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 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.

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

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?

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.

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.

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**Fall 2020**Caput Statistics: Topological Data Analysis**Spring 2020**Probability Theory**Spring 2020**Stochastic Processes**Fall 2019**Markov Decision Processes in Artificial Intelligence**Fall 2019**Seminar on Mathematical Methods in Artificial Intelligence**Spring 2019**Probability Theory**Fall 2018**Topics in Statistics 2: Neural Networks and Deep Learning**Fall 2017**Stochastic Processes**Spring 2017**Seminar on Neural Networks**Fall 2016**Seminar on the Poisson Point process (in German)**Spring 2014**Spatial Statistics II (TA)**Fall 2013**Spatial Statistics I (TA)**Spring 2012**Stochastic Networks II (TA)**Fall 2011**Stochastic Networks I (TA)**Spring 2011**Markov Chains and Monte Carlo Simulation (TA)

**PhD theses**- D. Willhalm (since 05/20, University of Groningen).
*Large deviations in stochastic geometry*

- D. Willhalm (since 05/20, University of Groningen).
**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*

- L. de Jonge (since 10/20, University of Groningen).
**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*

- J. Langenbahn (12/19, University of Mannheim):