Shiori Sagawa
Member Of Technical Staff at OpenAI
Palo Alto, California, United States
Summary
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Rockstar🎓
Top SchoolShiori Sagawa is a member of technical staff at OpenAI with six years of experience at the intersection of machine learning research and scientific computing. With a PhD pursuit in Computer Science at Stanford and a dual BA in Computer Science and Molecular & Cell Biology from UC Berkeley, she blends rigorous academic training with hands-on engineering. Her background includes scientific roles at D. E. Shaw Research and multiple Berkeley labs, lending deep domain knowledge in data-driven experimentation. An active contributor to the WILDS benchmark on GitHub, she has improved evaluation metrics and dataset-specific evaluations—showing attention to robustness under real-world distribution shifts. Based in Palo Alto, she brings both research-grade rigor and practical implementation skills to production ML systems.
6 years of coding experience
5 years of employment as a software developer
Bachelor of Arts (B.A.), Computer Science, Molecular and Cell Biology, Bachelor of Arts (B.A.), Computer Science, Molecular and Cell Biology at University of California, BerkeleyDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Stanford University
Github Skills (6)
pytorch10
machine-learning10
python10
evaluation10
metric10
data-analysis10
Programming languages (3)
Github contributions (5)
p-lambda/wilds
Dec 2020 - Aug 2021
A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.
Role in this project:
ML Engineer
ML EngineerContributions:5 reviews, 67 commits, 19 PRs in 7 months
Contributions summary:Shiori primarily contributed to the development and improvement of machine learning model evaluation within the `wilds` repository, which is a benchmark for in-the-wild distribution shifts. Their commits include cleaning up and addressing edge cases in metric calculations. They also introduced evaluations for the FMoW dataset and updated split labels, demonstrating a focus on dataset-specific evaluation implementations. Additionally, the user updated the dataset configuration to use a process_outputs_function.
loadersshiftswildmachine-learningmachine-learning-benchmark
ssagawa/overparam_spur_corr
Jul 2020 - Jul 2020
Contributions:4 commits, 2 pushes, 1 branch in 1 day
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