Information integration over time in unreliable and uncertain environments
Proceedings of the 21st international conference on World Wide Web
Modeling human behavior in the aftermath of a hypothetical improvised nuclear detonation
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Synthetic populations attempt to capture population dynamics of a geographic region and hence are widely used in large-scale multiagent applications simulating real-world phenomena. However, current synthetic populations are mostly static - individuals are assumed to perform same daily routine every day. My thesis aims at taking the first step towards making it a "live" synthetic population that would update automatically to reflect changes in the real population, by incorporating information from social media and other online data resources. As an initial step, I have extended synthetic population model for Washington DC metro area to include transient (tourists and business travelers) population. This is done by combining data from various online and offline data resources by hand. This subpopulation which keeps changing with time, has also shown to have an important effect on disease dynamics of the city. Next, I propose to use information from social media to improve activity patterns of individuals using hidden semi-Markov model.