Juan Villada is a data scientist based in Berkeley with a decade of experience focused on large-scale metagenomics QA/QC, bridging computational rigor with biosecurity-minded research. At Berkeley Lab he ensures integrity of high-throughput sequencing pipelines while probing how coding sequences are naturally and synthetically optimized and how harmful, metabolism-controlling sequences can be deterred or detected. He combines metagenomics at scale with AI techniques to harden analyses against adversarial or risky genetic designs, reflecting an uncommon blend of bioinformatics, security thinking, and production-grade data engineering. Passionate about defensible, auditable workflows, he brings practical solutions for reproducible metagenomic data in complex, real-world environments.
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