Large language models (LLMs) are increasingly integrated into daily life, powering applications in education, healthcare, code generation, and more. However, these models can inadvertently memorize and reproduce sensitive or harmful content, including private user data, copyrighted material, and unsafe instructions. Retraining LLMs from scratch to remove such content is often impractical due to high cost and complexity. This project charts a new course toward more controllable, debuggable, and secure artificial intelligence (AI) through LLM unlearning, a paradigm that enables the targeted removal of harmful data influences and behaviors from pretrained models without compromising their overall performance. The research advances national priorities by promoting trustworthy AI, strengthening data privacy, ensuring safe deployment across sectors such as cybersecurity, healthcare, and education, and enabling contextually-adaptive systems aligned with a wide range of social norms. The project also offers strong educational and outreach opportunities, including curriculum development, research dissemination through workshops, tutorials, publications, and open-source software, as well as the creation of inclusive mentoring programs. This project aims to establish a comprehensive foundation for LLM unlearning by addressing challenges across four interconnected areas: optimization, model, data, and application. On the optimization front, it develops new algorithmic frameworks to en