We use theoretical and computational approaches based on statistical physics, information theory, statistics, and computational geometry to develop data-driven and mechanistic models to uncover mechanistic principles underlying our immune responses pertaining to viral infection and cancer immunotherapy. Our approach marks synergistic integration of data from single cell and imaging experiments with theoretical and computational approaches leading to generation of models with predictive powers and mechanistic insights.