Research

My research focuses on the utilization of machine learning techniques, particularly explainable AI methods, to accelerate the site assessment process of wind turbine projects. While machine learning and AI are widely employed in the wind energy industry, there is an imbalance in their application across all phases of a wind turbine project’s lifecycle. Consequently, my research targets one of the early phases, specifically the planning phase. By integrating explainable AI techniques with traditional machine learning methods, my aim is to enable more informed decision-making.

Teaching

I strive to create an engaging learning environment where students can grasp challenging topics with confidence. I primarily teach Master's courses in Machine Learning and Software Development and supervise Master's theses and semester projects that explore data-driven decision-making, machine learning, and software development. I also contribute to a variety of other courses, where I share how machine learning can offer fresh perspectives within different fields.