Griffiss

Machine Learning Interpretability and Explainability (Internship)

The AFRL/RI RIGC Branch is comprised of a mathematicians, physicists, DSP engineers, software engineers, and intelligence operators. This combination of individuals allows them to tackle a wide spectrum of topics from basic research to the challenging aspects of real-time implementation of the results of research efforts. Typical summer topics may include software analysis, database development, programming, machine learning in IOT type systems and sensor data signal detection and processing. The branch interest range over topics linked to increasingly sophisticated techniques in signal detection, characterization, tracking, and classification, all with the goal of signatures via array processing. State of the art techniques such as deep neural networks and other machine learning algorithms are used to pursue solutions for increasing signal exploitation to enable warfighters with significant situational awareness and knowledge. Key to successful development and use of ML techniques is robust knowledge of ML and insights into the inner workings of these systems.