The University of Connecticut (UConn) received a $397,111 National Science Foundation (NSF) CAREER grant (CFDA 47.041 Engineering) to develop a new robust power system dynamic estimation paradigm to enhance situational awareness, improve dynamic model quality, and enable secure power system operation with high penetration of inverter-based resources like solar and wind. The 5-year project aims to bring transformative changes to the use of measurements for dynamic state estimation, model deficiency diagnosis, and measurement configuration in electric power grids. The research will focus on creating innovative derivative-free observability theories, integrating Bayesian inference with robust estimation for nonlinear dynamic estimation, and designing observability-informed parameter estimation and uncertainty quantification algorithms to continuously refine power system dynamic models. This research will support the development of the next generation of robust dynamic estimation tools for inverter-dominated power systems to ensure grid reliability and security.