Prof. Dr. Tobias Friedrich

Marcus Pappik

Chair for Algorithm Engineering
Hasso Plattner Institute

Office: K-2.19/20
Tel.: +49 331 5509-424
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:

Moreover, I was co-supervising the following projects and theses:

Invited Talks

  • talk on 'Discretization-based algorithms for repulsive Gibbs point processes' at the Dagstuhl Seminar on Counting and Sampling: Algorithms and Complexity 2022
  • presentation of our publication 'Algorithms for hard-constraint point processes via discretization' at COCOON 2022 (online)
  • presentation of our publication 'A spectral independence view on hard spheres via block dynamics' at ICALP 2021 (online); recording available online
  • presentation of our publication 'Convergence and Hardness of Strategic Schelling Segregation' at WINE 2019; slides and recording (at 02:05:00) available online


[ 2023 ] [ 2022 ] [ 2021 ] [ 2019 ] [ 2018 ] [ 2017 ]

2023 [ nach oben ]

  • Polymer Dynamics via Cliq... - Download
    Friedrich, Tobias; Göbel, Andreas; Krejca, Martin S.; Pappik, Marcus Polymer Dynamics via Cliques: New Conditions for ApproximationsTheoretical Computer Science 2023

2022 [ nach oben ]

  • A Spectral Independence V... - Download
    Friedrich, Tobias; Göbel, Andreas; Krejca, Martin S.; Pappik, Marcus A Spectral Independence View on Hard Spheres via Block DynamicsSIAM Journal on Discrete Mathematics 2022: 2282–2322
  • Algorithms for hard-const... - Download
    Friedrich, Tobias; Göbel, Andreas; Katzmann, Maximilian; Krejca, Martin S.; Pappik, Marcus Algorithms for hard-constraint point processes via discretizationInternational Computing and Combinatorics Conference (COCOON) 2022
  • Analysis of a Gray-Box Op... - Download
    Baguley, Samuel; Friedrich, Tobias; Timo, Kötzing; Li, Xiaoyue; Pappik, Marcus; Zeif, Ziena Analysis of a Gray-Box Operator for Vertex CoverGenetic and Evolutionary Computation Conference (GECCO) 2022: 1363–1371

2021 [ nach oben ]

  • A spectral independence v... - Download
    Friedrich, Tobias; Göbel, Andreas; Krejca, Martin S.; Pappik, Marcus A spectral independence view on hard spheres via block dynamicsInternational Colloquium on Automata, Languages and Programming (ICALP) 2021: 66:1–66:15

2019 [ nach oben ]

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

2018 [ nach oben ]

  • Kumar Shekar, Arvind; Pappik, Marcus; Iglesias Sánchez, Patricia; Müller, Emmanuel Selection of Relevant and Non-Redundant Multivariate Ordinal Patterns for Time Series ClassificationDiscovery 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, Emmanuel Framework for Exploring and Understanding Multivariate CorrelationsMachine Learning and Knowledge Discovery in Databases (ECML/PKDD) 2017: 404–408


[ 2020 ]

2020 [ nach oben ]

  • New Conditions via Markov... - Download
    Pappik, Marcus New Conditions via Markov Chains: Approximating Partition Functions of Abstract Polymer Models without Cluster Expansionmaster’s thesis, Hasso Plattner Institute 2020