Evangelos Milios received a diploma in electrical Engineering from the National Technical University of Athens, Greece, in 1980 and Master's and Ph.D. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology, Cambridge, Massachusetts, from where he graduated in 1986. While at M.I.T., he was a member of Digital Signal Processing group and he worked on acoustic signal interpretation problems at the M.I.T. Lincoln Laboratory. After spending 5 years doing research on shape analysis and sensor-based mobile robotics in the Department of Computer Science, University of Toronto, he joined York University in 1991 as an Associate Professor. Since July of 1998 he has been with the Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, where he is Professor and Killam Chair of Computer Science. He was Director of the Graduate Program (1999-2002). He is a Senior Member of the IEEE. He served as a member of the ACM Dissertation Award committee (1990-1992), a member of the AAAI/SIGART Doctoral Consortium Committee (1997-2001) and he is co-editor in Chief of the journal Computational Intelligence. He has published on the processing, interpretation and use of visual and range signals for landmark-based navigation and map construction in single- and multiagent robotics. His current research activity is centered on modelling and mining of content and link structure of Networked Information Spaces.

AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, RG2   Info

Expert Audience

English

-- Not specified --

Saarbrücken

Modern document resources are often interlinked, and possibly
in more than one way. The citation graph is a typical example, in
which papers are linked via references and citations, and also via
co-authorship relationships. Several problems have been defined on
such ``organically grown'' networked information spaces. Modeling
such spaces has given rise to various plausible evolution models
with interesting theoretical properties, but no tools for verifying
whether the models are good approximations of the real graphs. We
are developing a rich summary representation of graphs based on
degree cores and we demonstrate that many of the models are
rather poor approximations of real graphs. In a different project,
we are developing probabilistic approaches to document co-clustering,
by extending the standard Latent Dirichlet Allocation
model to capture, not only correlation between words, but also
between topics. Current research is directed towards combining
content and link structure in clustering and co-clustering in
networked information spaces.

Gerhard Weikum

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