Ethan Goolish
Senior Data Scientist ML Engineer
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolEthan Goolish is a Senior Data Scientist and ML Engineer based in Mountain View with eight years of experience building high-performance machine learning systems and leading algorithm teams. At Apple he serves as algorithms tech lead for Safety on CoreMotion, driving crash-detection models for iOS and watchOS in production. His background in applied math, statistics, and CS from Cornell underpins a strong research-to-production pathway—evident from CUDA/C++ contributions to NVIDIA RAPIDS (including Holt-Winters time-series work in cuML) that achieved multi-fold speedups over CPU baselines. He has a track record of improving numerical methods and scaling big-data ML workflows with tools like Dask, cuDF, and XGBoost, and has published computational analysis on sparse Fourier averaging. Comfortable both in low-level performance engineering and ML model leadership, he brings a reputation for refactoring complex codebases and shipping reliable, tested APIs. Colleagues describe him as someone who blends rigorous theory with pragmatic engineering to make models run faster and safer at scale.
8 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Applied Mathematics, Statistical Science, Computer Science, Bachelor's degree, Applied Mathematics, Statistical Science, Computer Science at Cornell University
Vietnamese, Spanish
Github Skills (10)
cuda10
nvidia10
machine-learning10
time-series10
machine-learning-algorithms10
cuml10
gpu10
data-analysis10
python9
cython9
Programming languages (4)
Github contributions (5)
rapidsai/cuml
Jun 2019 - Aug 2019
cuML - RAPIDS Machine Learning Library
Role in this project:
ML Engineer
ML EngineerContributions:30 commits, 1 PR, 2 comments in 1 month
Contributions summary:Ethan's contributions primarily involve modifications to the HoltWinters time series forecasting algorithm within the cuML library. The commits show the user refactoring and refactoring header files, and combined source files. These changes indicate efforts to integrate and refine the HoltWinters functionality, suggesting a focus on improving the machine learning capabilities of the cuML library. The user also updated Cython bindings and performed testing, with added docstrings.
cudacumlnvidiadata-sciencegpu
egoolish/notebooks
Jul 2019 - Aug 2019
RAPIDS Sample Notebooks
Contributions:4 PRs, 16 pushes in 27 days
jupyter-notebooknotebookmachine-learningnotebooksrapids
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