Teaching

Supervision

Please let me know if you are interested in doing your thesis or any other research project with me. Supervising projects is my favorite aspect of teaching :) I have multiple open projects for different levels. Or you can propose your own topic (if it is related to my research).

(Co-)supervised Master theses

Marius Högger (UZH, 2020), Bayesian Optimization with Neural Networks
Daniel Montagana (ETH, 2021), Leveraging Variance Swaps for non-parametric Option Price Surface Modelling
Nicholas Delmotte (ETH, 2021), *Pricing Options via Machine Learning"
Tereza Burgetová (ETH, 2021), Breakdown robust training of neural networks for outlier detection
Sebastian Schein (ETH, 2022), Feature Learning in Infinite-Width Neural Networks
Sven Rosenthal (UZH/ETH, 2022), On Inductive Bias towards Multi-Task Learning of L2-Regularized ReLU Networks
Markus Chardonnet (ETH/IBM Research, 2023), Probabilistic Forecasting for Time Series Anomaly Detection
Felix Illes (ETH, 2024), Monotone Neural Networks: An Experimental Survey

(Co-)supervised Semester theses

Alexis Stockinger (ETH, 2021), On the Reduction of Deep ReLU Networks part 2
Aurelio Dolfini (ETH, 2022), ML-based Uncertainty Quantification on Real World Data
Michele Meziu (ETH, 2022), Learning Risk-neutral Measures with Neural Networks
Paul Gregoire (ETH, 2024), Multitask learning properties of Gaussian Processes and Neural Networks

(Co-)supervised Bachelor theses

Alexis Stockinger (ETH, 2020), On the Reduction of Deep ReLU Networks
Samuel Anzalone (ETH, 2023), Inverse Problem with Neural Networks for Calibration in Finance
Noah Gigler (ETH, 2024), Uncertainty Quantification of Option Price Extrapolation using Neutral Networks

(Co-)supervised research assistants/interns

Marius Högger (UZH, 2021-2022)
Julien Siems (UZH, 2021)

(Co-)supervised projects for ML in Finance

Aurelio Dolfini (ETH, 2021), Pricing the Passport Option with Deep Reinforcement Learning
Sven Rosenthal (ETH, 2021), NTK vs P-functional theory
Sebastian Schein (ETH, 2021), Pricing the Passport Option with Deep Reinforcement Learning
Theo Smerting (ETH, 2021), Pricing the Passport Option with Deep Reinforcement Learning
Alexis Stockinger (ETH, 2021), Pricing the Passport Option with Deep Reinforcement Learning

Teaching at ETH

Group 3 (Probability Theory, Insurance Mathematics and Stochastic Finance)

The office hours for Group 3 can be found here. Alternatively, you can ask questions regarding my courses during an exercise class or any time in the forum. You can ask generic math questions at math.stackexchange.com (you can additionally send me or your TA a link to your question via e-mail). If none of the above options suits you, please don't hesitate to write me an e-mail, if you are interested in doing a project with me (e.g., a thesis) or if you have any questions (connected to my research) or if you find any mistake in the lecture notes or the exercise sheet or if you have any other question or remark. (Please check the course website, VZZ, the forum, and the Group 3 website before asking me administrative questions via e-mail.)

Wahrscheinlichkeit und Statistik Spring 2020 (D-MATH) (Coordination and TA)
Machine Learning in Finance 2021 (Project supervision)
Wahrscheinlichkeitstheorie und Statistik Spring 2021 (D-ITET) (Coordination and TA)
Wahrscheinlichkeitstheorie und Statistik Spring 2022 (D-ITET) (Coordination)
Probability and Statistics Spring 2023 (D-MATH) (TA)
Probability and Statistics Spring 2024 (D-MATH) (Coordination and TA)

Jakob Heiss