This project investigates fundamental techniques for building mathematical models that can be safely used to make trustworthy predictions and control decisions. Mathematical models form the foundation for modern Cyber-Physical Systems (CPS). Examples include vehicle models that predict how a car will move when brakes are applied, or physiological models that predict how the blood glucose levels change in a patient with type-1 diabetes when insulin is administered. The success of machine learning tools has yielded data-driven models such as neural networks. However, depending on how data is collected and the models are learned, it is possible to obtain models that violate fundamental physical, chemical, or physiological facts that can potentially threaten life and property. The approach of the project is to expose these model flaws through advanced analysis. The project seeks to broaden participation in computing through mentoring activities that will encourage undergraduate women and members of underrepresented minority groups to consider a career in research.
The research combines falsification methods for exposing failure to conform with verification approaches for rigorously proving conformance. Furthermore, approaches for learning models of dynamical systems from data and imposing core cyber-physical domain knowledge are under investigation. The project is applying these data-driven models with conformance guarantees to the design of safe controllers for autonomous vehicles, models of human insulin glucose regulation and robotic swarms. The effort is advancing CPS education by creating a framework for distance education focused on CPS. The researchers are developing a series of low cost hardware testbeds and self-paced learning tasks that will expose students to the process of building highly reliable and safety critical CPS.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
