Mufei 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.
Python package for graph neural networks in chemistry and biology
Role in this project:
Back-end Developer & DevOps Engineer
Contributions: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.
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Role in this project:
ML Engineer
Contributions: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.
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