Hanbin Hu is a software engineer and Ph.D. researcher with a decade of experience building ML-driven tools for analog and mixed-signal verification and EDA, currently working at Google in Sunnyvale. His work bridges rigorous academic research—multiple papers and awards in DAC/ICCAD/ITC—and production-quality C++ and ML systems, from optimizing sparse solvers in circuit simulators to deploying reversible networks for post-silicon anomaly detection. He brings deep expertise in robustness analysis, Bayesian methods, and adversarial techniques applied to hardware verification, combined with hands-on optimization skills demonstrated in photogrammetry and map geometry projects at Google. Notably, he transferred mid-PhD from Texas A&M to UC Santa Barbara and has repeatedly turned constrained simulation budgets into high-impact verification results using Gaussian processes and statistical techniques.
10 years of coding experience
5 years of employment as a software developer
High School Diploma, High School Diploma at Shanghai Yan'an High School
Doctor of Philosophy - PhD, Computer Engineering, Overall GPA: 4.0/4.0, Doctor of Philosophy - PhD, Computer Engineering, Overall GPA: 4.0/4.0 at UC Santa Barbara
Doctor of Philosophy - PhD, Computer Engineering, Overall GPA: 4.0/4.0, Doctor of Philosophy - PhD, Computer Engineering, Overall GPA: 4.0/4.0 at Texas A&M University
Master of Science (M.S.), Electronic Science and Technology, Overall GPA: 3.59/4.0; Major GPA: 3.71/4.0, Master of Science (M.S.), Electronic Science and Technology, Overall GPA: 3.59/4.0; Major GPA: 3.71/4.0 at Shanghai Jiao Tong University
Contributions:34 pushes, 1 branch in 3 years 4 months
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