This project aims to develop innovative methodologies for quantifying and controlling rare events in complex systems using digital twins. Rare events, such as traffic crashes, power grid failures, and extreme weather, have severe consequences despite their low frequency. Digital twins, virtual replicas of physical systems, update dynamically with real-time data, providing predictive insights and decision-making capabilities for rare event mitigation. However, current digital twin models often fail to account for rare events, leading to substantial risks in real-world applications. This project addresses this gap by creating RareDT, digital twins that explicitly incorporate rare event quantification and control. This project combines foundational advances in mathematics and statistics with the development of efficient algorithms to quantify the uncertainty of rare events and optimize decision-making in digital twins. The methodologies will be applied and validated in the context of autonomous vehicle traffic control, with the broader goal of enhancing safety and resilience in areas such as transportation, infrastructure planning, and disaster response. By ensuring that AI-enabled digital twin technologies are both reliable and robust in extreme scenarios, the project contributes to national safety and prosperity. The project also includes a strong educational component, offering new courses, workshops, and outreach activities for learners from K-12 through graduate school, and