Supervision & Teaching

Supervision

Please let me know if you are interested in doing your thesis or any other research project with me. Collaborations and supervising projects are my favorite aspects of my job :) 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's 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

• Sören Lambrecht (ETH 2025), Signal-Agnostic Uncertainty Quantification via Joint Calibration of Aleatoric and Epistemic Uncertainty in High Noise-to-Signal Settings

(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's 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 Neural 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 Zürich

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

• 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)

Teaching at TU Wien

Institute of Discrete Mathematics and Geometry

• Linear Algebra 1&2 (TA for large-scale question hours, 2017-2018)

Institute of Analysis and Scientific Computing

• Mathematics 1&2 for electrical engineering (TA, 2016-2017)

• Technical mentoring for electrical engineering (mentor, 2016-2018)

• Refresher course mathematics (TA, 2016-2018)