Teaching

I teach tutorials and seminars at the University of Hamburg Business School and give workshops for researchers and practitioners. Most sessions include code examples that participants work through themselves.

Teaching

Master's courses

  1. 2026–27

    Seminar: Recent Developments in AI and Foundation Models

    Seminar

    I supervise seminar papers on the robustness of models against adversarial attacks and on uncertainty in foundation models.

  2. 2025–26

    Seminar: Recent Developments in (Agentic) AI and Large Language Models

    Seminar

    Students study current research on large language models and AI agents and present it in a seminar paper and a talk.

  3. 2024–26

    Introduction to Deep Learning

    Tutorials

    In the tutorials, students apply neural networks to problems in computer vision and time series analysis and work with generative models.

  4. 2024–27

    Statistical Programming with Python

    Tutorials

    Students learn to analyse data with modern statistical methods in Python, including regression and classification methods, in a block course with lectures in the morning and exercises in the afternoon.

  5. 2024–25

    Seminar: Large Language Models

    Seminar

    Students study how current large language models work and how they can be used in research.

  6. 2024

    Seminar: Causal Machine Learning

    Seminar

    Students study modern methods of causal inference for economics and implement them with DoubleML in reproducible simulation studies.

  7. 2023

    Machine Learning in Economics and Business

    Tutorials

    In exercises with R, students apply machine learning methods such as the lasso and boosting to economic models.

  8. 2023

    Seminars in Business Analytics and Deep Learning

    Seminar

    I supervised students in writing seminar papers and preparing their presentations.

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Bachelor's courses

  1. 2025–26

    Causal Inference and Digital Causality Lab

    Tutorials

    The weekly exercise sessions accompany the lecture Kausale Inferenz und Digital Causality Lab.

  2. 2024–25

    Quantitative Risk Management

    Tutorials

    The tutorials cover decision theory, risk measures and dependence, models for loss severity and frequency, and simulation.

  3. 2023–25

    Statistics I and II

    Tutorials

    The tutorials cover descriptive statistics, regression, time series analysis, hypothesis tests and multivariate methods.

  4. 2023–24

    Introduction to Mathematics and Statistics in Economics

    Tutorials

    The tutorials help students make the transition from school mathematics to the mathematics used at university.

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Workshops

  1. 2026

    Künstliche Intelligenz und das „Warum“

    Studienstiftung des deutschen Volkes, Sommerakademie Leysin, Switzerland

    Together with Oliver Schacht, I taught an introduction to machine learning and causality over ten days of the summer academy.

  2. 2025–26

    Trainings in Causal Machine Learning with DoubleML

    Economic AI, taught by members of the DoubleML development team

    Over two days, the training covers the basics of causal inference and double machine learning as well as sensitivity analysis and difference-in-differences, with exercises in Python on real data.

  3. 2023–26

    Digital Modelling Core

    Finance Academy, Novartis AG

    The course covers forecasting and machine learning methods and the interpretation of the resulting models in a business context.

  4. 2024

    Causal Inference and Machine Learning

    ZIB Academy

    In one day, participants move from the basics of causal reasoning to fitting double machine learning models in programming exercises.

  5. 2024

    Using ML to Find Out the Why

    PyCon DE & PyData Berlin · tutorial

    The tutorial introduces data scientists to causal inference with exercises in Python.

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For a workshop with your team or at your event, please contact me by email.