A number of phenomena of societal importance, such as the spread of diseases and contagion processes, can be modeled by stochastic processes on networks. The analysis and control of such network phenomena involve, at their heart, fundamental graph-theoretic problems. The graphs encountered are typically of large-scale (having tens of millions of nodes); further, typical experimental analyses involve large designs with a number of parameters, leading to hundreds of thousands of graph computations. Novel methods for solving these problems are needed, since fast response times are critical to effective decision making.
The overarching goal of this project is to develop efficient distributed algorithms and associated lower bounds for graph-theoretic problems that arise in computational epidemiology and contagion dynamics. This will have a significant impact on these specific applications, through more efficient algorithmic tools for enabling complex analyses.
This is a collaborative project with Prof. Gopal Pandurangan (University of Houston) and Prof. Anil Vullikanti (Virginia Tech).