Office 20-30/A21

Maria-von-Linden-Str. 6

D-72076 Tübingen

Marvin Pförtner

I am a PhD student in Philipp Hennig’s group at the University of Tübingen and the International Max Planck Research School for Intelligent Systems (IMPRS-IS). My research interests lie at the intersection of Bayesian machine learning and numerical analysis. More specifically, my work revolves around

  • algorithms for scalable (approximate) Gaussian process inference,
  • Gaussian process theory (sample path properties, Gaussian measure theory),
  • probabilistic numerical methods for partial differential equations, and
  • Bayesian deep learning with Laplace approximations.

I’m also interested in applications of all the above to scientific inference tasks.

I like to tackle problems using the framework of matrix-free (probabilistic) numerical linear algebra, which often leads to elegant and efficient algorithms.


Nov 28, 2023 I will give a tutorial at the Probabilistic Numerics Spring School 2024 taking place in Southampton on the 8th and 9th of April.
Jun 29, 2023 I will give a talk at the CRiSM Workshop on “Fusing Simulation with Data Science” at the University of Warwick on July 18.
May 1, 2023 I will give talks at the University of Cambridge (May 3), at the University of Southampton (May 4), and at UCL (May 5).

selected publications

  1. Nathaël Da Costa, Marvin PförtnerLancelot Da Costa, and Philipp Hennig
  2. Marvin PförtnerIngo SteinwartPhilipp Hennig, and Jonathan Wenger
  3. In Advances in Neural Information Processing Systems, 2022