Lingfan Yu
Applied Scientist at Amazon Web Services (AWS)
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolLingfan Yu is an Applied Scientist at AWS with 11 years of experience focused on improving deep learning execution and serving infrastructure, now specializing in Large Language Model serving systems. He holds a Ph.D. in Computer Science from NYU, where his research and teaching under Professor Jinyang Li centered on distributed systems and performance optimization. Lingfan has practical ML engineering experience contributing optimized GAT and GCN implementations to the widely used DGL project, reflecting a blend of research rigor and production-oriented coding. His background includes internships and prior applied scientist roles at Meta and AWS, giving him cross-industry exposure to both research and cloud-scale deployment. Based in the San Francisco Bay Area, he combines academic depth with hands-on system building to make ML models faster and more reliable in production.
11 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at New York UniversityBachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Nanjing UniversityExchange Program (Non-Degree), Computer Science, Exchange Program (Non-Degree), Computer Science at University of Waterloo
Github Skills (5)
pytorch10
deep-learning10
graph-neural-network10
dgl10
machine-learning9
Programming languages (8)
Github contributions (5)
dmlc/dgl
Jun 2018 - Nov 2020
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 47 commits, 48 PRs in 2 years 4 months
Contributions summary:Lingfan contributed to the development of Graph Attention Network (GAT) and Graph Convolutional Network (GCN) models within the DGL framework. Their work includes implementing the GAT and GCN models in PyTorch, including the creation and modification of various files to support different datasets. The user also refactored and updated the model code to use the latest DGL API and to improve the performance of the code.
deep-learninggraphpythongraph-neural-networks
lingfanyu/dgl
Dec 2018 - Mar 2021
This is just a fork of dmlc/dgl: https://github.com/dmlc/dgl
Contributions:161 pushes, 55 branches in 2 years 3 months
dgl
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