Liyang Ling
AI Framework Engineer at Intel Corporation
Minhang District, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolLiyang Ling is an AI Framework Engineer at Intel with nine years of experience building deep learning compilers and making TensorFlow-like frameworks run efficiently across platforms. He holds an MS in Big Data Technology from HKUST and a BE in Software Engineering from Southeast University, and brings strong systems and multi-language programming skills (Python, C++, Java, JavaScript) across Linux/Windows. At Intel he progressed from Software Development Engineer to AI Framework Engineer, focusing on compiler optimizations and framework bridges; his open-source work includes adding advanced math functions and OpenVINO integrations to the widely used PlaidML project. Comfortable across back-end, ML, and big data stacks, he has internship experience in NLP on Spark and production web backends, and he pairs practical engineering with a curiosity for compiler-level improvements that accelerate real-world ML workloads.
10 years of coding experience
3 years of employment as a software developer
Bachelor of Engineering - BE, Software Engineering, Bachelor of Engineering - BE, Software Engineering at Southeast UniversityHong Kong University of Science and Technology (HKUST)
Chinese, English
Github Skills (11)
machine-learning10
web-framework10
c-language10
deep-learning10
cprogramming-language10
plaidml10
openvino10
testing9
python9
mlops8
linear-algebra8
Programming languages (8)
Github contributions (5)
plaidml/plaidml
Dec 2020 - Jul 2021
PlaidML is a framework for making deep learning work everywhere.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:11 reviews, 11 commits, 20 PRs in 7 months
Contributions summary:Liyang implemented several new inverse hyperbolic and other mathematical functions within the PlaidML framework, contributing significantly to the expansion of its capabilities. They added these functions across multiple dialects and integrations, including EDSL, tile, stdx, and OpenVINO. Furthermore, the user added and refined tests for the newly implemented functionalities. The user also worked on integrating one-hot and reduce operations with the OpenVINO bridge.
deep-learningplaidml
leonling-ll/LinuxSetup
Mar 2021 - Oct 2022
Contributions:52 commits, 64 pushes, 4 branches in 1 year 7 months
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