Prof. Dr. Tobias Friedrich

Marcus Pappik

Chair for Algorithm Engineering
Hasso Plattner Institute

Office: A-1.7/8
E-Mail: Marcus.Pappik(at)hpi.de

Research Interests

My research interests include a variety of topics such as data science, computational statistics (especially causal inference), game theory and stochastic processes. Currently, I am focusing on the application of Markov chains to discrete and continuous systems from statistical physics and the resulting algorithmic applications.

A variety of connections between theoretical computer science and statistical physics has been investigated within recent years. Some of the most remarkable results relate phase transitions of physical systems with the traceability of computational problems. The ongoing effort to connect those scientific branches leads to a two-way exchange: tools from statistical physics are used to explain computational properties of various algorithmic problems, and results from theoretical computer science are used to investigate phenomena from statistical physics. Probabilistic properties, such as spatial decay of correlations and rapid mixing of certain Markov chains, seem to be at the very core of these connections.


During my masters and Ph.D. studies, I was Teaching Assistant for the following courses:


In December 2019 I presented the publication 'Convergence and Hardness of Strategic Schelling Segregation' during WINE 2019 at the Columbia University, New York City. Slides and a recording (at 02:05:00) are available online.


[ 2019 ] [ 2018 ] [ 2017 ]

2019 [ nach oben ]

  • Convergence and Hardness ... - Download
    Echzell, Hagen; Friedrich, Tobias; Lenzner, Pascal; Molitor, Louise; Pappik, Marcus; Schöne, Friedrich; Sommer, Fabian; Stangl, DavidConvergence and Hardness of Strategic Schelling Segregation. Web and Internet Economics (WINE) 2019: 156-170

2018 [ nach oben ]

  • Kumar Shekar, Arvind; Pappik, Marcus; Iglesias Sánchez, Patricia; Müller, EmmanuelSelection of Relevant and Non-Redundant Multivariate Ordinal Patterns for Time Series Classification. Discovery Science (DS) 2018: 224-240

2017 [ nach oben ]

  • Framework for Exploring a... - Download
    Kirsch, Louis; Riekenbrauck, Niklas; Thevessen, Daniel; Pappik, Marcus; Stebner, Axel; Kunze, Julius; Meissner, Alexander; Kumar Shekar, Arvind; Müller, EmmanuelFramework for Exploring and Understanding Multivariate Correlations. Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) 2017: 404-408


[ 2020 ]

2020 [ nach oben ]

  • New Conditions via Markov... - Download
    Pappik, MarcusNew Conditions via Markov Chains: Approximating Partition Functions of Abstract Polymer Models without Cluster Expansion. master's thesis, Hasso Plattner Institute 2020