Cellular and systems biology, statistical inference, single-cell genomics

We are broadly interested in a molecular description of cellular diversity in complex tissues, its developmental and evolutionary origins, and its biological consequences for tissue function and degeneration. We use high-throughput single-cell genomic measurements to understand cellular diversity in the mouse retina and other systems, and explore their conservation in other vertebrates, including humans and non-human primates. A major focus is to develop computational approaches rooted in machine learning to make precise biological inferences that can not only guide experiments, but also reveal general mechanistic principles underlying cellular organization and molecular networks.