Zhen Wang
Associate Professor
Guangzhou City, Guangdong Province, China
Summary
🤩
Rockstar🎓
Top SchoolZhen Wang is an associate professor at Sun Yat-sen University and a seasoned machine learning engineer with 11 years of experience spanning academia and industry, including senior algorithm roles at Alibaba. He works at the intersection of federated learning, graph neural networks, and reinforcement learning, contributing to high-profile open-source projects such as FederatedScope, GraphGym, and Ray RLlib. Zhen’s contributions combine practical system fixes and documentation with algorithmic innovation—adding hyperparameter tuning demos, fixing GNN message-passing bugs, and implementing advanced RL agents like DDPG and Rainbow. Based in Guangzhou, he blends rigorous research training from a PhD at SYSU and internships at Microsoft Research Asia with production-grade engineering, often focusing on reproducibility and integration across ML platforms.
11 years of coding experience
Liuzhou high school
Github Skills (26)
pytorch10
python10
gnn10
graph10
machine-learning10
reinforcement-learning10
message-passing10
deep-learning10
tensorflow10
hyperparameter-tuning10
graph-neural-network10
model-driven9
evaluation9
configuration-management9
ddpg9
Programming languages (6)
Github contributions (5)
alibaba/FederatedScope
Apr 2022 - Jan 2023
An easy-to-use federated learning platform
Role in this project:
ML Engineer
ML EngineerContributions:320 reviews, 33 commits, 130 PRs in 9 months
Contributions summary:Zhen updated the auto-doc component to reflect changes in the codebase, focusing on documenting the `gfl`, `nlp`, `cv`, and `attack` modules. They also added a demo for black-box optimization, integrating the federated learning platform with the `emukit` library for hyperparameter tuning. Furthermore, the user fixed path issues and corrected logger usage within the server code and the FedSagePlus implementation. The contributions demonstrate a focus on model development, integration, and documentation of a federated learning platform.
pytorchlearning-platformdata-privacydeep-learningmachine-learning
ray-project/ray
Apr 2018 - Jul 2019
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:ML Engineer
ML EngineerContributions:17 commits, 25 PRs, 2 pushes in 1 year 3 months
Contributions summary:Zhen primarily contributed to the Ray RLlib library within the project. Their work involved implementing and validating reinforcement learning algorithms, including DDPG and Rainbow (incorporating noisy networks and distributional Q-learning). The user also addressed issues related to the collection and handling of metrics within the RLlib framework, and implemented the exploration with Parameter Space Noise. This involved modifications to policy graphs and the overall agent training processes.
pythonconsistsruntimetensorflowserving
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