George Ho
Senior Machine Learning Scientist at Flatiron Health
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolGeorge Ho is a Senior Machine Learning Scientist in New York with a decade of experience building production ML systems and NLP solutions for finance and healthcare. He blends Bayesian probabilistic modeling and practical NLP know-how—having worked on HMC/NUTS improvements in PyMC and applied NLP research as a Quantitative Researcher at Point72—with hands-on engineering polish from contributions to projects like Aesara, PyMC4, Alphalens, and Pyfolio. Comfortable across research and backend code, he favors maintainable, well-documented solutions and has a track record of refactoring core libraries and extending probabilistic tooling. Known among collaborators as someone who “runs remote processes,” he pairs rigorous statistical thinking with pragmatic software hygiene and a taste for strong coffee.
9 years of coding experience
5 years of employment as a software developer
Bachelor of Science in Engineering, Bachelor of Science in Engineering at The Cooper Union for the Advancement of Science and ArtChinese International School | 漢基國際學校
Stackoverflow
56reputation
81reached
1answer
0questions
Github Skills (30)
data-visualizations10
probabilistic-programming10
mc10
python10
optimizers10
pandas10
statistics10
optimizer10
data-visualisation10
statistic10
numpy10
pep10
mcmc10
pymc10
p810
Programming languages (13)
Github contributions (5)
pymc-devs/pymc4
Jan 2019 - Dec 2019
Experimental PyMC interface for TensorFlow Probability. Official work on this project has been discontinued.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:32 commits, 32 PRs, 47 pushes in 11 months
Contributions summary:George primarily focused on extending the PyMC4 library by wrapping TensorFlow Probability (TFP) distributions. Their work involved defining and implementing new random variables, and they refactored existing code to improve maintainability and readability. They added docstrings and fixed typos, suggesting a focus on improving the user experience with the library. The user also contributed to adding more distributions supported by the library.
pythonpymcmachine-learningdiscontinuedprobability
quantopian/pyfolio
Jun 2017 - Jan 2019
Portfolio and risk analytics in Python
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:104 commits, 24 PRs, 94 pushes in 1 year 7 months
Contributions summary:George focused on implementing a simple tearsheet feature, adding functionality for summary performance statistics and plots. They added comments, tests, and made edits to adhere to coding standards, encapsulating the tearsheet behavior in a function and swapping out graphs. Their contributions included adding benchmark returns as an input and incorporating images for the README file, demonstrating an understanding of performance analysis visualization and code quality.
riskanalyticspython
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