We develop algorithms for both detection and decision support in nuclear threat identification. Using our flagship Bayesian Aggregation method for source detection and characterization we are developing fast and efficient tools for situational awareness and safety applications. Our work focuses on robust methods, multi-sensor and multi-modal data fusion, and decision support infrastructure for rapidly processing alerts.
Enhanced Radiological and Nuclear Inspection and Evaluation (ERNIE) uses AI/ML for threat detection and characterization using radiation signatures measured on vehicles coming into the U.S. through ports of entry for the U.S. Customs and Border Protection (CBP), U.S. Department of Homeland Security (DHS). In the ERNIE system, AI improves threat detection and significantly reduces the need for manual inspections of incoming cargo for potential radiological threat.