I am Simon. Currently, I am working as a senior data scientist at the Swiss Data Science Center in the academic cell.

In my spare time, I enjoy developing open-source software and writing case studies. I also occasionally offer consulting in statistics and ML, in particular on causal inference.

I've conducted my doctoral studies at ETHZ in Switzerland, where I developed probabilistic methods for the analysis of genetic interventional data. Before that, I have studied computer science and bioinformatics at TU/LMU in Munich, Germany (and seven semesters of philosophy at LMU).

My research interests revolve mainly around causal inference, generative and probabilistic modelling, Bayesian inference, and probabilistic programming languages.

As a computer scientist turned applied statistician, I am particularly enthusiastic about probabilistic programming which, as a discipline, lies at the interface of both fields. Probabilistic programming languages (PPLs) use computer programs to represent probabilistic models and are able to automatically infer quantities of interest (usually posterior distributions) without users needing to manually implement specific samplers or optimizers. In that line of research, I frequently contribute to modern PPLs, e.g., the frameworks Stan or NumPyro.

As a former researcher in computational biology and wannabe-philosopher, I am keen on causal inference (CI) due to its relatedness to philosophy of science, epistemology and scientific discovery, and since it mathematically formalizes how (and if) cause-and-effect relationships can be established (CI concerns itself with the discovery and inference of cause-and-effect relationships).

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