Keynote Speakers


George KESIDIS, Professor of Electrical Engineering and Computer Science and Engineering, The Pennsylvania State University.

Title: Epidemiology of the spread of virus/worms in the Internet

Abstract: We begin with a brief overview of susceptible/infective/removal(death) modeling. The rapid spread of the 2003 Slammer scanning worm, impeded only by congestion in the access networks, will be empirically characterized and then modeled by a deterministic "stratified" SIR model (and its stochastic counterpart).  How other scanning worms qualitatively differed from Slammer will also be discussed. We will then describe the covert and multiple-vectored nature of the spread of current botnets in the Internet.

"White" worms have been proposed to rapidly deploy patches and eliminate vulnerabilities; there is risk associated with white worms in that the patches may not have been adequately tested for compatibility and white worms require authentication overhead. In this context, we will (a) present a recent model of white worm performance on an general graph and (b) discuss the trade-off between the risks of inoculation and disease through a simple game.

Finally, we will discuss the recent explanation for how susceptibility sparseness, due to a plurality of host operating systems,  impedes the spread of viruses in cellular phone systems.

References (informal):
Kesidis et al., "SQL Slammer worm", ACM TOMACS, 2008.
T. Reluga et al., "Trading off the risks of innoculation and disease".
C. Griffin, "White worms on general graphs".
Barabasi et al., "Virus spread among cellular phones", Science, 2009.


Vivek S. BORKAR, Professor, School of Technology and Computer Science, Tata Institute of Fundamental Research, INDIA.

Title: A variation of ant colony optimization for network problems

Abstract: This talk briefly surveys a recent variation of the ant colony optimization algorithm for single and multi-stage shortest path problems, and sketches an extension to the traveling salesman problem. It also proposes some related issues for future research.


Wolfgang BANZHAF, Professor and Head Department of Computer Science, Memorial University of Newfoundland, CANADA.


Title : The Science and Engineering of Complex Systems

Keynote abstract: Complex Systems have taken center stage in the Sciences. Recently, engineers have found interest in core concepts of complex systems research. The interconnectedness of components, the emergence and self-organization of functions, and their existence far from equilibrium make them of primary interest for both explanation of phenomena and for their application. Networks are a primary example of this trend of joint scientific and technological developments. Our talk will highlight recent progress in biologically-inspired information processing, notably derived from the area of genetic regulatory networks.


Nicolas CHAMPAGNAT, Chargé de Recherche INRIA Sophia Antipolis - Méditerranée


Title: From individual-based stochastic processes to macroscopic models in adaptive evolution

Keynote abstract: This is a joint work with Régis Ferrière (ENS, Paris) and Sylvie Méléard (Ecole Polytechnique, Palaiseau).
We are interested in modelling Darwinian evolution, resulting from the interplay of phenotypic variation and natural selection through ecological interactions. Our models are rooted in the individual-based, stochastic description of a population of individuals characterized by one or several adaptive traits. The population is modelled as a stochastic process, describing the dynamics over continuous time of birth, mutation, death and interactions between individuals, as influenced by each individual's trait values. An offspring inherits the trait values of its progenitor, except when a mutation gives a new trait value at birth. We consider several large population approximations, by combining various scalings of population size, birth and death rates, mutation rate, mutation step, or time. A single individual-based model is shown to lead to contrasting macroscopic limits, of different nature: deterministic, in the form of ordinary, integro-, or partial differential equations, or probabilistic, like stochastic partial differential equations or superprocesses. In the limit of rare mutations, we also obtain a stochastic jump process, allowing us to justify rigorously the phenomenon of evolutionary branching, where a population, initially concentrated around a single trait value, is led by selection to population states concentrated around two (or more) distinct traits values.