Kuang Liu
Software Engineer at Google
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolKuang Liu is a software engineer with 11 years of experience building resilient network and cloud infrastructure, currently contributing to Google’s backbone network efforts from San Jose. His career began in network support and escalation roles at A10 Networks, where he progressed to staff engineer working on threat protection systems, giving him deep operational and security-focused networking expertise. At Google he blends that pedigree with large-scale cloud infrastructure development, routinely tackling performance, reliability, and system design challenges. Kuang is also an active ML practitioner—his open-source work includes improving CNN architectures for CIFAR-10 (notably adding EfficientNetB0 and RegNet variants) and achieving competitive results on that benchmark. He holds an MS from USC and a BEng from Shanghai Jiao Tong University, pairing formal electrical engineering training with hands-on software and ML engineering. Colleagues would describe him as a pragmatic problem-solver who moves fluidly between low-level networking details and higher-level model optimization.
11 years of coding experience
6 years of employment as a software developer
Master of Science (MS), Electrical and Electronics Engineering, Master of Science (MS), Electrical and Electronics Engineering at USCBachelor of Engineering (BEng), Electrical and Electronics Engineering, Bachelor of Engineering (BEng), Electrical and Electronics Engineering at Shanghai Jiao Tong University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
mask-rcnn10
faster-rcnn10
pytorch10
machine-learning10
deep-learning10
efficientnet10
cifar1009
python8
Programming languages (3)
Github contributions (5)
kuangliu/pytorch-cifar
May 2019 - Feb 2021
95.47% on CIFAR10 with PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:31 commits, 5 PRs, 88 pushes in 1 year 9 months
Contributions summary:Kuang primarily contributed to implementing and updating various deep learning models within the repository. Their work included adding EfficientNetB0 and RegNet models, along with updating the architecture of EfficientNetB0. The user also made changes to the DLA model, demonstrating a focus on exploring and integrating different CNN architectures for the CIFAR10 dataset. These changes suggest an active role in model selection and optimization for achieving improved performance.
pytorchcifar10resnetefficientnet
kuangliu/transforms
Jan 2021 - Feb 2022
Contributions:12 commits, 28 pushes, 2 branches in 1 year 1 month
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