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development of transferable learned representations from large-scale public omics data and metadata, and the software infrastructure needed to build, maintain, and distribute them at community scale, including
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, and knowledge-augmented modeling. By studying and improving how LLMs are trained, calibrated, and deployed, the lab aims to build principled AI systems that can be integrated into high-stakes healthcare
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