Quan Gan is a research-driven applied scientist with 12 years of experience bridging deep learning research and production systems, currently working on relational data modeling and large language models at HKU Shanghai Cross Innovation Lab and AWS. He combines a strong background in systems engineering and backend design with hands-on ML implementation—contributing to DGL (a prominent graph deep learning library) by implementing GNN modules such as a TreeGlimpsedClassifier. His experience spans industry roles at PepsiCo and AWS as well as academic research and teaching at NYU, giving him a rare mix of applied R&D and production delivery. Based in Shanghai, he leverages AWS and PyTorch expertise to move graph and language models from prototype to scalable deployment.
12 years of coding experience
2 years of employment as a software developer
New York University
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Fudan University
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Role in this project:
ML Engineer
Contributions:22 releases, 827 reviews, 487 commits in 4 years 9 months
Contributions summary:Quan contributed to the deep learning on graph library, DGL. The commits indicate the implementation of a GNN module, particularly a TreeGlimpsedClassifier based on a balanced tree of latent variables. Code changes involve modifications to model definition (model.py) and graph data structure (graph.py) as well as applying edge softmax. The user demonstrates an understanding of graph neural networks and their implementations as well as the use of PyTorch.
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