Teaching

Current courses

• Mathematical Statistics (Winter)
• Fortgeschrittene Mathematische Methoden in der Statistik (Winter)
• Seminar: Theoretical Foundations of Deep Learning (Winter)
• Statistical Learning Theory (Summer)
• Methoden der Linearen Algebra in der Statistik (Summer)

Lecture notes

• Statistical Learning Theory
• Lineare Algebra
• Statistics for Astronomy and Physics students

Awards

Teacher of the Year 2020, Faculty of Science, Leiden University

Thesis supervision

My group is supervising theses at both BSc and MSc levels, on a wide range of topics. Our group’s primary interest is in developing and understanding statistical methods and models. Here’s a non-exhaustive list of areas of particular interest:

• Copulas and other dependence models
• Functional data analysis
• Time series
• Probabilistic machine learning
• Statistical learning theory
• Bootstrap, cross-validation, and other resampling methods

Of course, theses about these topics may involve applications to real problems in environmental sciences, engineering, finance, insurance, etc.

Interested LMU students may send an email with a recent transcript and indication of their interests.

Past highlights

A. Razawy: Spline Based Estimation for Sufficient Dimension Reduction
D. Krüger: General vine copula models for stationary multivariate time series
R. Weber: Value at Risk Estimation with Subset Simulation
A. Kreuzer: Analysing the spatial dependency among fire danger indices
A. Brauer: Kernel estimation of conditional copula densities