[Colloq] Talk by Madhav Marathe, Virginia Tech - Wednesday, Nov. 18 at 2pm

[Rachel Kalweit]

The College of Computer and Information Science presents a Colloquium by:

Madhav  Marathe
Dept. of Computer Science and
Network Dynamics and Simulation Science Laboratory
Virginia Bioinformatics Institute at Virginia Tech

Date: Wednesday, November 18
Time: 2:00pm
Place: 366 West Village H

Title: Computational Network and Social Science: Implications for  Public Policy

Abstract:
Complex Networks are pervasive in our society.  Realistic biological,
information, social and technical networks share a number of unique
features that distinguish them from physical networks.  Examples of
such features include: irregularity, time-varying structure,
heterogeneity among individual components and selfish/cooperative
game-like behavior by individual components.  Furthermore, the network
structure, the dynamical process on the network and the behavior of
constituent agents co-evolve over time.  The size and heterogeneity of
these networks, their co-evolving nature and the technical
difficulties in applying dimension reduction techniques commonly used
to analyze physical systems makes the task to understanding and reasoning
about these networks even more challenging.

Recent quantitative changes in high performance and pervasive computing
including faster machines, distributed sensors and
service-oriented software have created new opportunities
for collecting, integrating,
analyzing and accessing information related to such large complex
networks.  The advances in network and information science that build
on this new capability provide entirely new ways for reasoning and
controlling these networks. Together, they enhance our ability to formulate,
analyze and realize novel public policies pertaining to these complex
networks.

Over the last 15 years, our group has established a theory based
program for modeling, simulation and associated decision support tools
for understanding large complex network. Complementing this is a program to develop
a scalable service delivery framework, that
provides policy analysts and scientists seamless access to the modeling
environment. After a brief overview, I will describe our approach within the
context of a specific application: development of modeling and
decision support environments to study epidemics in co-evolving
social and wireless networks. Understanding these
epidemiological processes is of immense societal
importance. Additionally they serve as excellent "model organisms" for
developing a theory of co-evolving complex networks. Individual and
collective behavioral adaptation is critical in these systems
and will be highlighted via illustrative case studies.