Data-Driven Inference & Control of Dynamic Systems

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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:

  • Machine learning for cyber physical resilience.

  • Physics-informed graph learning and inference.

  • Explainable graphical model inference.

  • 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 robust and explainable physics-informed graph neural networks for smart transportation systems under extreme conditions such as natural disasters.