Mao Guo
Subcon Program Manager at Intel
China
Summary
🤩
Rockstar🎓
Top SchoolMao Guo is a Subcon Program Manager at Intel with over 11 years of experience in IC packaging, substrate engineering, and manufacturing qualification, specializing in NAND and 3D XPoint memory products. He blends deep technical expertise—holding a PhD in Mechanical Engineering—with hands-on program leadership to drive material selection, process qualification, and timely transfer to mass production. His background includes leading SMT process characterization and resolving substrate/leadframe challenges from pathfinding to certification, ensuring smooth ramp-ups. Beyond manufacturing, he contributes to open-source AI inference tooling (OpenVINO), implementing shape inference and performance optimizations for CPU plugins—an unusual cross-disciplinary facet that highlights his ability to bridge hardware process engineering and machine learning tooling.
11 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Mechanical engineering, Doctor of Philosophy (Ph.D.), Mechanical engineering at Shanghai Jiaotong UniversityMaster’s Degree, Mechanical engineering, Master’s Degree, Mechanical engineering at Southwest Jiaotong UniversityBachelor’s Degree, Mechanical engineering, Bachelor’s Degree, Mechanical engineering at Central South University
Github Skills (8)
computer-vision10
deep-learning10
inference10
shapes10
openvino10
performance-optimization9
c-language8
cprogramming-language8
Programming languages (2)
Github contributions (5)
openvinotoolkit/openvino
Oct 2021 - Jul 2022
OpenVINO™ is an open source toolkit for optimizing and deploying AI inference
Role in this project:
ML Engineer
ML EngineerContributions:160 reviews, 16 commits, 32 PRs in 8 months
Contributions summary:Mao contributed to the OpenVINO toolkit by implementing shape inference for ExperimentalDetectronTopKROIs and other shape inference related tasks. They also addressed issues related to adaptive pooling for dynamic shape support. Additionally, the user made adjustments to the code to support the LRN, MVN, and ROI pooling functionality within the CPU plugin and enabled CPU Plugin cache for those layers, which likely involved optimizing performance.
inference-enginepytorchmodel-optimizerdeep-learninggpu
tianocore/edk2-archive
Feb 2016 - Mar 2018
Archive of old branches for EDK II
Contributions:338 commits, 3 branches in 2 years 1 month
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