Jiaxuan You
Urbana, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolJiaxuan You is a machine learning engineer with 10 years of experience specializing in graph neural networks and experiment infrastructure, based in Urbana, Illinois. A Stanford-trained researcher, Jiaxuan has contributed notable improvements to PyTorch Geometric and Stanford's GraphGym—adding a "gold-standard" GNN layer, refining GNN convolutions, and streamlining experiment pipelines and DevOps workflows. Their work spans model implementation (GNN explainers and GCN layers) to back-end tooling for reproducible experiments, showing comfort across research code and production-grade libraries. Active in high-profile open-source projects, they bridge algorithmic design and engineering pragmatism to accelerate GNN development. An interesting detail: beyond model design they routinely refactor shell scripts and configs, improving the day-to-day ergonomics of ML experimentation.
9 years of coding experience
4.0/4.0, 4.0/4.0 at Stanford University
Github Skills (19)
geometric-deep-learning10
pytorch10
python10
gnn10
configuration-management10
machine-learning10
bash10
deep-learning10
experiment10
experiment-manager10
graph-neural-network10
graph-convolutional-networks10
ml9
networkx9
mle9
Programming languages (3)
Github contributions (5)
snap-stanford/GraphGym
Nov 2020 - Aug 2022
Platform for designing and evaluating Graph Neural Networks (GNN)
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:2 releases, 17 reviews, 62 commits in 1 year 9 months
Contributions summary:Jiaxuan made significant contributions to the project's infrastructure and experiment management. They refactored and split shell scripts, likely streamlining the experiment workflow. They also updated configuration files and modified the training pipeline, suggesting involvement in model training and evaluation. The changes indicate a focus on improving experiment execution and managing resources.
gnnneural-graphneural-networksgraph-neural-networksgraph
pyg-team/pytorch_geometric
Jun 2021 - Jun 2022
Graph Neural Network Library for PyTorch
Role in this project:ML Engineer
ML EngineerContributions:22 reviews, 13 commits, 12 PRs in 1 year
Contributions summary:Jiaxuan primarily contributed to the development and enhancement of graph neural network layers within the PyTorch Geometric library. They introduced a "Gold-Standard GNN Layer" and made multiple iterations, including general GNN convolutions with various design options. Additionally, the user was involved in integrating GraphGym with PyG, and fixing related bugs. These contributions demonstrate an active role in improving the core functionality of GNN models within the repository.
pytorchgraph-convolutional-networksgeometric-deep-learningdeep-learningneural-graph
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