Qian Ge
Menlo Park, California, United States
Summary
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SeniorQian Ge is a research scientist at Google, applying data-driven methods to fleet-wide systems optimization with a focus on memory/CPU efficiency, tiered memory design, and C++ allocator improvements (TCMalloc). She blends over a decade of industry and academic experience, including a PhD from UNSW on principled elimination of microarchitectural timing channels through OS-enforced time protection and a formative period in the UNSW Trustworthy Systems group under Prof. Gernot Heiser. Her work spans low-level systems engineering and ML-driven optimization, turning theoretical insights into production-ready implementations. Prior to Google, she held software engineering roles at Google (2019–2021) and CSIRO Data61, with earlier industry experience at Huawei, giving a broad view of practical systems development. Based in Menlo Park, she brings a rigorous academic foundation from the University of Sydney and UNSW to bear on scalable, secure, and efficient software at scale.
11 years of coding experience
Github Skills (21)
googlenet10
propagation10
convolutional-neural-networks10
classification10
variational-autoencoder10
back-propagation10
autoencoders10
autoencoder10
keras10
information-theory10
unsupervised-learning10
densenet10
adversarial-learning10
resnet10
activation10
Programming languages (1)
Github contributions (5)
conan7882/DeepVision-tensorflow
Sep 2017 - Sep 2018
A deep learning package for computer vision algorithms built on top of TensorFlow
Contributions:247 commits, 2 PRs, 209 pushes in 1 year
visiondeep-learningcomputer-visiontensorflowon-top
conan7882/CNN-Visualization
Oct 2017 - Sep 2018
TensorFlow implementations of visualization of convolutional neural networks, such as Grad-Class Activation Mapping and guided back propagation
Contributions:153 commits, 127 pushes, 6 branches in 11 months
implementationspropagationactivationdeep-learningback-propagation
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