Aobo Li
Antenna Engineer at Google
San Diego, California, United States
Summary
👤
Senior🎓
Top SchoolAobo Li is an antenna and RF engineer with roughly a decade of hands-on research and product experience designing antennas, RF/microwave circuits, and wireless power systems for leading consumer-technology platforms. He has played key roles at Apple, Meta, and Google developing cellular, Wi‑Fi, GPS, and AR/VR antenna systems—work that spans watch, tablet, phone and headset form factors. Combining a PhD-level background in applied electromagnetics with practical PCB/IC prototyping and lab characterization skills, he excels at turning advanced theory (metasurfaces, phased arrays) into manufacturable hardware. Known for diagnosing tricky desense and integration problems, he brings both simulation fluency and bench-test pragmatism. Based in San Diego, he blends industrial product experience with academic research, making him effective at bridging early-stage innovation and high-volume device engineering.
8 years of coding experience
10 years of employment as a software developer
University of California San DiegoMaster of Science (MS), Telecommunications Engineering, Master of Science (MS), Telecommunications Engineering at Georgia Institute of TechnologyDoctor of Philosophy (PhD), Applied Electromagnetics, Doctor of Philosophy (PhD), Applied Electromagnetics at University of California, San DiegoBachelor's degree, Electrical, Electronics and Communications Engineering, Bachelor's degree, Electrical, Electronics and Communications Engineering at Shanghai Jiao Tong University
English, Chinese
Github Skills (10)
denoising8
transfer-learning6
neutrino5
physics5
bayesian5
machine-learning5
time-series3
ai3
zen1
bayesian-inference1
Programming languages (4)
Github contributions (5)
NuDot/machine_learning
Feb 2018 - Nov 2019
Contributions:32 PRs, 39 pushes, 12 branches in 1 year 8 months
legend-exp/gem
Sep 2020 - Sep 2022
Germanium Machine Learning is the collective machine learning project repository for Ge based neutrinoless double beta decay experiments.
Contributions:3 reviews, 28 commits, 22 PRs in 2 years
machine-learning
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