Page of Simon Pfahler

Publications

• Taming numerical imprecision by adapting the KL divergence to negative probabilities
  Simon Pfahler, Peter Georg, Rudolf Schill, Maren Klever, Lars Grasedyck, Rainer Spang, Tilo Wettig
  Stat. Comput. 34 (2024)
  also on arXiv: arXiv:2312.103021 [stat.CO] (2023)
• Reconstructing Disease Histories in Huge Discrete State Spaces
  Rudolf Schill, Maren Klever, Kevin Rupp, Y. Linda Hu, Andreas Lösch, Peter Georg, Simon Pfahler, Stefan Vocht, Stefan Hansch, Tilo Wettig, Lars Grasedyck, Rainer Spang
  Künstl Intell (2024)
• Modeling metastatic progression from cross-sectional cancer genomics data
  Kevin Rupp, Andreas Lösch, Y. Linda Hu, Chenxi Nie, Rudolf Schill, Maren Klever, Simon Pfahler, Lars Grasedyck, Tilo Wettig, Niko Beerenwinkel, Rainer Spang
  also on bioRxiv:2024.01.30.577989 (2024)

Posters & presentations

• ISMB/ECCB 2023: Using low-rank tensor formats to enable computations of cancer progression models in large state spaces
• MECO49: Taming numerical imprecision by adapting the KL divergence to negative probabilities
• TUG24: Easy colorblind-safe typesetting - General guidelines and a helpful LaTeX package
• COMPSTAT2024: Taming numerical imprecision by adapting the KL divergence to negative probabilities

Side projects

• Easy colorblind-safe typesetting in LaTeX: the colorblind package
• Rendering and exploration of Mandelbrot and Julia sets
• EQL÷ network implementation for neural symbolic regression, written in TensorFlow2