Christian Hirsch

Christian Hirsch

Assistant Professor for Topological Data Analysis

University of Groningen

Christian Hirsch

I am Assistant Professor for Topological Data Analysis at the University of Groningen. I am affiliated with both the CogniGron and the Probability and Statistics Group, where I am studying random networks motivated from materials science and biology via techniques from topological data analysis and stochastic geometry.

Before that, I was Assistant Professor at the University of Mannheim. I was Postdoc at Aalborg University, at the LMU Munich and the WIAS Berlin. I received my PhD from Ulm University.


  • limit theorems in topological data analysis
  • stochastic channel models
  • statistical modeling and analysis of networks in materials science
  • random networks in statistical physics
  • interplay between dynamical systems and probability


  • PhD in Mathematics, 2014

    Ulm University

  • Diploma in Mathematics, 2010

    LMU Munich


Topological data analysis

Topological data analysis is based on an equally simple as intriguing principle. Leverage invariants from algebraic topology to gain novel insights into data. TDA is now applied in a wide variety of disciplines.

Inference in channel models

Channel modeling lies at the very foundation of wireless communication and is the basis for simulations of more complex communication systems.

Random network models for synaptic plasticity

Despite many parallels, there remain fundamental differences how artificial and real neural networks operate. This leads to the question:What properties should a dynamic network have to support the learning of complex patterns?

Wireless communication networks

In the context of the Internet of Things and in 5G cellular networks, Device-to-Device (D2D) communication plays a key role. This technology aims to reduce the load on the base station by allowing users to communicate with one another, either directly or through several intermediate steps.

Large deviations in stochastic geometry

What is the probability that a random geometric graph in a sampling window has atypically few or atypically many edges or triangles? What are the sources leading to such rare events? These examples illustrate the core questions of large devations in geometric probability.


  • PhD theses
    • D. Willhalm (since 05/20, University of Groningen). Large deviations in stochastic geometry
  • MSc theses
    • L. de Jonge (since 10/20, University of Groningen). Percolation in reinforcement-based models for synaptic plasticity
    • Y. Couzinié (09/18, LMU Munich). Sublinearly reinforced Pólya urns on graphs of bounded degree
    • F. Rudiger (09/18, LMU Munich). Recurrence and transience of graphs generated by point processes
    • A. Hinojosa Calleja (08/16, TU Berlin). Interference in ad-hoc telecommunication systems in the high-density limit
    • E. Rolly (06/16, TU Berlin). Gibbs-Masse für Trajektorien von Nachrichten in einem Kommunikationsnetzwerk
    • A. Tóbiás (04/16, TU Berlin). Highly dense mobile communication networks with random fadings
  • BSc theses
    • J. Langenbahn (12/19, University of Mannheim): Konvergenz des Pseudo-Marginalen MCMC Verfahren
    • H. Blocher (02/18, LMU Munich): Poisson Matching
    • F. Brück (06/17, LMU Munich): Percolation properties of Poisson graphs