Dynamic processes, such as rumor spreading in social networks, occurrence of crimes in a city, migration of birds across continents, generate a large volume of high dimensional “asynchronous” and “interdependent” temporally and spatially stamped event data.  This type of event data is rather different from traditional iid. data and discrete-time temporal data, which calls for new models and scalable algorithms for analyzing, learning and utilizing them. In this talk, I will present a framework based on multivariate point processes, high dimensional sparse recovery, and randomized algorithms for addressing a sequence of problems arising from this context.  As a concrete example, I will also present experimental results on learning and optimizing information diffusion in web logs, including estimating hidden diffusion networks and influence maximization with the learned networks. With both careful model and algorithm design, the framework is able to handle millions of events and millions of networked entities, and achieve the state-of-the-art results.