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Researcher Profile

Michael Barber Ph.D.

 Michael Barber Ph.D.




Innovation Systems & Policy

Competence Unit

Innovation Systems & Policy




+43 50550 4504


+43 50550 4599


Giefinggasse 4, 1210 Vienna


Field: Innovation Dynamics and Modelling

Main Research: complex networks, geography of innovation, agent based models

Experience: Michael Barber has a B.S. in physics from Michigan Technological University and a Ph.D. in physics from Washington University (Dissertation: Studies in Neural Networks: Neural Belief Networks and Synapse Elimination). In 2006, he began his present position as Scientist at the AIT innovation Systems Department. Previously, he was a researcher at the Center for Mathematical Sciences of the University of Madeira and a postdoctoral researcher at the Institute for Theoretical Physics of the University of Cologne. His research interests include complex systems and networks, machine learning, structure and function of R&D networks, geography of collaborations, neural networks, and statistical physics. He is highly skilled in the application of analytical and computation methods to the investigation of complex systems and networks.

  • 2006-now: Scientist
    Austrian Institute of Technology / Austria
  • 2001-2006: Researcher
    Center for Mathematical Sciences, University of Madeira / Portugal
  • 1999-2001: Postdoctoral researcher
    Institute for Theoretical Physics, University of Cologne / Germany
  • 1995-1999: Research Assistant
    Department of Physics, Washington University / USA
  • 1993-1996: Teaching assistant
    Department of Physics, Washington University / USA
  • 2010: Martin Beckmann Award http://ersa.org/home/article/2010-martin-beckmann-award


  • Network of European Union–funded collaborative research and development projects
    Author(s): M.J. Barber, A. Krueger, T. Krueger, and T. Roediger-Schluga
    Journal: Physical Review E, 73(3) (2006) ; 036132
  • Modularity and community detection in bipartite networks
    Author(s): M.J. Barber
    Journal: Physical Review E, 76(6) (2007) ; 066102
  • Detecting network communities by propagating labels under constraints
    Author(s): M.J. Barber and J.W. Clark
    Journal: Physical Review E, 80(2) (2009) ; 026129
  • Spatial interaction modelling of cross-region R&D collaborations: empirical evidence from the 5th EU framework programme
    Author(s): T. Scherngell and M.J. Barber
    Journal: Papers in Regional Science, 88(3) (2009) ; 531-546
  • Distinct spatial characteristics of industrial and public research collaborations: evidence from the fifth EU Framework Programme
    Author(s): T. Scherngell and M.J. Barber
    Journal: The Annals of Regional Science, 46(2) (2011) ; 247-266

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