Lingqing Gan
Software Engineer at Google
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolLingqing Gan is a software engineer and PhD-trained researcher with nine years of experience building scalable back-end systems and probabilistic models, currently contributing to Google and the Kubeflow Pipelines open-source project. Her doctoral work and thesis focused on detecting network topology from time-series data, where she designed MCMC/Gibbs sampling algorithms and optimized estimation from exponential to quadratic complexity. She has a strong research track record in Bayesian modeling, opinion dynamics, and model selection for growing networks, implemented in Python and MATLAB. A practiced educator, she has built introductory Python curricula for teenagers and served five years as an undergraduate TA, reflecting an ability to explain complex algorithms clearly. Based in Seattle, she blends deep theoretical skill with hands-on engineering, evidenced by bug fixes and API work on a widely used ML pipelines repo.
9 years of coding experience
Doctor of Philosophy (Ph.D.), Electrical Engineering, Doctor of Philosophy (Ph.D.), Electrical Engineering at Stony Brook UniversityHigh School, High School at The No.1 Middle School Affiliated To Central China Normal UniversityBachelor of Engineering (BEng), Information Engineering, Bachelor of Engineering (BEng), Information Engineering at Zhejiang University
English, Chinese
Github Skills (11)
kubeflow-pipelines10
go10
api10
data-pipeline10
data-pipelines10
back-end-development10
apidoc10
pipe10
pipeline10
yaml9
python6
Programming languages (11)
Github contributions (5)
kubeflow/pipelines
Apr 2022 - Jan 2023
Machine Learning Pipelines for Kubeflow
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 releases, 332 reviews, 69 commits in 9 months
Contributions summary:Lingqing primarily contributed to the back-end of the Kubeflow Pipelines project, focusing on the implementation and support of the IR YAML format within the API, which involved modifying existing components. They also worked on bug fixes related to simple loops and other errors. Further commits reflect version bumps and updates to the Python SDK and included changes to time display, with related tests.
pipelinetektondata-sciencemachine-learningmlops
Linchin/pipelines
Mar 2022 - Aug 2023
Machine Learning Pipelines for Kubeflow
Contributions:4 PRs, 224 pushes, 46 branches in 1 year 5 months
data-sciencemachine-learningmlopskedrokubeflow
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