Guoqing Xu
Staff Data Scientist
Fremont, California, United States
Summary
👤
Senior🎓
Top SchoolGuoqing Xu is a Staff Data Scientist with a decade of experience building and deploying large-scale machine learning systems across retail, real estate, telecom, and research organizations. Based in Fremont, CA, he blends production ML engineering at Walmart Global Tech with hands-on model development from prior roles at realtor.com, Huawei, and 24-7 Intouch. He has deep practical expertise in TensorFlow—publishing tutorials and efficient TFRecord-based pipelines for image classification—demonstrating a strong focus on data handling and model throughput. Guoqing’s background includes academic research experience and two master’s degrees, which underpin his ability to translate research ideas into robust production solutions. Colleagues describe him as an engineer who balances pragmatic product impact with careful model engineering and scalable data pipelines. Notably, his open-source tutorials reveal a commitment to sharing best practices for reproducible, performance-conscious ML workflows.
10 years of coding experience
7 years of employment as a software developer
Master of Science - MS, Master of Science - MS at The University of WinnipegMaster of Science - MS, Master of Science - MS at Northeastern University (CN)
Stackoverflow
482reputation
22kreached
27answers
1question
Github Skills (13)
mask-rcnn10
faster-rcnn10
tensorflow10
python10
image-classification10
tfrecord9
machine-learning9
pytorch6
google-cloud-storage6
google-cloud-ml-engine6
google-cloud-ml6
google-cloud-platform6
google-cloud-kms6
Programming languages (1)
Github contributions (5)
kevin369ml/My-TensorFlow-tutorials
Mar 2017 - Mar 2019
This repo contains all of my TensorFlow tutorials
Role in this project:
ML Engineer
ML EngineerContributions:141 commits, 2 PRs, 139 pushes in 2 years
Contributions summary:Guoqing contributed to TensorFlow tutorials, focusing on image classification tasks using convolutional neural networks (CNNs). Their work includes developing data input pipelines, specifically for image datasets like cats vs. dogs and CIFAR-10, implementing image standardization, and building model architectures. The commits also involve the transformation of data into TFRecord format for efficient processing and model training, which further suggests a focus on data handling within the machine learning workflow.
pythondeep-learningmachine-learningtensorflow-tutorialstensorflow
kevin369ml/ml_pytorch
Jun 2023 - Jan 2024
Contributions:11 pushes, 1 branch in 7 months
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