Teaching

Teaching Philosophy

My classes are usually made up of a mix of students; undergraduates, master students and PhD students and all from different fields of study. I enjoy this kind of dynamic classroom.
I tend to mix black-board lectures with computer demonstrations for all my classes. My goal in teaching is that the students leave my class recognizing that statistical modeling is not a "push-the-button" type exercise, and every data set requires unique consideration.

Courses & Workshops

In past years I have cycled teaching applied statistics couses (Linear models, Applied multivariate analysis) and method courses (Statistical inference principles and Survival analysis).
Due to a higher load of administrative duties as vice-dean, I only teach two courses at the moment. My main course is Statistical Learning for Big Data (MSA220/MVE441) given in LP4. In this course, I connect classical statistical methods to new machine learning ' methods like deep neural networks. The focus is to understand the assumptions that underpin each method, know their limitations and advantages. We explore the methods through theory and projects. In LP1, I co-teach Statistics and machine learning in high dimensions, EEN100 . This is a more theoretical companion course to Statistical Learning for Big Data. Here, we go more into detail how "big" or high-dimensional data can be a blessing, for example in community detection, spectral clustering and high-dimensional model selection.