Meidani wins NSF CAREER Award
By Celeste Arbogast
CEE Assistant Professor Hadi Meidani has been awarded a National Science Foundation (NSF) CAREER award to develop faster, more accurate methods for modeling smart infrastructure systems.
Administered under the Faculty Early Career Development Program, CAREER awards are the NSF’s most prestigious form of support and recognition for junior faculty who “exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations.”
The size and complexity of infrastructure systems have traditionally made them cumbersome to model, but advances in computing and sensing technologies have opened up new possibilities, Meidani said. His project aims to streamline the modeling of infrastructure systems so that the complex responses and inherent uncertainties of these large systems can be accurately predicted. One facet of his work involves the incorporation of stochastic simulations and predictive analytics that enable optimal management of future smart cities.
“The main challenge that still prevents stochastic simulations from being widely used for civil infrastructure systems is their high computational cost due to large size of infrastructure networks,” Meidani said.
Meidani’s CAREER project will develop faster uncertainty quantification (UQ) methodologies that can reduce simulation time and are particularly tailored for infrastructure networks. The analysis and optimization of smart infrastructures for autonomous vehicles as well as interdependent transportation-energy systems under the integration of electric vehicles will be considered.
“Healthy and optimal operation of infrastructure systems has immediate implication on the well-being of society,” Meidani said.
Meidani joined the faculty in CEE at Illinois in 2014. His primary research areas include uncertainty quantification, probabilistic machine learning, optimization and control under uncertainty, and predictive analytics for smart urban systems.