Censoring in time-to-event data poses a challenge in survival analysis. General interval censoring places an interval of doubt around each event time. Even when none of the event times are known exactly, useful analysis techniques are available. The nonparametric maximum likelihood (NPMLE) survival function provides a natural way to demonstrate how survival progresses over time. This talk covers recent research using constrained newton methods, mixture distributions and non-negative least squares algorithms to efficiently solve the problem of finding the NPMLE survival function.