Dr. Javidi understands the importance of data, especially its time sensitivity.  A delay in data transmission in a large cyber-physical system, such as a data center or smart energy grid, can result in the loss of millions for companies and the missed opportunity to discover vital knowledge via real-time data analytics. 

A cyber-physical system includes computing technologies that are responsible for the collection, processing, and acquisition of information, which is synthesized with a physical system. Thus in the case of a smart energy grid, the"cyber" elements include the computing technologies which measure energy usage, and the "physical" system is the energy grid itself which controls the output of energy, based on commands from the "cyber" system.  Given that large cyber-physical systems have so many sensors, collecting all possible data typically results in a glut of information. This may be inefficient and overwhelming in terms of processing, and it also makes the data collection excessively expensive and resource-intensive.  

• Dr. Javidi is developing a new theoretical framework for understanding how to best control information flow in large cyber-physical systems such as data centers or smart energy grids.

• Dr. Javidi is conducting experiments on large electricity grids to predict failures (small or cascading) or even typical aspects of the network operation such as what time of day is best for doing certain energy-rich tasks.

• In particular, she is investigating which pieces of data gathered by cyber-physical systems are the most informative, but also which pieces of information can add reliability to such a prediction.

Dr. Javidi's new theoretical framework for understanding how to best control information flow in large cyber-physical systems has the potential to save millions in costs and to take data analytics to a whole new level of understanding, which can lead to groundbreaking new insights.