For individuals with Type 1 diabetes, keeping blood sugar levels within a safe range is essential but often difficult due to daily changes in diet, stress, activity, and other factors. This project aims to improve diabetes care by creating virtual models of individual patients--called digital twins--that can learn from wearable health sensors and help guide real-time insulin delivery using automated medical devices. By combining personal health data with artificial intelligence (AI), the project seeks to reduce the burden of self-management, prevent life-threatening highs and lows in blood glucose concentration, and improve long-term health outcomes. A central innovation of this work is the use of mathematical methods to quantify and manage uncertainty in predictions and recommendations made by AI models, thereby improving the reliability of treatment decisions. These methods also contribute to federal efforts to advance science for medical devices, supporting the safe and effective deployment of AI-empowered healthcare technologies. The broader impacts of the project include reducing diabetes-related complications and healthcare costs, improving public trust in AI-powered systems, and fostering interdisciplinary education. It will create educational opportunities for students and early-career researchers at the intersection of mathematical science, computational engineering, and biomedical science, including hands-on workshops in computational medicine, offering early exposu