Generalized low-rank matrix recovery is a fundamental statistical tool that provides a unifying framework for important data science problems such as Euclidean distance geometry, robust principal component analysis, and matrix completion. It arises in applications such as sensor localization, molecular conformation, ultrasound imaging, video processing, and hyperspectral imaging. In these applications, data are often incomplete, corrupted by outliers, and governed by inherent geometrical pattern