Research Direction

PERCEPT for data-driven ligand design

Modular, last-step targeting ligand attachment using reversible, traceless conjugation chemistry, built for iteration and matched comparisons in vivo.

Focus

I build large pooled libraries of peptide-decorated CRISPR RNPs, screen them in vivo, and use the resulting datasets to train models that prioritize the next library design. The goal is an active learning loop for discovering ligands and formulations that improve tissue access and functional editing in vivo.

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