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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:

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 Full Professor of Computer Science at Humboldt-Universität zu Berlin since August 2018. Prior to that, he was Full Professor at University of Cologne and Assistant Professor at Karlsruhe Institute of Technology. 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 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


A nearly complete and reasonably up-to-date list of my publications can be found at DBLP.

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

  • C. Tzovas, M. Predari, H. Meyerhenke: Distributing Sparse Matrix/Graph Applications in Heterogeneous Clusters - an Experimental Study. In Proc. 27th IEEE Intl. Conf. on High Performance Computing, Data, and Analytics (HiPC 2020). To appear.

  • A. van der Grinten, E. Angriman, H. Meyerhenke: Scaling up Network Centrality Computations ‐ a Brief Overview. In it - Information Technology. De Gruyter, 2020. [Online version]

  • M. Simsek, H. Meyerhenke: Combined Centrality Measures for an Improved Characterization of Influence Spread in Social Networks. J. of Complex Networks 8(1), Feb 2020, Oxford University Press. [DOI: 10.1093/comnet/cnz048]