Daniel Shao
Software Engineer at CertiK
New Haven, Connecticut, United States
Summary
🤩
Rockstar🎓
Top SchoolDaniel Shao is a software engineer with five years of experience specializing in machine learning tooling, model compression, and large-model optimization, currently building production-grade systems at CertiK in New Haven. He has a strong academic foundation from Yale (BS Computer Science) and a curious humanities background from Harvard in Chinese Language and Literature, which informs his clear technical communication. His open-source contributions include meaningful fixes and feature additions to high-profile projects like Microsoft NNI and Colossal-AI, improving PyTorch tracing, conv1d compression support, activation checkpointing, and torchaudio integration. Daniel’s work emphasizes robustness and testing—resolving CPU/GPU tracing issues and adding comprehensive test coverage for complex modules. He blends research experience in computer architecture and clustering with hands-on engineering to make large AI models faster and more reliable. Notably, his GitHub handle (@GabbyKoki) and cross-disciplinary education hint at a developer who pairs technical depth with unconventional perspectives.
5 years of coding experience
1 year of employment as a software developer
Chinese Language and Literature, Chinese Language and Literature at Harvard UniversityBachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Yale UniversityHigh School Diploma, High School Diploma at Choate Rosemary Hall
Github Skills (15)
testing10
pytorch10
machine-learning10
deeplearning-ai10
deep-learning10
automated-machine-learning10
python10
ai10
neural-architecture-search10
large-scale9
parallelization9
data-parallelism8
data-parallel8
distributed-computing8
tensorflow3
Programming languages (7)
Github contributions (5)
hpcaitech/ColossalAI
Jul 2022 - Jan 2023
Making large AI models cheaper, faster and more accessible
Role in this project:
ML Engineer
ML EngineerContributions:214 reviews, 47 commits, 96 PRs in 6 months
Contributions summary:Daniel's commits focus on enhancing the Colossal-AI library for large AI models. They contributed to code style improvements for existing modules, specifically in the learning rate scheduler. The user added comprehensive testing capabilities, particularly concerning the integration of the library with the torchaudio module. Additionally, the user implemented and tested activation checkpointing strategies, which are crucial for memory optimization in large-scale models.
aideep-learninghpclarge-scaledata-parallelism
microsoft/nni
Jan 2023 - Jun 2023
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Role in this project:ML Engineer
ML EngineerContributions:42 reviews, 23 PRs, 3 pushes in 5 months
Contributions summary:Daniel's commits primarily focused on modifications and improvements to the NNI (Neural Network Intelligence) toolkit, an open-source AutoML platform. They addressed issues related to model compression, specifically fixing bugs and enhancing the `concrete_trace` functionality, which is used to trace and analyze PyTorch models within NNI. The user also contributed to the code base by adding support for `conv1d` compression, allowing dummy input as a dictionary, and resolving issues related to CPU/GPU mixing during tracing, improving the robustness and features of the tool. Furthermore, the user fixed minor issues discovered during testing and updated the MMDetection version, enhancing the usability of the toolkit.
automlfeature-engineeringmachine-learningmodel-compressionneural-architecture-search
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