tongy-msft
Bellevue, Washington, United States
Summary
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RockstarTong Y is a software engineer with four years of experience building cloud and ML-focused products, currently contributing to Messenger Youth at Meta after a multi-year tenure on Responsible AI and Azure ML teams at Microsoft. He holds an MS in Electrical Engineering from Columbia and has a strong background in computer vision and model optimization from internships and research roles, including layer-wise DNN pruning work with a patent pending. Tong has practical full-stack experience improving developer-facing AI tooling—his contributions to Microsoft’s Responsible AI Toolbox enhanced feature-importance visualizations and UI debugability—bridging frontend usability with ML model diagnostics. Based in Bellevue, WA, he blends research-driven rigor with product engineering at scale, often surfacing detailed exceptions and UX limits to make complex AI systems more interpretable for stakeholders.
4 years of coding experience
Github Skills (18)
javascript10
error-detection10
typescript10
user-interface10
ui-design10
uid10
front-end-development10
error-log10
typescript-types10
error-handling10
typescripts10
react10
data-visualizations9
data-visualisation9
data-visualization9
Programming languages (3)
Github contributions (5)
microsoft/responsible-ai-toolbox
Oct 2021 - Jan 2023
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:2 releases, 360 reviews, 80 commits in 1 year 3 months
Contributions summary:Tongy-msft primarily focused on improving the user interface and functionality of the Responsible AI Toolbox. Their contributions include bug fixes in React components related to feature importance visualization, specifically addressing issues with data display. They also made enhancements to the user interface, returning detailed exception messages to the UI for debugging, and improved usability by limiting feature selection. Further improvements involved updates and refactoring of core components, showing a focus on improving the user experience.
fairness-mlwidgetpythonmonitordeveloper-tools
Azure/AzureMLResponsibleAI
Sep 2022 - Oct 2022
A repository to hold information about the preview of Azure Machine Learning's Responsible AI and model evaluations tools
Contributions:3 reviews, 1 comment in 6 days
evaluationsmachine-learningazure-machine-learningresponsible-aiazure
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