Advances in Network Complexity
© 2013 Wiley-VCH Verlag GmbH & Co. KGaA
Editor(s): Matthias Dehmer, Abbe Mowshowitz, Frank Emmert-Streib
Print ISBN: 9783527332915
Online ISBN: 9783527670468
About the Author
Matthias Dehmer studied mathematics at the University of Siegen, Germany, and received his PhD in computer science from the Darmstadt University of Technology, Germany. Afterwards, he was a research fellow at Vienna Bio Center, Austria, Vienna University of Technology and University of Coimbra, Portugal. Currently, he is Professor at UMIT - The Health and Life Sciences University, Austria, and is Head of the Institute for Bioinformatics and TranslationalResearch. His research interests are in bioinformatics, chemical graph theory, systems biology, complex networks, complexity, statistics and information theory. He has published extensively on network complexity and methods to analyze complex networks quantitatively.
Abbe Mowshowitz studied mathematics at the University of Chicago (BA 1961), and both mathematics and computer science at the University of Michigan (PhD 1967). He has held academic positions at the University of Toronto, The University of British Columbia, Erasmus University-Rotterdam, the University of Amsterdam and has been a professor of computer science at the City College of New York and in the PhD Program in Computer Science of the City University of New York since 1984. His research interests lie in applications of graph theory to the analysis of complex networks, and in the study of virtual organization.
Frank Emmert-Streib studied physics at the University of Siegen, Germany, gaining his PhD in theoretical physics from the University of Bremen. He was a postdoctoral research associate at the Stowers Institute for Medical Research, Kansas City, USA, and a senior fellow at the University of Washington, Seattle, USA. Currently, he is Lecturer/Assistant Professor at the Queen's University Belfast, UK, at the Center for Cancer Research and Cell Biology, heading the Computational Biology and Machine Learning Lab. His research interests are in the field of computational biology, machine learning and network medicine.