  This thesis proposal makes the following technical contributions: (i) we
provide a definition of the Dynamic Influence Maximization Under Uncertainty
(or DIME) problem, which models the problem faced by homeless shelters
accurately; (ii) we propose a novel Partially Observable Markov Decision
Process (POMDP) model for solving the DIME problem; (iii) we design two
scalable 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 a
pilot study with actual homeless youth in Los Angeles. The success of this
pilot (as explained later) shows the promise of using influence maximization
for social good on a larger scale.
