Lead of FAIR Data & Distributed Analytics Group
Lead of FAIR Data & Distributed Analytics Group
Fraunhofer Institute for Applied Information Technology (FIT)
Institute for Medical Informatics, University Hospital of Cologne
I believe that AI and data science should be both powerful and trustworthy. My research develops machine learning, semantic web technologies, knowledge management, and FAIR data infrastructure that work reliably across domains—from healthcare and biodiversity to scholarly data and cultural heritage. With dual appointments at Fraunhofer FIT and University Hospital of Cologne, I build systems that enable researchers in any field to harness AI, semantic interoperability, and data science responsibly, transparently, and effectively.
Leading the FAIR Data & Distributed Analytics Group at Fraunhofer FIT
Data management, FAIR principles, data spaces
Deep learning, neural networks, AI applications
Natural language processing, text mining
Data governance, privacy, security
Explore my research areas - click to see related publications
Our paper "Evolutionary Soups: Evolving Mixture-of-Experts for Multi-Objective LLM Alignment" has been accepted to the EMNLP 2026 Main Conference. The work introduces an evolutionary mixture-of-experts approach for controllable multi-objective alignment of large language models.
Learn moreOur project "Synthetic Data as a Bridge Across Health Data Silos: Evaluating Privacy, Fidelity and Utility for Cross-Domain Patient Journey Analysis" has been approved for funding by the Deutsche Forschungsgemeinschaft (DFG) as part of its Exploration Phase. The project investigates how synthetic health data can bridge fragmented healthcare data environments while preserving privacy and supporting meaningful cross-domain patient journey analysis. The work will evaluate synthetic data along three key dimensions: privacy, fidelity, and utility, in collaboration with the Institute for Biomedical Informatics in Cologne and Berlin University of Applied Sciences.
Learn moreOur paper "Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters" has been accepted at ICPR 2026. The work studies how reinforcement-learning algorithms and hyperparameters influence cross-environment generalization using SHAP-based explanations.
Learn morePresenting our paper 'Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge' at the 25th International Semantic Web Conference (ISWC 2026), introducing an operational framework for FAIR Digital Objects that can autonomously validate evidence, apply policies, communicate updates, and reconcile conflicting knowledge.
Event DetailsParticipating in the final ARGUS project meeting from 3-5 November 2026, concluding the Horizon Europe project on non-destructive, scalable and smart monitoring of remote cultural heritage. The internal project meeting will be followed by the ARGUS Final Conference on 4-5 November, presenting the project's results on remote sensing, sensor networks, AI, digital twins, data integration, risk assessment, and preventive conservation.
Event DetailsPresenting our short paper 'Automated Construction of FAIR Digital Object Knowledge Graphs from Flat Cultural Heritage Records' at CIKM 2026. The work investigates how flat Europeana cultural heritage metadata can be transformed into machine-actionable FAIR Digital Object knowledge graphs using CIDOC-CRM, persistent identifiers, controlled vocabularies, and large language models.
Event Details