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Simon Pfahler
Simon Pfahler

I am a PhD student working on
machine learning applications ranging
from theoretical physics to bioinformatics.

Posters and presentations

I try to make my scientific work as reproducible and transparent as possible, so here you can find all of the presentations and posters that I showed at conferences over the years.
For archiving reasons, they are not updated, so if in doubt, always believe the newer information. Or even better, ask me.

  • Lattice 2026: Machine-Learning-Accelerated Multigrid Setup for Lattice QCD Dirac Solves
  • HISKP seminar Bonn 2026: Neural-network approaches for preconditioning the Dirac equation in lattice QCD
  • Lattice 2025: A novel gauge-equivariant neural network architecture for preconditioners in lattice QCD
  • RECOMB 2025: Exploiting weak modularity in cancer progression to infer large Mutual Hazard Networks
  • Seminar Mathematik des Maschinellen Lernens: Exploiting symmetries to achieve fast model convergence
  • COMPSTAT2024: Taming numerical imprecision by adapting the KL divergence to negative probabilities
  • TUG24: Easy colorblind-safe typesetting - General guidelines and a helpful LaTeX package
  • MECO49: Taming numerical imprecision by adapting the KL divergence to negative probabilities
  • ISMB/ECCB 2023: Using low-rank tensor formats to enable computations of cancer progression models in large state spaces

© 2026 Simon Pfahler.