Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Modellierung und Analyse komplexer Systeme

Prof. Dr. Henning Meyerhenke


Henning Meyerhenke

Adresse / Mail Address:

Humboldt-Universität zu Berlin
Institut für Informatik
Unter den Linden 6
D-10099 Berlin

Sitz / Visitor Address:

Johann-von-Neumann-Haus
Rudower Chausse 25, Raum III.303
D-12489 Berlin-Adlershof

Telefon / Phone:

+49 30 2093 41220

E-Mail / Email:

meyerhenke (at) hu-berlin.de


Brief Curriculum Vitae

Henning Meyerhenke is Professor of Computer Science at Humboldt-Universität zu Berlin since August 2018. Prior to that, he was Professor at University of Cologne and Assistant Professor at Karlsruhe Institute of Technology, respectively. Henning held postdoctoral positions at Georgia Institute of Technology (Atlanta, USA), NEC Laboratories Europe, and University of Paderborn. He received his Diplom degree in Computer Science from Friedrich-Schiller-University Jena in 2004 and his Ph.D. (with highest distinction) in Computer Science from the University of Paderborn in 2008.

Since his time in Karlsruhe, Henning has acquired significant funding from DFG, BMBF, and MWK Baden-Wuerttemberg. Together with his co-authors, he received the Best Algorithms Paper Award at the 22nd IEEE International Parallel and Distributed Processing Symposium (2008) and the Best Paper Award of the 2015 International Symposium on Foundations and Applications of Big Data Analytics.

Research Interests

Henning Meyerhenke's main research interests concern scalable algorithms for large and complex networked systems, in particular for three application areas:

  • Algorithmic analysis of large complex networks
  • Combinatorial scientific computing
  • Applied optimization for algorithmic problems in the natural sciences

Publications

A nearly complete and reasonably up-to-date list of my publications can be found at DBLP. We also maintain a list of our publications.

Papers recently accepted but not yet in the DBLP list include:

  • Ü.V. Çatalyürek, K.D. Devine, M. Fonseca Faraj, L. Gottesbüren, T. Heuer, H. Meyerhenke, P. Sanders, S. Schlag, C. Schulz, D. Seemaier, D. Wagner: More Recent Advances in (Hyper)Graph Partitioning. Accepted by ACM Computing Surveys, November 2022.
  • M. Predari, R. Kooij, H. Meyerhenke: Faster Greedy Optimization of Resistance-based Graph Robustness. Accepted by ASONAM 2022.
  • Z. Su, J. Kurths, H. Meyerhenke: Network Sparsification via Degree- and Subgraph-based Edge Sampling. Accepted by ASONAM 2022.
  • A. van der Grinten, G. Custers, D. Le Thanh, H. Meyerhenke: An MPI-Parallel Algorithm for Static and Dynamic Top-k Harmonic Centrality. Accepted by SBAC-PAD 2022.
  • S. Kulagina, A. Benoit, H. Meyerhenke: Mapping Tree-shaped Workflows on Memory-heterogeneous Architectures. Accepted by 20th Intl. Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous (HeteroPar), co-located with Euro-Par 2022, July 2022. Preliminary version available at HAL.
  • Z. Su, H. Meyerhenke, J. Kurths: The climatic interdependence of extreme-rainfall events around the globe. Accepted by Chaos: An Interdisciplinary Journal of Nonlinear Science, AIP (American Institute of Physics) Publishing, March 2022. To appear. Preliminary version available at arXiv.
  • E. Angriman, E. Bergamini, P. Bisenius, H. Meyerhenke: Computing Top-k Closeness Centrality in Fully-dynamic Graphs. In Massive Graph Analytics. Taylor & Francis. To appear. Conference version appeared at ALENEX'18.
  • M. Wolter, M. von Looz, H. Meyerhenke, C. Jacob: Systematic partitioning of proteins for quantum-chemical fragmentation methods using graph algorithms. Journal of Chemical Theory and Computation. To appear.
    [DOI: 10.1021/acs.jctc.0c01054]