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Tim Schneider

Analytic Computing


+49 711 685 88106

Business card (VCF)

Universitätsstraße 32
70569 Stuttgart
Room: 01.204

Office Hours

by appointment


My current research interests include:

  • Symbolic Regression
    • Bayesian Symbolic Regression (BSR)
  • Integration of Prior Knowledge into Machine Learning
    • (Physics) Informed Machine Learning
    • Physics Guided Neural Networks (PGNN)
  • Bayesian Methods and Probablistic Reasoning
    • Probabilistic Graphical Models (PGM)
    • Markov Chain Monte Carlo (MCMC)
  1. 2023

    1. T. Schneider, A. Totounferoush, W. Nowak, and S. Staab, “Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning,” 2023. [Online]. Available:
  2. 2022

    1. T. Schneider et al., “Detecting Anomalies within Time Series using Local Neural Transformations.,” arXiv preprint, 2022, [Online]. Available:
  3. 2020

    1. E. Wong, T. Schneider, J. Schmitt, F. R. Schmidt, and J. Z. Kolter, “Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications,” in 2020 IEEE Intelligent Vehicles Symposium (IV), in 2020 IEEE Intelligent Vehicles Symposium (IV). 2020, pp. 1753–1758. doi: 10.1109/IV47402.2020.9304765.
  • Machine Learning Tutorial for Engineering Scientists (Summer Term 2022)
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