Qibin Chen
Member Of Technical Staff at OpenAI
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolQibin Chen is a machine learning engineer with nine years of experience, currently a Member of Technical Staff at OpenAI after founding work on Apple's Foundation Model post-training team. He combines a strong academic background from Carnegie Mellon and Tsinghua with hands-on research in representation learning, NLP, and software engineering applications, including contrastive and graph representation learning, knowledge graphs, and open-domain question answering. At Apple he helped operationalize large-model workflows, and his open-source contributions to graph deep learning projects (e.g., CogDL) show a focus on robust data loading and graph neural network components. Co-advised by prominent researchers during his MS, he brings both rigorous research methodology and production-oriented engineering to ML system design. Based in the Bay Area, he’s also exploring parallel computing and distributed systems to better scale ML infrastructure.
9 years of coding experience
8 years of employment as a software developer
Master of Science - MS Computer Science (Language and Information Technology), Master of Science - MS Computer Science (Language and Information Technology) at Carnegie Mellon University School of Computer ScienceBachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at Tsinghua University
Chinese, English
Github Skills (4)
graph-neural-network10
data-handling10
python10
pytorch10
Programming languages (4)
Github contributions (5)
THUDM/CogDL
Mar 2019 - Jan 2020
CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)
Role in this project:
Back-end Developer
Back-end DeveloperContributions:48 commits, 1 PR, 23 pushes in 9 months
Contributions summary:Qibin's commits primarily focus on updating and improving the `kegraph/data/data.py` and `kegraph/datasets/entities.py` files. The code changes include modifications to data handling and dataset processing, demonstrating a focus on the data loading and management aspects of the project. Further contributions involve work on the `cognitive_graph` module, particularly in areas related to graph neural networks, with commits addressing the model, and link_prediction.
pytorchleaderboardgraph-convolutional-networksgnndeep-learning-on-graphs
squaresLab/VarCLR
Mar 2021 - Jan 2023
Contributions:43 commits, 17 pushes, 2 branches in 1 year 9 months
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