Research and Projects

My research lies at the intersection of Foundation Models for Dynamical Systems and eXplainable Artificial Intelligence (XAI). I am currently focused on transformer-based models pretrained on synthetic data for prediction and control of dynamical systems, while continuing to develop concept extraction methods that make deep neural networks more transparent and trustworthy. Below you can find my main research themes and funded projects.

Research Themes

Foundation Models for Dynamical Systems

Exploring transformer-based foundation models pretrained on synthetic data for prediction and control of dynamical systems.

eXplainable Artificial Intelligence (XAI)

Developing methods for concept extraction, localization, and learning to explain what deep neural networks learn through training.

Applied AI

Industrial applications of artificial intelligence across manufacturing, quality control, and transport domains.

Funded Projects

JUPITER AI Factory (JAIF)

EuroHPC AI Factory built around Europe’s first exascale supercomputer, enabling foundation model research for dynamical systems.

Internet of Production

Enabling a new level of cross-domain collaboration by providing context-aware data and AI methods from production, development and usage in real-time.

QUISS - Increasing Rail Transport Quality Through Data-Based Damage Pattern Recognition

Developing data-based applications using modern data science and AI approaches to optimize scheduling and maintenance of rail vehicles.