I am Simon. Currently, I am working as a senior data scientist at the Swiss Data Science Center in the academic cell.
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).
My papers and preprints can be found on Google Scholar. A selection is listed below.
My software and code is openly accessible on GitHub. Some packages are shown below.
sbijax
implements several methods for simulation-based inference.
surjectors
is a library for density estimation using surjective normalizing flows.
reconcile
is another Python package for probabilistic reconciliation of time series forecasts.
Ramsey
is a Python package for probabilistic modelling using JAX and Haiku. It implements recent developments in probabilistic machine and deep learning and among other things aims to bring these methods to a larger audience.
clad
is a proof-of-concept reverse-mode automatic differentiation package written in Clojure.
shm
is a Python package for probabilistic inference in structured hierarchical models.
R--
is an interpreter for the R language written in C++.
netReg
implements network-regularized regression models in R/C++.
cuda-etudes
is a selection of numerical CUDA recipes.
cluedo
implements the well known board game in JavaScript. This version plays in ancient Greece where Socrates, a true champion of the open society, has been murdered by one of his enemies.
PyBDA
is a Python library and command line tool for big data analytics and machine learning scaling to tera byte sized data sets.
Stan
is a state-of-the-art probabilistic programming langauge for Bayesian modeling and high-performance statistical computation.
NumPyro
is a lightweight probabilistic programming library that provides a NumPy backend for Pyro. It relies on JAX for automatic differentiation and JIT compilation to CPU/GPU/TPU.