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This thesis proposal makes the following technical contributions: (i) weprovide a definition of the Dynamic Influence Maximization Under Uncertainty(or DIME) problem, which models the problem faced by homeless sheltersaccurately; (ii) we propose a novel Partially Observable Markov DecisionProcess (POMDP) model for solving the DIME problem; (iii) we design twoscalable POMDP algorithms (PSINET and HEALER) for solving the DIME problem,since conventional POMDP solvers fail to scale up to sizes of interest; and(iv) we test our algorithms effectiveness in the real world by conducting apilot study with actual homeless youth in Los Angeles. The success of thispilot (as explained later) shows the promise of using influence maximizationfor social good on a larger scale.

Influence Maximization for Social Good: Use of Social Networks in Low Resource Communities