The immune system protects the body by recognizing abnormal cells and infectious threats, yet the molecular rules that allow immune cells to distinguish dangerous targets from healthy cells remain poorly understood. This project will develop new computational approaches to better understand immune recognition, a fundamental problem in biology with broad relevance to health, cancer immunotherapy, autoimmunity, and future biomedical discovery. The project will also advance computing by developing new methods to model biological systems using structure-aware protein language models. In addition to the research activities, the project will support a month-long summer program in New York City that introduces high school students to immunology, data science, and machine learning through hands-on projects and mentorship. Openly shared software, educational materials, and datasets will help broaden access to computational biology and strengthen the future scientific workforce. This project will develop structure-aware protein language models to decode how T cell receptors recognize peptide antigens presented by major histocompatibility complex molecules. The research will generate high-confidence three-dimensional models of paired T cell receptors, represent local structural environments as symbolic tokens, and integrate sequence, structure, and spatial information in a transformer architecture trained with masked multimodal prediction and contrastive learning. The resulting repre