MADSTARE: Modeling and Analysis for Data-Starved or Ambiguous Environments

Michael Schneider | 19-SI-004

Executive Summary

By integrating probabilistic statistical modeling with deep (machine) learning, we will design, build, and test a new data analysis capability for space situational awareness. This capability and its associated quantum-scalable computational architecture will enable the exploitation of small or ambiguous data and support accuracy and uncertainty quantification, with applications in national security and other fields with large data problems.

Publications, Presentations, and Patents

Goumiri, I. R., et al. 2020. "Star-Galaxy Separation via Gaussian Processes with Model Reduction." 21st Advanced Maui Optical and Space Surveillance Technologies Conference (online), September 2020. LLNL-CONF-813954