Modeling Asynchronous Event Dynamics

In this talk we discuss the development of machine learning and data mining methodology and algorithms for modeling, learning and control of temporal marked point processes. We are especially interested in understanding and modeling of how the occurrences of a specific type of events at present and future depend on the occurrences of events of the same and other types happened in the past, and how this dynamic dependency exhibits heterogeneity across a population and across time. Ultimately, we want to leverage our knowledge of the dynamic properties of temporal marked point processes to manipulate and control their time evolution in order to achieve more desirable outcomes. We will also discuss applications of the methodology and algorithms in social networks, health informatics and Web search.