Xiaojie Guo
Senior Research Scientist at 微软
Washington DC-Baltimore Area United States
Summary
🤩
Rockstar🎓
Top SchoolXiaojie Guo is a Senior Research Scientist with 7+ years of experience bridging academic rigor and industry impact in AI for code, NLP, and Graph Neural Networks. With a PhD in Information Science and Technology from George Mason University and roles at IBM and JD.com, Xiaojie has driven foundation-model and GNN research into practical tools and published multiple papers on these topics. They contributed technical improvements to the popular Graph4NLP library—enhancing GraphSAGE aggregation and edge-weight handling—demonstrating a talent for turning research ideas into robust open-source components. Now at Microsoft, Xiaojie continues to focus on genAI and LLMs for code, combining deep graph-learning expertise with strong engineering skills in Python, R, and MATLAB. Colleagues describe them as both a rigorous researcher and a hands-on implementer who closes the loop from theory to production.
7 years of coding experience
5 years of employment as a software developer
Master's degree Control Engineering, Master's degree Control Engineering at Soochow University (CN)Doctor of Philosophy - PhD Information Science and Technology, Doctor of Philosophy - PhD Information Science and Technology at George Mason University
English, Chinese
Github Skills (5)
pytorch10
machine-learning10
graph-neural-network10
natural-language-processing9
deep-learning9
Programming languages (2)
Github contributions (5)
graph4ai/graph4nlp
Jul 2020 - Apr 2022
Graph4nlp is the library for the easy use of Graph Neural Networks for NLP. Welcome to visit our DLG4NLP website (https://dlg4nlp.github.io/index.html) for various learning resources!
Role in this project:
ML Engineer
ML EngineerContributions:108 commits, 17 PRs, 27 pushes in 1 year 9 months
Contributions summary:Xiaojie primarily contributed to the development and modification of GraphSAGE, a Graph Neural Network (GNN) model within the repository. Their work involved implementing edge weights, refining aggregation types (mean, GCN), and addressing potential issues in the BiSepGraphSAGELayerConv for the GraphSAGE model, indicating efforts to enhance the model's performance and adaptability. The commits demonstrate bug fixes and updates to integrate the model within the project's overall architecture, and testing the model within a name entity recognition example.
nlppytorchindexdeep-learningvisit
xguo7/NEC-DGT
Feb 2020 - Mar 2020
Contributions:25 commits, 48 pushes, 1 branch in 1 month
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