Data-Driven Inference & Control of Dynamic Systems

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Electric Power Grid
Data-Driven Inference & Control of Dynamic Systems

Research Focus Area

As infrastructural systems rapidly grow in scale, complexity and interconnectedness, legacy operations driving the systems have shown to be limited in their ability to handle large-scale disasters that can occur either through natural extreme events, or through carefully orchestrated attacks, and in particular cyber-attacks. Advances in sensing and communication technologies allow diverse data collection at massive scale, granting unprecedented visibility into system operations and provide enormous potential to overhaul the operational paradigms towards increased safety, security and sustainability. It is towards this end that we are conducting  data-intensive data-driven research  for inference and control in large scale cyber physical infrastructural networks. Our research builds on four broad thrusts:

  • robust graphical model inference
  • machine learning for cyber physical resilience
  • hierarchical reinforcement learning for distributed control
  • unified data representation framework through knowledge graphs.
 

Research Focus Group Members

This is a new initiative and collaborative research group. The pilot phase will focus on problems in the smart electric grid where much of the data infrastructure is in place and the developed methodologies can be evaluated.

 

We presently have an open position in our team for a post-doctoral researcher with strong expertise in foundations of data science. Details of the position and the application process can be found here >.