Applied mathematics and machine learning perspectives on Big Data Problems in Computational Sciences

September 30, 2019 to October 4, 2019
Location : CECAM-DE-SMSM


  • Susanne Gerber (Institute for Developmental Biology and Neurobiology, Gutenberg University Mainz, Germany)
  • Christof Schuette (FU Berlin AND Zuse Institute Berlin, Germany)
  • Karin Everschor-Sitte (Johannes Gutenberg Universität Mainz, Germany)
  • Michael Wand (Johannes Gutenberg University Mainz, Germany)
  • Tristan Bereau (Max Planck Institute for Polymer Research, Mainz, Germany)
  • Pospisil Lukas (Universita della svizzera italiana, Switzerland)




This summer school aims at presenting and comparing important representatives from a wide variety of approaches that address these challenges, highlighting the differences and possible synergies between the quite different view points on their mathematical and computational limitations. Practical exercises in the afternoon sessions will also be tackling specific mathematical and computational issues that arise in the context of, e.g., biophysical and biomedical applications. In the hands-on sessions we will introduce several new open-source tools, illustrating them on toy model systems - as well as on an analysis of biomolecular data for a Parkinson-relevant alpha-synuclein molecule and on analysis of single cell human mRNA data.


1) Backgrounds in Bayesian and Markov modeling
2) Backgrounds in Neuronal Networks for dynamic systems
3) Dimension reduction and feature selection
4) Computational aspects (numerics, complexity scaling, assumptions bias)
5) Software tools