Chenhui Hu
Sr. Manager, Machine Learning at Zscaler
United States
Summary
🤩
Rockstar🎓
Top SchoolChenhui Hu is a senior machine learning leader with nine years of experience translating advanced research into production AI that secures enterprises and drives product revenue. With a Ph.D. from Harvard and early honors degrees from Shanghai Jiao Tong University, he has built large-scale forecasting and predictive systems at Microsoft and led ML teams at Zscaler focused on AI-driven app segmentation, DLP, and ZPA. He combines deep technical breadth—from neuroimaging signal processing to recommendation-system hyperparameter tuning and AzureML deployment—with strong product instincts that helped win major cloud deals and accelerate customer adoption. An active contributor to open-source best practices in forecasting and recommender experiments, he pairs rigorous academic training with practical engineering to move models from prototype to production. Notably, his background spans cybersecurity, e-commerce, and healthcare, reflecting a rare ability to apply ML across highly regulated and operationally critical domains.
9 years of coding experience
14 years of employment as a software developer
Cross-registration MIT 6.342 - Wavelet Approximation and Compression; MIT 18.338 - Eigenvalues of Random Matrices, Cross-registration MIT 6.342 - Wavelet Approximation and Compression; MIT 18.338 - Eigenvalues of Random Matrices at Massachusetts Institute of TechnologyDoctor of Philosophy (Ph.D.) Electrical Engineering and Computer Science, Doctor of Philosophy (Ph.D.) Electrical Engineering and Computer Science at Harvard UniversityMaster's degree Electrical Engineering, Master's degree Electrical Engineering at Shanghai Jiao Tong University
English
Github Skills (12)
hyperparameter-tuning10
jupyter-notebook10
machine-learning10
recommendation-system10
azure-machine-learning10
python10
data-science10
ranking9
pandas9
deep-learning8
deeplearning-ai8
keras7
Programming languages (7)
Github contributions (5)
recommenders-team/recommenders
Jun 2019 - Jun 2019
Best Practices on Recommendation Systems
Role in this project:
ML Engineer
ML EngineerContributions:10 commits, 1 PR, 5 pushes in 14 days
Contributions summary:Chenhui primarily focused on modifying and optimizing a hyperparameter tuning experiment within a Jupyter Notebook. Their commits involved updating dataset preparation, adjusting evaluation metrics, and correcting input arguments for a model selection process. They also made changes to the `reco_utils/azureml/svd_training.py` file, including adjustments to ranking metrics and input parameters. These changes indicate a focus on enhancing the performance and configuration of recommendation system models using AzureML.
recommendation-systemspythonjupyter-notebookoperationalizationmicrosoft
microsoft/forecasting
Jul 2018 - Jun 2020
Time Series Forecasting Best Practices & Examples
Contributions:1 release, 21 reviews, 287 commits in 1 year 11 months
forecastingpythontime-series-analysisjupyter-notebookmodel-deployment
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