About

I joined the chair of Statistical Learning and Data Science as a research scientist and member of the work group Causal and Fair Machine Learning in November 2024.

Initially, I started as a PhD student in the research group Data Mining and Machine Learning at the Faculty of Computer Science of the University of Vienna in December 2020 where I am supervised by Prof. Sebastian Tschiatschek and am part of the Probabilistic and Interactive Machine Learning work group. My research focuses on causal structure learning and probabilistic inference.

I obtained a Master's degree (M.Sc.) in Physics from the Technical University of Munich and a Bachelor's degree (B.Sc.) in Physics from the University of Augsburg.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

simon.rittel [at] stat.uni-muenchen.de

Research interests

• Probabilistic machine learning, deep generative models, Bayesian probability
• Causal reasoning and inference, probabilistic graphical models, causal discovery
• Interpretable and explainable machine learning, uncertainty quantification, sensitivity analysis

You Can Find me on

• GitHub
• LinkedIn

Teaching

• Winter 2024/25:
  • Seminar “Causal Discovery” (MA)
  • Seminar “Trustworthy ML” (BA)

Prior teaching @UniVie

• Co-lecturer:
  • Introduction to Machine Learning (MA): WiSe 2024/25
  • Mining Massive Datasets (MA): SoSe 2024
• Teaching assistant:
  • Introduction to Machine Learning (MA): WiSe 2022/23, 2023/24
  • Mining Massive Datasets (MA): SoSe 2022, 2023, 2024

Talks

• 28.03.2024, “Bayesian Causal Structure Leanring”, invited talk in the “Machine Learning and Modelling Seminar” at Charles University, Prag

References

1. Rittel S, Tschiatschek S (2024) On Differentiable Bayesian Causal Structure Learning Workshop on Causal Inference at the Conference on Uncertainty in Artificial Intelligence, Openreview.
   link|pdf.
2. Rittel S, Tschiatschek S (2023) Specifying Prior Beliefs over DAGs in Deep Bayesian Causal Structure Learning European Conference on Artificial Intelligence, IOS Press.
   link|pdf.