Modern use of data relies heavily on predictive modeling. Machine learning methods are needed to distill large, heterogeneous, and fragmented data sources into useful pieces of information such as answers to search queries, purchasing patterns of customers, or likely actions of mobile users. This research focuses on predicting the behavior of mobile users -- actions they are likely to take in any particular context -- based on a collection of intermittent sensors such as GPS, wifi, accelerometer, and others. Our goal is to develop methods that will be useful more broadly. Our work addresses the following key problems: 1) scaling to realistic problem sizes, 2) robustness, and 3) maintaining privacy even as data are used collaboratively.