Ramon Zhou
Senior GTM Specialist at Amazon Web Services (AWS)
Shanghai, Shanghai, China
Summary
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Rockstar🎓
Top SchoolRamon Zhou is a Senior GTM Specialist and seasoned solutions architect with over 15 years of experience across Intel, Tencent, Oracle and EMC, blending product, channel and partner strategy with hands-on R&D background from Oracle’s development center. He has led cloud-native and medical AI initiatives—most notably contributing to Tencent’s national medical imaging platform and NHSA cloud framework—while building partner ecosystems with top ISVs to win large healthcare and enterprise deals. Technically fluent, Ramon has contributed to open-source deep learning graph tooling (DGL) with performance-focused sampling and distributed training features, reflecting his ability to bridge low-level engineering with go-to-market execution. Based in Shanghai, he combines strong customer-facing skills and industry relationships with a track record of turning complex AI, database and storage technologies into scalable commercial solutions.
8 years of coding experience
9 years of employment as a software developer
Master Computer Science, Master Computer Science at Nanjing University
Stackoverflow
1reputation
0reached
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Github Skills (13)
pytorch10
machine-learning10
deeplearning-ai10
distributed-training10
deep-learning10
graph-neural-network10
python10
cprogramming-language9
performance-optimization9
c-language9
algorithms8
data-structures8
data-structure8
Programming languages (3)
Github contributions (5)
dmlc/dgl
Jul 2023 - Apr 2024
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:64 reviews, 89 PRs, 25 pushes in 10 months
Contributions summary:Ramon contributed significantly to the DGL project by adding features related to graph sampling and distributed training, including support for pickle serialization for `SampledSubgraph`, refactoring sampling code, and introducing a multi-GPU node classification example. The contributions also include the optimization of sampling algorithms for improved performance. The user also worked on code refactoring to make use of pre-allocation in sampling, eliminating torch.cat, and adding functionality for to and to_pyg_data for MiniBatch.
pytorchpythondeep-learningmachine-learninggraph-neural-networks
RamonZhou/dgl
Jul 2023 - Apr 2024
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Contributions:406 pushes, 58 branches in 10 months
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