Mufei Li
Applied Scientist Intern at Amazon Web Services (AWS)
United States
Summary
🤩
Rockstar🎓
Top SchoolMufei Li is a software engineer with nine years of experience specializing in machine learning infrastructure and graph neural networks. He has contributed significant fixes and refactors to widely used open-source projects such as DGL and DeepChem, improving GCN implementations and making them more robust and user-friendly. His work spans ML engineering, backend development, and DevOps—packaging builds, creating reproducible conda environments, and automating lint/build scripts for scientific tooling. Based in the United States, he focuses on making advanced graph learning tools accessible to chemistry and biology researchers through projects like dgl-lifesci. Notably, his contributions include practical bug fixes (dropout, dynamic node/edge creation) and example updates that help bridge research code to reliable production-ready components. He maintains a public homepage showcasing his projects and technical interests.
10 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at Georgia Institute of TechnologyMATH & SCIENCE, MATH & SCIENCE at New York UniversityB.S., Honors Mathematics, B.S., Honors Mathematics at NYU Shanghai
Chinese, English
Github Skills (18)
pytorch10
python10
scripting10
machine-learning10
script10
deep-learning10
sh10
shell10
graph-neural-network10
dgl10
dependency-management9
conda9
drug-discovery9
build-automation9
computer-engineering9
Programming languages (6)
Github contributions (5)
dmlc/dgl
Oct 2018 - Jan 2023
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Role in this project:
ML Engineer
ML EngineerContributions:2927 reviews, 229 commits, 577 PRs in 4 years 3 months
Contributions summary:Mufei's commits focused on modifying and improving the GCN module within the DGL (Deep Graph Library) framework. They fixed and debugged the GCN module, including fixing the dropout module and adding support for creating nodes/edges after setting representations. The user refactored the GCN module and updated example code, specifically for GCN implementation on the Cora dataset.
deep-learninggraphpythongraph-neural-networks
awslabs/dgl-lifesci
May 2020 - Dec 2022
Python package for graph neural networks in chemistry and biology
Role in this project:Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:8 releases, 273 reviews, 291 commits in 2 years 7 months
Contributions summary:Mufei primarily focused on maintaining and updating the codebase, with a specific emphasis on packaging and setting up the build environment. They also added and modified scripts, specifically task linting and build scripts using shell. This work involved installing dependencies such as DGL and creating conda environments.
graph-neural-networkpythondeep-learninggraph-neural-networksdgl
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