None
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.
If you want to chat, you can reach me here:

Code

You can find my software onGitHub.

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