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Michael Jünger studied Computer Science at the University of Bonn and Stanford University (M.S. 1980) as well as Operations Research and Applied Mathematics at the University of Bonn and the University of Augsburg (Dr. rer. nat. 1983). His research and teaching are devoted to algorithms and data structures for combinatorial optimization problems. His work and the work of his research associates have had a strong relation to data science from the beginning: in automatic graph drawing, the goal is to produce visualizations of structured data that support human understanding. His working group has made many contributions ranging from theory to practical applications, and it is involved in the creation and maintainance of according software packages, most notably the OGDF open graph drawing framework (http://www.ogdf.net/doc-ogdf) [3]. A highlight is the development of the FM3 approach for efficiently visualizing large data sets, jointly with Stefan Hachul [7], and currently further developed by Martin Gronemann, another is the visualization using topographic maps, jointly with Martin Gronemann [4], as for example applied to drug discovery in molecular chemistry [2].

Application of the topographic maps approach to the visualization of the structural similarities of 256 molecules with a superimposed heat map. Highly active molecules are highlighted in red. The color gradient from red to green represents decreasing activity.

Selected publications

  1. M. Jünger, P. Mutzel, and C. Spisla, Orthogonal compaction using additional bends, in: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Volume 3: IVAPP, SciTePress, 2018, pp. 144–155.
  2. M. Gronemann, M. Jünger, N. Kriege, and P. Mutzel, The landscape metaphor for visualization of molecular similarities, Communications in Computer and Information Science 458 (2014), 85–100.
  3. M. Chimani, C. Gutwenger, M. Jünger, G. W. Klau, K. Klein, and P. Mutzel, The Open Graph Drawing Framework (OGDF), in: R. Tamassia (ed.): Handbook of Graph Drawing and Visualization, Chapman and Hall/CRC, 2013, pp. 543–569.
  4. M. Gronemann and M. Jünger, Drawing Clustered Graphs as Topographic Maps, in: W. Didimo and M. Patrignani (eds.): Graph Drawing GD 2012 Redmond, Lecture Notes in Computer Science 7704, Springer, 2013, pp. 426–438.
  5. M. Chimani, P. Hungerländer, M. Jünger, and P. Mutzel, An SDP approach to multi-level crossing minimization, in: M. Müller-Hannemann and R. Werneck (eds.) SIAM Workshop on Algorithm Engineering & Experiments 2011 (ALENEX11), SIAM, 2011, pp. 116–126.
  6. C. Buchheim, M. Chimani, D. Ebner, C. Gutwenger, M. Jünger, G.W. Klau, P. Mutzel and R. Weis- kircher, A branch-and-cut approach to the crossing number problem, in: E. Balas, A. Hoffman, and T. McCormick (eds.), Special issue of Discrete Optimization dedicated to the memory of George B. Dantzig, Discrete Optimization 5 (2008), 373–388.
  7. S. Hachul and M. Jünger, Large-graph layout algorithms at work: an experimental study, Journal of Graph Algorithms and Applications 11 (2007), 345–369.
  8. M. Jünger and P. Mutzel (eds.), Graph Drawing Software, in the series Mathematics and Visu- alization edited by G. Farin, H.-C. Hege, D. Hoffmann, C. Johnson, and K. Polthier, Springer, 2004.
  9. W. Barth, M. Jünger, and P. Mutzel, Simple and efficient bilayer cross counting, Journal of Graph Algorithms and Applications 8 (2004), 179–194.
  10. M. Jünger and S. Leipert, Level planar embedding in linear time, Journal of Graph Algorithms and Applications 6 (2002), 67–113.