Yuying Liu is an applied scientist with nine years of experience building ML-driven systems at the intersection of AI agents, nonlinear dynamics, and privacy-preserving architectures. Currently at Amazon, she applies probabilistic graphs and federated learning to detect fraud early in the Trade-In business while architecting privacy-first solutions on AWS. Her research background spans physics-informed dynamical models (Koopman networks and Neural ODEs) and Bayesian signal processing, with prior work improving indoor localization and contributing to disease and poverty modeling using remote sensing. Trained in applied mathematics (PhD) and computer science (MS), she blends rigorous theory with production engineering—an attribute reflected in her habit of treating messy real-world feedback loops as first-class modeling components.
9 years of coding experience
2 years of employment as a software developer
Summer School Certificate, Deep Learning, Summer School Certificate, Deep Learning at Duke Kunshan University
Doctor of Philosophy - PhD, Applied Mathematics, 3.87/4.0, Doctor of Philosophy - PhD, Applied Mathematics, 3.87/4.0 at University of Washington
Bachelor of Science (B.Sc.), Mathematics & Statistics, 92/100, Bachelor of Science (B.Sc.), Mathematics & Statistics, 92/100 at Nankai University
Master's degree, Computer Science, 4.00/4.00, Master's degree, Computer Science, 4.00/4.00 at Georgia Institute of Technology
Contributions:27 commits, 25 pushes, 1 branch in 11 months
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