Posted: 02-06-2024

Location: U.S. Army Combat Capabilities Development Command-Aberdeen Proving Ground

Level: Graduate Student

General Topic: Computer Science    

Description of Research: Advances in AI/ML over the last 10 years have led to superhuman performance of classifiers operating on image and video streams. More recent advances in unsupervised training for foundation models such as large language models have led to impressive zero-shot learning results. Neuro-symbolic approaches provide mechanisms to combine prior knowledge as rules with data-driven learning enabling effective performance using small training datasets. While much research in these areas have focused on computer vision, less attention has been devoted to less power hungry sensing assets such as acoustic and seismic sensors. This project intends to incorporate prior knowledge such as physical constraints into data-driven learning, possibly leveraging foundation models to develop better performing acoustic and/or seismic target detectors/classifiers.

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