Our research is at the interface of Computer Science, Physics, and Biology, focusing on the representation, inference and design of multicellular systems. We develop computational frameworks, based on ideas rooted in dynamical systems theory and machine learning to better understand how cells encode multiple layers of spatial and temporal information, and how to efficiently decode that information from single-cell data. We aim to uncover organization principles underlying information processing, division of labor, collective cellular function, and self-organization of multicellular structures.