Event Prognosis, Asset Reliability and Maintenance

This research direction aims at developing advanced methodologies for event data using IoT sensor data. An "event" marks a random elicitation in the time domain.  In the context of machines, systems, and production equipment -- an event can be a warning, a failure, a maintenance activity, etc. Thus, one can easily find a system (or a fleet of systems) to experience a multitude of events during its operational lifetime.  Some of the broad research goals under this direction are:

Domain-aware Active Learning

We create methods to synergistically combine domain knowledge, (i.e., biology and physics) with real-world experiments to develop next-gen bioprinted liver tissue models.

In collaboration with: Dr. Srikanthan Ramesh, Dr. Sundar Madihally.