I am interested in the application of statistical tools and machine learning to solve practical, chemical problems. This particularly involves the green transition and exploration of more sustainable chemistry. I also explore the use of automation for autonomous reaction optimization and analysis, with a particular focus on proper validation of mathematical models.

My skills include organic chemistry, polymer chemistry, chemometrics, programming, data analysis, and experimental design.

My teaching philosophy is based on dialogue and partly on Kolb's learning cycle. I am a strong advocate for problem-based learning, where students learn to solve complex, real-world problems. I teach subjects including  engineering courses and organic chemistry, as well as the application of  statistical and machine learning methods to chemical problems. I supervise semester- and BEng graduation projects, as well as serve as an external examiner for BSc/MSc theses and BEng graduation projects.