CJ Battey is a senior computational scientist with 11 years of experience applying machine learning and statistical modeling to cell-free DNA assays for prenatal and oncology diagnostics. At Myriad Genetics he leads development of fetal aneuploidy, twin zygosity, fetal fraction, and tumor fraction models, is an inventor on four cfDNA-related patents, and mentors PhD interns while operating regulated bioinformatics pipelines. His academic background includes a PhD in biology from the University of Washington and an NIH F32–funded postdoc where he published first-author papers and released open-source deep learning tools for geographic ancestry inference. Comfortable bridging wet lab assay tuning and production-grade software in SDLC environments, he brings a rare mix of population-genetics research rigor and practical diagnostics engineering. An Ojai-based scientist, he balances high-impact diagnostics R&D with a track record of deploying reproducible, low-coverage WGS methods for challenging clinical applications.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD Biology, Doctor of Philosophy - PhD Biology at University of Washington
Bachelor's degree Integrative Biology English, Bachelor's degree Integrative Biology English at University of California, Berkeley
deep learning prediction of geographic location from individual genome sequences
Contributions:3 releases, 10 reviews, 96 commits in 3 years 4 months
genomeindividualgeographypredictiondeep-learning
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