AI Engine - US Space Force Space Systems Command
Context-Aware Anomaly Detection
Description:
USSF SSC: Context-Aware Anomaly Detection Machine learning models have been widely adopted across many domains to support classification, prediction, and pattern-recognition tasks at scale. Within this landscape, data-fusion models play an increasingly important role by integrating information from multiple data sources to capture richer system dynamics than any single modality can provide.
These approaches—ranging from statistical fusion frameworks to modern graph-based and multi-modal deep learning architectures—enable more comprehensive situational understanding and improved model robustness. Building on these advances, this project aims to learn baseline patterns of activity across multiple types of data sources, such as logs, sensors, behavioral signals, and telemetry, and to explore graph-based fusion methods, multi-modal deep learning, and transfer-learning techniques to improve pattern recognition and reduce false alarms in complex systems.
Pre-requisite knowledge/skills:
- Open to sophomores through seniors
Mentors: