Xingcheng Yao
Research Scientist in AI at Moonshot AI
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolXingcheng Yao is an AI research scientist and machine learning engineer with seven years of experience building and evaluating graph neural network models and knowledge-graph embeddings. He has contributed to the widely used CogDL library—adding GNN implementations, unit tests, and support for bio/chem datasets—and has hands-on experience integrating TransE, RotatE, Complex, and DistMult for link prediction. His research roles span Moonshot AI and a Princeton research internship, and he is currently pursuing a PhD in computer science at UCLA after earning a BE from Tsinghua. Known for bridging rigorous research with production-quality engineering, he focuses on robust model interfaces and reproducible evaluation. A practical problem-solver, he often brings cross-domain datasets (biology/chemistry) into pretraining workflows to improve real-world model transfer.
7 years of coding experience
1 year of employment as a software developer
Bachelor of Engineering - BE, Computer Science, Bachelor of Engineering - BE, Computer Science at Tsinghua UniversityUniversity of California, Los Angeles
Github Skills (7)
pytorch10
machine-learning10
graph-neural-network10
python10
graph-embedding9
knowledge-graph9
unit-testing8
Programming languages (3)
Github contributions (5)
THUDM/CogDL
Sep 2020 - Feb 2021
CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 7 PRs in 4 months
Contributions summary:Xingcheng contributed to the implementation and testing of various graph neural network models within the CogDL library. Their work included modifying interfaces for existing models like DGI and GCC, and adding unit tests to ensure functionality. They also integrated knowledge graph embedding algorithms, such as TransE, RotatE, Complex, and DistMult, for link prediction tasks. Furthermore, the user added support for bio and chem datasets for GNN pretraining.
pytorchleaderboardgraph-convolutional-networksgnndeep-learning-on-graphs
yaoxingcheng/TLM
Oct 2021 - Jul 2022
ICML'2022: NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Contributions:28 commits, 3 PRs, 14 pushes in 8 months
pytorchnlplanguage-modelbertdeep-learning
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