Bing Zhang
QA Engineer at DongXing Securities Ltd
Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolBing Zhang is a QA Engineer based in Beijing with four years of professional experience and a background in Android application development at Alibaba and NavInfo. He currently ensures app quality and deployment at DongXing Securities, bringing practical knowledge of mobile application behavior in production. Earlier roles include Android development for in-car navigation and health apps, giving him strong end-to-end familiarity with application-layer UX, voice interaction, and system integration. On GitHub he contributed ML-focused backend code to the OpenHGNN project, implementing a Metapath2Vec sampler and trainer to enable heterogeneous graph embeddings and node classification within a PyTorch/DGL stack. This blend of QA discipline, mobile engineering history, and hands-on ML toolchain work highlights his ability to bridge testing, app development, and research-grade open-source contributions. He combines pragmatic release ownership with curiosity about graph ML techniques that improve product analytics and features.
4 years of coding experience
1 year of employment as a software developer
Bachelor of Science (BS), Bachelor of Science (BS) at Soochow University (CN)
English
Github Skills (11)
algorithm10
algorithms10
pytorch10
machine-learning10
graph-neural-network10
python10
implement10
dgl10
data-processing9
data-set9
datasets9
Programming languages (5)
Github contributions (5)
BUPT-GAMMA/OpenHGNN
Nov 2021 - Jan 2023
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:2 releases, 23 reviews, 162 commits in 1 year 1 month
Contributions summary:Bing implemented the Metapath2Vec algorithm for heterogeneous graph embedding within the OpenHGNN toolkit. Their work included developing a `Metapath2VecSampler` for generating random walks and negative samples, alongside a `Metapath2VecTrainer` for model training and evaluation. These contributions aimed to enable node classification tasks using the generated embeddings, integrating the algorithm and associated training pipeline into the existing framework. The commits demonstrate a focus on the application of graph neural networks within a PyTorch environment.
dglgraphheterogeneousgraph-neural-networkspytorch
Zhanghyi/graphlearn-for-pytorch
Jul 2023 - Nov 2024
A graph learning library for PyTorch that makes distributed GNN training and inference easy and efficient.
Contributions:5 PRs, 99 pushes, 20 branches in 1 year 4 months
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