We use applied statistics, machine learning and efficient algorithms to address fundamental problems in biology and medicine. We develop computational methods for better design and integrative analysis of high-throughput functional genomic assays and perturbation experiments, with a focus on multi-modal single-cell technologies. We apply these methods to study regulatory genomics of cell function and cell-cell interactions *in vivo* in close collaboration with experimental biologists, with a focus on immunology and cancer.

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