For information contact: email@example.com
Past research has shown that publicly available data can accurately forecast societal events such as civil unrest and disease outbreaks. For example, DOD’s Integrated Crisis Early Warning System (ICEWS) and IARPA’s Open Source Indicators (OSI) have developed methods to forecast societal events using structural data, news feeds, blogs, web search queries and other publicly available data. However, in many cases, relevant data have significant lag times, lack accuracy or are classified. There has been little research to examine whether classified data from foreign Signals Intelligence (SIGINT) can be used to forecast events with high accuracy and lead-time. The Mercury program aims to fill this gap by developing methods for continuous, automated analysis of foreign SIGINT data to anticipate and/or detect significant events, including military and terrorist activities, political crises and disease outbreaks in Arabic-speaking countries in the Middle East and North Africa.
The Mercury program seeks to develop methods for continuous, automated analysis of SIGINT in order to anticipate and/or detect political crises, disease outbreaks, terrorist activity, and military actions. Anticipated innovations include: development of empirically driven sociological models for population-level behavior change in anticipation of, and response to, these events; processing and analysis of streaming data that represent those population behavior changes; development of data extraction techniques that focus on volume, rather than depth, by identifying shallow features of streaming SIGINT data that correlate with events; and development of models to generate probabilistic forecasts of future events. Successful proposers will combine cutting-edge research with the ability to develop robust forecasting capabilities from SIGINT data.
- SIGINT analytics
- Event forecasting
- Machine learning
- Streaming data
- Data fusion
- Disease outbreak
To access Mercury program-related publications, please visit Google Scholar.