Gabriel Goh
Research Scientist at OpenAI
Iowa, United States
Summary
🤩
Rockstar🎓
Top SchoolGabriel Goh is a research scientist with 11 years of experience applying numerical optimization and machine learning to real-world systems, currently focused on interpretability and large models at OpenAI. His background includes distributed deep learning work at Apple and a PhD in Mathematics from UC Davis, blending rigorous theory with production-scale engineering. He contributes to notable open-source projects like tensorflow/lucid, adding CLIP models and interpretability tooling that bridge research models and serving infrastructure. Gabriel’s interdisciplinary interests span computer graphics, VR, and game development, which inform his practical approach to visualization and model insight. Colleagues describe him as someone who moves seamlessly between low-level numerical methods and high-level model behavior, making complex systems more understandable and reliable. Based in Iowa, he brings a research-first mindset to engineering problems with a knack for turning interpretability research into deployable tools.
10 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, Mathematics, Doctor of Philosophy - PhD, Mathematics at University of California, DavisMathematics, Mathematics at Davis
English
Github Skills (6)
machine-learning10
tensorflow10
python10
interpretation9
visualizations7
visualization7
Programming languages (6)
Github contributions (5)
tensorflow/lucid
Aug 2019 - Mar 2021
A collection of infrastructure and tools for research in neural network interpretability.
Role in this project:
ML Engineer
ML EngineerContributions:4 reviews, 14 commits, 9 PRs in 1 year 7 months
Contributions summary:Gabriel contributed to the `tensorflow/lucid` repository by making several modifications related to the modelzoo and interpretability tools. They added a new CLIP model and made changes related to saving and serving URLs. The user also updated imports and adjusted code to fix minor inconsistencies within the project's codebase. Their work focused on expanding the models available within the project and ensuring compatibility with serving infrastructure.
pytorchinterpretabilitydeep-learningjupyter-notebookinfrastructure
gabgoh/epcalc
Mar 2020 - Jan 2021
Epidemic Calculator
Contributions:17 commits, 15 pushes, 3 branches in 10 months
calculatorjavascriptepidemic
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