propose TimeShap-FL, which generates SHAP-like explanation for time-series prediction in a distributed setting. Our approach improves explanation accuracy while maintaining full data privacy.
This dissertation introduces Federated Learning Platforms (FLPs), a platform-oriented extension of federated learning designed for dynamic, real-world collaboration. It develops mechanisms for scalable governance, explainable client admission, imbalance-aware training, andlearning under concept drift, transforming federated learning from a static protocol into a continuously evolving, privacy-preserving machine learning platform.
Selected invited talks on Privacy-Preserving Learning, Artificial Intelligence, and their applications in healthcare.
Invited talk at the 1st International Workshop on Knowledge Graphs for Health (KG4Health), co-located with AIME 2026 at the University of Ottawa, Canada. The talk explored opportunities and challenges at the intersection of Federated Learning and Knowledge Graphs for distributed healthcare AI.
Joint invited talk with Prof. Dr. Jin Yamamura, combining technical and medical perspectives on how AI-based assistance systems can streamline workflows, support clinical teams, and reduce administrative burden in everyday healthcare practice.
Courses (Level, Type, Semesters):
