Cory Stephenson
Staff Research Scientist at Databricks Mosaic Research
San Francisco Bay Area United States
Summary
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Rockstar🎓
Top SchoolCory Stephenson is a Staff Research Scientist based in the San Francisco Bay Area with a PhD in Physics and about four years of focused experience in machine learning research and engineering. He has progressed from academic physics research into applied ML roles at Intel and Databricks Mosaic Research, developing practical algorithms for audio source separation, traffic anomaly detection, and scalable model-training tools. Cory contributes to open-source ML tooling—adding implementations like CutMix and user-facing tutorials to the widely used mosaicml/composer library—showing a blend of production-minded engineering and research rigor. His background in physics and numerical methods informs a principled approach to complex systems and experimental validation, and he has a track record of shipping both novel algorithms and approachable documentation for practitioners.
4 years of coding experience
15 years of employment as a software developer
Bachelor of Science (BS) Physics, Bachelor of Science (BS) Physics at Arizona State UniversityDoctor of Philosophy (PhD) Physics, Doctor of Philosophy (PhD) Physics at University of Illinois Urbana-Champaign
Github Skills (8)
neural-network10
pytorch10
machine-learning10
deeplearning-ai10
deep-learning10
python10
artificial-neural-networks10
documentation8
Programming languages (1)
Github contributions (5)
mosaicml/composer
Nov 2021 - Nov 2022
Supercharge Your Model Training
Role in this project:
ML Engineer
ML EngineerContributions:106 reviews, 28 commits, 43 PRs in 11 months
Contributions summary:Cory contributed several methods and enhancements to the `composer` library, which focuses on supercharging model training. The user implemented the CutMix method for data augmentation, adding a new algorithm for improved model performance. Further contributions involved improving the documentation and adding a notebook tutorial for custom methods. These actions demonstrate a focus on expanding the library's capabilities for machine learning training.
pytorchml-systemsdeep-learningneural-networksmachine-learning
coryMosaicML/composer
Nov 2021 - Aug 2024
Composing methods for ML training efficiency
Contributions:366 pushes, 50 branches in 2 years 9 months
machine-learningml-trainingtraining
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