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Computing to decode single-cell semantics

Cells in our body, work in clans. Our recently developed ability to study them in silo presents us with a learning singularity. Such uninhibited learning happens only when we can effortlessly see the bewildering amount of information captured in single-cell nucleic acid readouts from 30,000 feet. Computing enables that. Our lab brings hidden gems of artificial intelligence, statistics, and algorithmics to bear, for developing a systems-level understanding of the emergence, dynamicity, and fate of cellular phenotypes in healthy tissues and cancer. 


A detailed molecular portrait of a cell is possibly the most exquisite piece of art that conveys its past, present, and future. Our mission is to bring computing and data science to bear, to extract maximum from omic profiles. We develop robust, assumption-free computational approaches to explore unique, rare cell types and cell-states in disease and normal conditions. 

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