##  [MADSTARE: Modeling and Analysis for Data-Starved or Ambiguous Environments](/archives/ldrd-annual-2019/high-performance-computing/19-SI-004) 

##   *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.