Summary
Hanbin Hu is a software engineer and researcher specializing in machine learning for analog circuit verification and robust neural networks. He is currently a Software Engineer at Google in Sunnyvale, bridging industry-scale software development with cutting-edge PhD research in computer engineering. His doctoral work at UCSB and earlier at Texas A&M centers on ML-driven electronic design automation, Bayesian analysis, and trustworthy ML for post-silicon failure detection. He has published DAC 2018, ICCAD 2018, and ITC 2020 papers and earned a Best Paper Nomination, reflecting a strong publication track record. His career spans internships and roles at TI, Cadence, Synopsys, and Google, contributing to solver optimization, anomaly detection, and mapping/visualization work. Based in the Bay Area, he combines deep theoretical background with hands-on software engineering to deliver practical, scalable design-automation solutions.
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 Texas A&M University
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
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
English, Chinese