Power grids form the backbone of national and local economies, yet they are increasingly exposed to anomalies such as extreme weather events, failures of aging infrastructure, and cyberattacks. This project brings together experts in statistical machine learning and power grid engineering with the long-term goal of developing rigorous algorithms for fastest anomaly detection in power systems with guaranteed speed and identification accuracy. To achieve this goal, the project proposes a cohesive fusion of classic quickest change detection theory, recent advances in predictive and generative machine learning, and modern power grid engineering.