Xin Huang
Technical Project Manager at KonnectONE(via Elink-Elite)
Richardson, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolHuang Xin is a Senior Applied Scientist at AWS in New York with nine years of experience applying statistical rigor and machine learning to production problems across finance and cloud AI. A PhD candidate in Statistics at UT Dallas, he blends deep academic training (3.90 GPA) with hands-on work—contributing SageMaker demo notebooks for LightGBM, CatBoost, AutoGluon and TabTransformer to a widely used amazon-sagemaker-examples repo. His background spans quantitative research at JPMorgan, time-series and image-analysis internships, and building reproducible ML demos that help bridge research and deployment. Known for turning complex probabilistic models into practical solutions, he also brings advanced data-visualization and automation experience from earlier analytics roles.
9 years of coding experience
3 years of employment as a software developer
Master of Science - MS, computer science, Master of Science - MS, computer science at The University of Texas at DallasBachelor's degree, Energy&Dynamic Engineering, 3.4, Bachelor's degree, Energy&Dynamic Engineering, 3.4 at Chengdu University of Technology
Chinese, English
Github Skills (11)
amazon-sagemaker10
machine-learning10
jupyter-notebook10
catboost10
data-science10
lightgbm10
python9
deep-learning8
aws8
trainings7
inference7
Programming languages (2)
Github contributions (5)
aws/amazon-sagemaker-examples
Mar 2022 - Jan 2023
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:37 reviews, 8 commits, 38 PRs in 10 months
Contributions summary:Xin added demo notebooks for SageMaker JumpStart, specifically for tabular classification and regression models using LightGBM and CatBoost. They also included notebooks for AutoGluon and TabTransformer algorithms. Their contributions focused on providing practical examples of how to build, train, and deploy machine learning models with Amazon SageMaker. The user was responsible for demo notebook development, code formatting, and typo corrections.
pythonjupyter-notebooktrainingawssagemaker
Neo9061/amazon-sagemaker-examples
Mar 2022 - Mar 2024
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
Contributions:89 pushes, 31 branches in 2 years
sagemakerpythonamazon-sagemakermachine-learning-deploydata-science
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