Hello, I am Simon 👋
I am currently working as a senior AI/ML engineer at Logitech . Previously, I was working in the research team at the Swiss Data Science Center , conducting my doctoral studies in computational statistics at ETH Zurich , and studying at TU Munich .
Code
You can find my software on GitHub .Research interests (in no particular order)
Before Logitech, where I work on deep learning and signal processing, I was mainly interested in:- Causal inference
- Applied probability (probabilistic modelling, stochastic processes, ...)
- Approximate and Bayesian inference (Markov Chain Monte Carlo, VI, Simulation-Based Inference)
- Probabilistic programming
- Generative modelling (score and flow matching, normalizing flows)
Recent work
- Causal posterior estimation ( arXiv:2505.21468 ): a new method for simulation-based inference that exploits the
conditional independence structure of the posterior programs and
encodes it into a new NN architecture.
Keywords: simulation-based inference, flow matching, causality - Simulated-Annealing ABC with multiple summary statistics ( arXiv:2505.23261 ): we propose a novel ABC method that works well on high-dimensional
parameter and data spaces using Simulated Annealing.
Keywords: ABC, MCMC - Simulation-based Inference with the Python Package sbijax ( arXiv:2409.19435 ): a Python package implementing simulation-based inference and ABC
methods in JAX.
Keywords: simulation-based inference, ABC, Python, JAX - High resolution seismic waveform generation using denoising
diffusion ( arXiv:2410.19343 ): a bespoke diffusion model trained from scratch on real
earthquakes to synthesize realistic, high-frequency seismic
waveforms.
Keywords: denoising diffusion, score matching, signal processing
Currently reading
- Erich Fromm, Fear of Freedom
- Erich Fromm, The Sane Society
- Hartmut Rosa, Resonance
- Francois Le Gall, Brownian Motion, Martingales, and Stochastic Calculus