Ying L
Business Intelligence Engineer at Rewriting the Code
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolYing L is a Business Intelligence Engineer based in Seattle with five years of experience translating data into actionable product and growth insights across startups and large enterprises. Currently on AWS’s Analytics and Data Solutions team, she blends a strong analytics background with hands-on data engineering and product-focused measurement from prior roles at Liven, inCitu, and EonLabs.AI. Ying holds a Master’s in Informatics from NYU and a BBA in Finance, which informs her ability to bridge technical analytics with business strategy. She’s also an active contributor to open-source graph ML tooling—having worked on DGL’s graph reordering and data pipeline fixes—bringing practical expertise in graph neural network workflows that isn’t obvious from her BI title alone.
4 years of coding experience
2 years of employment as a software developer
Master's degree Informatics, Master's degree Informatics at New York UniversityBachelor of Business Administration - BBA Finance, Bachelor of Business Administration - BBA Finance at Hong Kong Baptist University
Chinese, English, Chinese
Github Skills (6)
graph-algorithms10
graph-neural-network10
python10
gnn10
dgl10
machine-learning9
Programming languages (7)
Github contributions (5)
dmlc/dgl
Jun 2021 - Jan 2023
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 releases, 1589 reviews, 185 commits in 1 year 6 months
Contributions summary:Ying primarily contributed to the development of graph algorithms and machine learning models, specifically focusing on DGL and its applications to graph neural networks. Their commits include implementing and improving the functionality of the ``dgl.reorder()`` API for graph reordering and relabeling, as well as fixing issues with the data loading and partitioning pipeline within the GraphBolt framework. The user's work is closely tied to the core functionality and API of the DGL library, enhancing its capabilities for graph analysis and machine learning tasks.
pytorchpythondeep-learningmachine-learninggraph-neural-networks
Rhett-Ying/dgl
Jun 2021 - Mar 2025
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Contributions:12 PRs, 1982 pushes, 930 branches in 3 years 9 months
pytorchpythondeep-learningmachine-learninggraph
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