Real-world events can often be viewed as salient outcomes of interactions between sets of latent spatiotemporal processes that reflect behaviors of individuals and of populations, along with the evolution of environmental factors. We seek to develop advanced, automated methods of consolidating the vast amount of available data into predictions of meaningful events. This involves integrating information from a range of sources, modeling their interactions over time, and making inferences about significant patterns and/or anomalies.

Two examples of projects in this domain include SAFE, where we are developing a suite of methods for forecasting geopolitical events, and EFFECT, where we are developing an end-to-end system for detecting cyber security vulnerabilities and predicting the timing and targets of attacks.





Postdoctoral Associates


PhD Students

Nazanin AlipourFard

Adam Badawy

Palash Goyal

Nazgol Tavabi