Yuying Huang
Software Engineer at Amazon
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolYuying Huang is a software engineer with 11 years of experience who blends robotics research and production engineering, recently joining Amazon after a year at a stealth startup. She holds an MS in Robotics & AI from Stanford (3.98 GPA) and has driven research that raised affordance prediction accuracy from 30% to 90% and cut simulation data generation time by half. Her hands-on work spans real-robot control (Franka), CUDA-accelerated human pose estimation, and motion planning implemented in Python and C++, with a CoRL 2023 submission on articulated object manipulation. She also contributes to open-source ML tooling, improving accessibility in scikit-learn visualizations, and brings cross-industry internship experience from Omron, Siemens, and Pratt & Whitney. Known for pragmatic algorithm design and fast prototyping, she thrives at the intersection of vision, control, and scalable implementation.
11 years of coding experience
2 years of employment as a software developer
Bachelor of Engineering (BEng) Mechanical Engineering, Bachelor of Engineering (BEng) Mechanical Engineering at McGill UniversityThe Chinese University of Hong Kong (CUHK)Master of Science - MS Mechanical Engineering, Master of Science - MS Mechanical Engineering at Stanford University
Chinese, English, French
Github Skills (9)
scikit10
data-visualizations10
machine-learning10
data-visualization10
data-visualisation10
python10
matplotlib10
scikit-learn10
data-science9
Programming languages (3)
Github contributions (5)
scikit-learn/scikit-learn
Oct 2015 - Oct 2015
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:11 commits, 13 PRs, 18 comments in 1 day
Contributions summary:Yuying primarily contributed to enhancing the visual clarity and accessibility of plots within the scikit-learn library. This involved modifying various example scripts to ensure colorblind compatibility in visualizations. The changes primarily impacted examples related to linear models, support vector machines, tree-based models, and Gaussian mixture models, promoting better readability and understanding of model results. Further contributions included minor visual adjustments to plots for consistency, such as switching from `plot` to `scatter` for visual clarity.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
johannah/iceview
Oct 2015 - Jun 2017
Contributions:58 commits, 41 pushes, 1 branch in 1 year 8 months
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