Tim Reichelt

Tim Reichelt

Probabilistic Programming, Probabilistic Inference

I am a PhD student at the University of Oxford lucky to be supervised by Luke Ong and Tom Rainforth as part of the AIMS Centre for Doctoral Training. I have broad interests in probabilistic machine learning, Bayesian statistics and deep learning. On a high-level, I like to build tools which help people make better data-driven decisions. More specifically, I do research in the field of probabilistic programming languages.



  • T. Reichelt , L. Ong , T. Rainforth , Pitfalls of Full Bayesian Inference in Universal Probabilistic Programming, in POPL Workshop on Languages for Inference (LAFI), 2023.


  • T. Reichelt , L. Ong , T. Rainforth , Rethinking Variational Inference for Probabilistic Programs with Stochastic Support, in Advances in Neural Information Processing Systems, 2022.
  • T. Reichelt , A. GoliƄski , L. Ong , T. Rainforth , Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently, in Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, 2022.