Samuel Joray

Samuel received his Bachelor’s and Master’s degrees in Mathematics and Applied Mathematics, respectively, from ETH Zürich. During his Master’s, he focused on statistics, which led him to write his Master’s thesis on instrumental variables in causal machine learning, a method widely used to mitigate unobserved confounders in fields such as economics, social sciences, and healthcare. This experience introduced him to research and motivated him to delve deeper into the topic of causal machine learning.

In October 2024, Samuel joined the SCAI lab as a research intern. The project aims at improving explainability in machine learning-based methods through causal discovery and inference techniques enhancing trust in decision-making algorithms in healthcare.

In his free time, he enjoys playing tennis, hiking, and traveling by train.