PhD Candidate, Department Of Biomedical Data Science
Palo Alto, California, United States
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Oana Enache is a PhD candidate in Biomedical Data Science at Stanford, specializing in statistical methods that probe algorithmic fairness and equity in AI/ML models affecting Medicare Advantage beneficiaries. With a decade of experience across computational biology, biostatistics, and policy-facing AI roles, she blends deep technical skill with practical health-policy impact—having supported the U.S. Department of Commerce through a Stanford HAI fellowship. Her background includes computational biology positions at the Broad Institute and industry internships, giving her fluency in large-scale biomedical data and translational research. Trained at Duke (MS Biostatistics) and UC Berkeley (BA Applied Mathematics, computational biology), she brings rigorous quantitative foundations to messy, real-world health datasets. Notably, her work explicitly targets the intersection of methodology and equity, aiming to make ML-driven health decisions more just for marginalized Medicare populations. Based in Palo Alto, she combines academic research with policy engagement to move algorithmic advances into equitable practice.
10 years of coding experience
5 years of employment as a software developer
BA, Applied Mathematics, concentration Computational Biology, BA, Applied Mathematics, concentration Computational Biology at University of California, Berkeley
Master of Biostatistics, Duke University School of Medicine, Data Science Concentration, Master of Biostatistics, Duke University School of Medicine, Data Science Concentration at Duke University
Doctor of Philosophy - PhD, Biomedical Informatics, Doctor of Philosophy - PhD, Biomedical Informatics at Stanford University School of Medicine
Repository for our dashboard exploring aspects of COVID19-related trials in the United States and globally.
Contributions:4 reviews, 5 PRs, 77 pushes in 2 months
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Oana Enache - PhD Candidate, Department Of Biomedical Data Science