Yafeng Guo
Director, AI & Data Engineering Greater China
Pudong, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolYafeng Guo is a seasoned AI and data engineering leader based in Shanghai, currently directing AI & Data Engineering for Greater China at Nike with over 15 years of hands-on experience in project, release and team management. He combines deep expertise in data warehousing, real-time stream and offline data architectures with strong Java and Python engineering skills, having driven end-to-end solutions from data collection to compute, storage and service layers. His background spans major tech and enterprise environments—Ant Financial, Alibaba, Sanofi and HPE—where he shaped scalable data platforms and solution architectures. An active contributor to the widely used Alibaba MNN deep learning framework, he focuses on robust training and data pipeline fixes that improve cross-platform reliability. Fluent in oral English and quick to adopt new technologies, he holds degrees from Shanghai Jiao Tong University and a master’s in AI from Illinois Institute of Technology, blending practical systems thinking with research-informed approaches.
9 years of coding experience
20 years of employment as a software developer
Master of Artificial Intelligence, Master of Artificial Intelligence at Illinois Institute of TechnologyBachelor Electrical Engineering, Bachelor Electrical Engineering at Shanghai Jiao Tong University
English, Chinese
Github Skills (10)
debug10
c-language10
cprogramming-language10
n10
msn10
machine-learning9
deep-learning9
convolution7
arm6
vulkan4
Programming languages (7)
Github contributions (5)
alibaba/MNN
Jan 2020 - Nov 2022
MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba. Full multimodal LLM Android App:[MNN-LLM-Android](./apps/Android/MnnLlmChat/README.md)
Role in this project:
Backend Developer
Backend DeveloperContributions:8 commits, 1 PR, 5 pushes in 2 years 10 months
Contributions summary:Yafeng primarily contributed to bug fixes within the training and demo tools of the MNN framework. These fixes address compilation issues on Windows platforms and resolve bugs in demo functionality. The code changes demonstrate a focus on refining the data loading and processing components utilized within the training pipeline, specifically targeting the use of different data loader types. Furthermore, the user addressed shape-related issues in binary operations.
casesvulkanconvolutionalibabaframework-learning
yyfcc17/MNN
Jan 2020 - Nov 2023
MNN is a lightweight deep neural network inference engine.
Contributions:5 pushes in 3 years 11 months
pytorchinference-enginedeep-learningdeep-neural-networkinference
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