Maz Abulnaga is a PhD candidate whose work is changing the way researchers and physicians study the placenta with the goal of improving health outcomes for mothers and babies. Specifically, Maz develops algorithms for studying the placental shape in fetal magnetic resonance imaging, an imaging modality that is being used to track the health and function of the placenta, identify pathology, and support patient outcomes. Maz has made several contributions to the theoretical and algorithmic foundations of volumetric geometry processing and has successfully applied his models to clinical research of the placenta. With the support of his MathWorks Fellowship, Maz will develop geometry processing and machine-learning algorithms to quantify placental shape and functional changes throughout pregnancy, which are necessary to identify pathology and plan pregnancy outcomes. He has recently developed a novel approach to provide a standardized representation of placental shape that is being used by researchers globally. He envisions developing a set of open-source tools to enable clinical studies to improve our understanding of placental and fetal development with the goal of revolutionizing fetal-maternal health.