Richard Zhu is a Harvard-trained computational scientist bridging AI, statistics, neuroscience, and biomedicine with nine years of research experience across labs at Johns Hopkins, Harvard Medical School, Brigham and Women's, and UCLA. He develops and applies machine learning methods—recently diffusion models for protein-protein docking and LLM-based biomedical agents for drug repurposing—to accelerate therapeutics for neurological disease. His work spans wet-lab molecular investigations of Alzheimer’s patient-derived cells to bioinformatics studies of bacterial virulence, giving him rare end-to-end fluency from data generation to model-driven insight. Beyond the bench, he leads student teams in synthetic biology and medical technology, competes on Harvard’s nationally ranked quadball team, and runs intergenerational storytelling initiatives that inform his human-centered research perspective. Currently pursuing an MS in Computer Science, he combines rigorous statistical training with hands-on experimental experience to translate cutting-edge AI into clinically relevant tools. An under-the-radar strength is his track record of presenting award-winning, cross-disciplinary projects at scientific symposia, demonstrating both technical depth and communication impact.
Supporting multi-center research requires combining data created in different data models; this community coordination project aims to provide an data model adaptor for CTSA hubs.
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