Armita Nourmohammad Research Group

Yale University

How do biological systems learn and evolve molecular programs that produce adaptive immune responses? The PhABLE group develops physics-inspired machine learning models to map the immune recognition shape space for antigen–receptor interactions. Researchers integrate information theory and statistical physics to uncover dynamic landscapes of immune decision-making in health and disease. Projects apply computational models and data analysis to predict adaptive immune responses and guide immune engineering strategies. The lab combines ML, control theory, and quantitative modeling to design interpretable models of T and B cell receptor specificity and evolution.