The METHODS (Machine learning Enabled Two-pHase flow metrologies, models, and Optimized DesignS) program brings together a team of experts from five universities in a multidisciplinary convergence of liquid-vapor phase change transport modeling domain knowledge, advanced fluid physics metrology, and emerging physics-informed (PhI) machine learning (ML) and computer vision techniques. Funded by the Office of Naval Research (ONR) Multidisciplinary University Research Initiative (MURI), the METHODS MURI seeks to leverage PhI ML approaches to enhance fundamental understanding and develop an end-to-end framework (comprising metrology development, data interpretation, model discovery and training) to enable generalized prediction of phase change during two-phase flows.