Teaching
Teaching
Machine Learning Seminar
Guest lectures within the graduate Machine Learning Seminar.
Linear Models and Design of Experiments
I taught practical sessions focused on linear regression models and experimental design, with an emphasis on implementation and data analysis in R. My teaching approach centered on helping students translate theoretical concepts into working statistical models, write reproducible code, and critically interpret outputs. Particular attention was given to model diagnostics, experimental structure, and the connection between assumptions and results.
Statistics
I taught both theory and practice in an introductory statistics course for engineering students. Alongside foundational topics in descriptive statistics, probability, and inference, I introduced students to statistical computing in R, focusing on hands-on data analysis and interpretation. The goal was to equip students with practical tools to analyze real data and to understand statistical reasoning in applied health and engineering contexts.
Supervision
Master's thesis (co-supervision)