Lecture — Methods for Mathematical Data Analysis (in German)
Lecture — Higher Analysis Announcement (PDF)
Teaching & Supervision
Clear ideas, careful proofs and active learning
I teach mathematics and data science in German and English, ranging from first-year engineering courses with several hundred students to specialised graduate lectures and seminars.
My courses combine carefully structured notes and visual material with detailed blackboard proofs, examples and extensive exercise programmes. I aim to bring students into contact with current research early in their studies.
Teaching highlights
- 2026 Teaching Award of the Faculty of Sciences, FAU Erlangen-Nürnberg.
- Extensive experience designing and leading large lectures for up to 450 students.
- Very strong student evaluations, including top departmental rankings.
- Development of digital exercises, self-tests, short videos and interactive course materials.
- Supervision of a prize-winning Master's thesis.
Current courses
WS 2026/27
SS 2026
Lecture — Neural Network Theory Announcement (PDF)
Lecture — Approximation Theory (in German) Ankündigung (PDF)
Course material, dates and rooms are available through the FAU campus system (StudOn).
Earlier courses at FAU Erlangen-Nürnberg and elsewhere
WS 2025/26
Lecture — Higher Analysis Announcement (PDF)
Seminar — Neural Network Approximation Announcement (PDF)
SS 2025
Lecture — Mathematics for Engineers A2 (in German)
Seminar — Neural Network Approximation
WS 2024/25
Lecture — Mathematics for Engineers A1 (in German)
SS 2024
Lecture — Approximation Theory (in German)
Seminar — Neural Network Approximation
SS 2023
Lecture — Mathematics for Engineers E2 (in German)
WS 2022/23
Lecture — Analysis III (in German)
Lecture — Mathematics for Engineers E1 (in German)
SS 2022
Lecture — Analysis II (in German)
WS 2021/22
Lecture — Analysis I (in German)
SS 2021
Seminar — Partial Differential Equations (in German) Ankündigung (PDF) online course
WS 2020/21
Lecture — Partial Differential Equations (in German) online course
SS 2020
Lecture — Distributions, Sobolev Spaces and Elliptic Differential Equations (in German) Ankündigung (PDF) online course
WS 2019/20
Lecture — Mathematics for Engineers A3 (in German)
Lecture — Regularity Theory of Elliptic PDEs (in German) Ankündigung (PDF)
SS 2019
Lecture — Mathematics for Engineers A2 (in German)
WS 2018/19
Seminar — Function Spaces (in German)
2010–2017
Lectures — Mathematics for Engineers A1–A4 (in German) repeatedly, WS 2010/11 through WS 2016/17
Seminar — Interpolation Spaces and Applications in Numerical Analysis (in German) WS 2014/15
2008–2009
Teaching assistant — Complex Analysis; Measure and Integral Theory (in German) University of Leipzig
Teaching assistant — Analysis I for Physicists (in German) FSU Jena
Supervised students — PhD
since 10/2026
Julian Billner — Overcoming the curse of dimensionality: A basis-theoretic approach to neural network approximation working title, FAU Erlangen-Nürnberg
since 01/2024
Nick Schneider — Adaptive methods for solving evolution equations on non-smooth domains: Convergence rates and approximation classes studied via regularity in anisotropic Besov spaces working title, FAU Erlangen-Nürnberg
2019–2022
Florá Orsolya Szemenyei — Besov regularity of elliptic and parabolic PDEs with inhomogeneous boundary conditions on Lipschitz domains FAU Erlangen-Nürnberg, 2022
Supervised students — Master theses
2026
Julian Billner — Schauder and Riesz bases formed by inner products of higher dimensional Lebesgue spaces inspired by ReLU neural networks
2026
Jonas Krüger — Approximation with neural networks in variable Lebesgue spaces
2025
Priyanka Yadav — Nonlinear approximation and deep ReLU networks: ReLU networks are at least as expressive as free knot linear splines Data Science
2023
Nick Schneider — Approximation classes for adaptive time-stepping finite element methods awarded the Fritz and Maria Hofmann Prize for an excellent Master thesis
2022
Metin Bozkurt — Sobolev regularity of parabolic PDEs with inhomogeneous boundary data
2019
Florá Orsolya Szemenyei — General Lipschitz spaces and Sobolev's embedding theorem
Supervised students — Bachelor theses
2024
Julian Billner — Properties of C(S,ℝ) and applications to neural networks
2024
Samuel Probst — Wiener's Tauberian theorems
2023
Ba Duc Duong — Applications of Morrey and Campanato spaces to partial differential equations
2023
Michael Koch — Approximation theorem of Weierstraß Bachelor thesis and Staatsexamensarbeit
2022
Thorsten Beischer — Theorems of Jackson-Bernstein type
2022
Kilian Seib — Generalizations of the approximation theorem of Weierstraß
2021
Steven Kellner — Eigenvalue estimates via entropy and approximation numbers
2016
Jinxuan Cheng — On an extreme class of real interpolation spaces
Thesis topics
I am happy to supervise Bachelor and Master theses in analysis, approximation theory and the mathematics of neural networks, both in Mathematics and in Data Science. If one of the topics above sounds interesting, or if you have an idea of your own, write to me.