Dr. Andrew E. Hong
Andrew E. Hong received his B.S. from Carnegie Mellon University and PhD from the University of Pennsylvania in statistics. He is currently a modeling & simulation engineer at the MITRE Corporation and is formerly an ORISE Fellow at the Department of Homeland Security, Science and Technology. His research interests are in the area of modeling & simulation of cyber-physical systems and the application of machine learning and optimization towards better complex engineered systems.
The increasing demand for energy and adoption of new renewable technologies pose increasing risks to the resilience of critical infrastructure systems. Further complicating matters is the increasing use of proprietary software defined controls instead of traditional physical hardware. The increased complexity of managing these systems and the lack of representative models has been at least partially blamed for recent incidents involving systems with a significant renewable component. However, the topic has sparked a great deal of research interest in advancing the state of the practice in energy systems modeling and adoption of mixed data-driven and physical models. Relatedly, there have been new developments in the analysis of these systems for greater resiliency from vulnerabilities, particularly of a cyber-physical origin. In this talk, I will describe some of the research potential unlocked by new technologies from scientific machine learning which will allow for a greater understanding of critical components and accelerate the creation of digital twins. Lastly, I will discuss key practices and assumptions made in this space that need to be overcome to allow for meaningful progress.
