Depth, relevance, and responsibility.
Across more than eight years and 70 ECTS of teaching at bachelor’s, master’s, and doctoral levels, I have connected durable principles with the systems and tools students will encounter in practice.
Foundations before black boxes
Students learn the assumptions behind a method, why it works, and where it can fail before they rely on it in a complex system.
Research meets practice
State-of-the-art ideas are taught alongside industrial tools and state-of-the-practice technologies, so students can critically evaluate both.
Learning by engineering
Hands-on, challenge-based activities make theoretical assumptions visible and help students build confidence, judgment, and intellectual independence.
Teaching in practice
Two recent course designs show how I connect theory to realistic engineering and AI challenges.
Control theory on real hardware
I redesigned Industrial Informatics activities around the Arduino Engineering Kit. Students move from equations and simulation to embedded controllers, sensors, actuators, delays, noise, and physical constraints.
Red-teaming generative AI
Students collaboratively design adversarial prompts and evaluate safety, bias, and fairness in large language models and AI image generators, turning responsible AI into a practical engineering exercise.
Current course
- 2025–Now
Project Management of AI Systems — part of the Applied Artificial Intelligence master’s degree.
Past teaching experience
Industrial Informatics
A 4.5 ECTS course connecting control techniques with simulation, embedded systems, and real hardware.
Verification and Validation of Embedded Systems
Six ECTS covering systematic approaches to dependable embedded software.
Programming foundations
Basic Programming and related subjects across Computer Science, Mechanical Engineering, and other engineering programmes.
Teaching through supervision
I combine clear methodological guidance with intellectual ownership. The goal is to help students develop their own research direction and become independent researchers and engineers who can test, understand, and improve the systems they build.