Detecting, anticipating, and reasoning potential proliferation expertise and capabilities using domain knowledge extracted from publicly available data is a highly desired task that supports the nuclear nonproliferation mission. Existing research efforts rely on graph analytics and are reactive in nature; they primarily focus on co-citation network analysis of scientific literature in English. In comparison, our effort supports moving away from reactive analyses to take a proactive posture by:
EXPERT is developing and deploying a series of usable artificial intelligence-driven analytics to enable descriptive, predictive, and prescriptive inferences used to transform nuclear nonproliferation monitoring globally.
Our transformational, artificial intelligence-driven approach aims not only to provide deeper understanding of how publicly available data could be used to detect, monitor, forecast, and potentially prevent proliferation, but also to discover real-world examples of patterns and behavior to facilitate the investigation of potentially illicit proliferation activity (e.g., before and after the Joint Comprehensive Plan of Action).
This project is a collaboration with the University of Washington and Lawrence Berkeley National Laboratory.