Supercomputing and Computation


Ultrascale Predictive Analysis Toolbox

Problem Statement:

  • Supercomputing’s major successes to date have come from discovery through simulation rather than discovery through analysis
  • Traditional multivariate analysis is not scalable to extremes; high-dimensional data are broken into subsets that ignore system-level context
  • Can we tractably analyze ultrascale complex-systems data, learning from the system-wide interactions?

Technical Approach:

  • Use system-level interaction information to address the sample complexity problem.
  • Treat variables as tasks in a multi-task machine learning approach.
  • Provide the capability for scientists to explore ultrascale datasets in new ways.

Advantage over the State-of-the-Art:

  • Rather than being stymied by complexity, our approach takes advantage of complex interdependencies in a system to learn in ways that aren’t possible in less complex systems
  • Allows data from different time periods, different locations, and even different variables to be used together without typical fusion problems, such as data imputation for missing variables.



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