Research Projects
Publicly funded research projects I am or have been involved in as principal investigator. Contract research is not listed. See also the work group page and the university research profile.
Active
CoFill — Intelligent Circular Economy Through Advanced AI
AI-driven data analysis and prediction models to improve efficiency and sustainability in the circular economy.
JetsKI — Waterjet Cutting with Smart AI Modeling
Applying artificial intelligence to optimize waterjet machining of difficult-to-machine materials like high-strength ceramics.
TrAIber.NRW — Transformation der automotiven Industrie in der bergischen Region
Supporting automotive suppliers in the Bergisches Land region in coping with drivetrain transitions via digitalization and AI-driven innovations.
Completed
Digitales Mentoring
Developing AI-supported tools for the study entry phase: an adaptive mathematics practice environment that adjusts to individual student abilities, Educational Data Mining to identify dropout risks and success factors, and a Learning Analytics acceptance study, carried out across three universities of applied sciences.
SharKI — Shared Tasks als innovativer Ansatz zur Implementierung von KI- und Big-Data-basierten Anwendungen in der Hochschullandschaft
Developing shared tasks as an innovative teaching method. Shared tasks are friendly scientific competitions where participants create solutions to research problems. The project advances the TIRA platform with AI and Big Data capabilities, uniting research and teaching through problem-oriented, game-based learning.
TIMALIE — TIered MAchine Learning ArchItEctures
Developing tiered and continual learning methods (capsule networks, tree-based CNNs, and neural-network-based gradient boosting) that can learn new tasks without storing old data, enabling GDPR-compliant deployment. Applied to industrial quality assurance, autonomous robot navigation, and resource-constrained systems. Best Paper Award at Canadian AI 2022.
DIBS — Data MIning zur Beratung von Studierenden
Data mining approach to recognize dropout risks at early stages while prioritizing data protection.
SimCloud — Cloud-Based FEM Simulation
Making compute-intensive FEM simulations available as a cloud service while protecting sensitive engineering data. Security by design: domain decomposition distributes sub-problems across multiple cloud providers so that no single provider can reconstruct the original model. Machine learning optimizes load distribution across heterogeneous cloud resources.
Cooperation
Interested in a joint research project, contract research, or a thesis in cooperation with your company? Feel free to get in touch; we have long-standing experience in publicly funded projects with industry partners, especially SMEs.