Jiaxiang Wu
Researcher at 元象 XVERSE
Shenzhen, Guangdong Province, China
Summary
🤩
Rockstar🎓
Top SchoolJiaxiang Wu is a researcher with 11 years of experience in AI and model compression, currently based in Shenzhen and working at XVERSE after a six-year research tenure at Tencent. He holds a Ph.D. in Computer Science from the Institute of Automation, Chinese Academy of Sciences, bringing deep academic rigor to applied engineering problems. His contributions to Tencent’s PocketFlow project show hands-on expertise in automatic model compression, GPU-based channel pruning, and practical tooling improvements for maintainability and deployment. Jiaxiang blends research and backend engineering, focusing on performance optimization and reproducible workflows for smaller, faster AI applications. Colleagues rely on him to translate complex model-level ideas into production-friendly code and scripts. He pairs strong algorithmic foundations with an eye for developer experience, evidenced by documentation and script fixes alongside core learner enhancements.
11 years of coding experience
6 years of employment as a software developer
Bachelor’s Degree, Automation, Bachelor’s Degree, Automation at Beijing Institute of TechnologyDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Institute of Automation, Chinese Academy of Sciences
Stackoverflow
1reputation
0reached
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Github Skills (8)
deep-learning10
python10
model-compression10
tensorflow9
shell8
script8
sh8
scripting8
Programming languages (3)
Github contributions (5)
Tencent/PocketFlow
Oct 2018 - May 2019
An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:178 commits, 61 PRs, 35 pushes in 6 months
Contributions summary:Jiaxiang contributed to bug fixes and made improvements to the file path parsing in the `utils/get_path_args.py` and also fixed some issues in `scripts/run_local.sh`. They also added source files and dependencies for documentation, which indicates involvement in code maintainability. The user worked on GPU-based channel selection, including model fine-tuning and performance optimization in `learners/channel_pruning_gpu/learner.py`.
aimodel-compressiondeep-learningmobile-appautoml
jiaxiang-wu/libhash
Jan 2017 - Mar 2017
Contributions:61 commits, 54 pushes, 1 branch in 2 months
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