Ariella Gladstein is an evolutionary biologist and computational scientist with nine years of experience applying machine learning and deep learning to population genomics and ancestral inference. Her PhD focused on reconstructing evolutionary history from genomic data, and her postdoc expanded that work into practical deep-learning models and community-driven benchmarking for population genetics. In industry she scaled ancestry deconvolution pipelines at Embark, improving automation from 40% to over 95% and helping operations grow to millions of samples while contributing to a multi-million-dollar science initiative. At UCLA she continues bridging academic research and production-grade computational biology, with strong skills in programming, data science, and scientific communication. Known for collaborative contributions to the tskit/stdpopsim ecosystem, she combines rigorous methodological development with product-minded engineering to solve complex genomics problems.
9 years of coding experience
2 years of employment as a software developer
The University of Arizona
Russian State University for the Humanities
Bachelor of Science - BS, Mathematical Biology, Bachelor of Science - BS, Mathematical Biology at Beloit College
Contributions:2 reviews, 27 commits, 13 PRs in 4 months
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