Yuan Tang is a Senior Software Engineer at Meta with 11 years of experience building and hardening production systems, currently focused on ranking ads for Instagram. He blends machine learning and backend expertise—developing recommendation and spatial-temporal models at Alibaba and improving ad relevance at Meta—while contributing security-sensitive C++ crypto code (EdDSA support) to Facebook’s Fizz TLS library. His open-source work includes XSS hardening and tests for the widely used TEAMMATES education tool, reflecting a pragmatic attention to code quality and safety. A Carnegie Mellon ECE master's graduate with a strong NTU undergraduate record, he pairs research-grade CV and NLP experience with real-world product deployments at scale.
11 years of coding experience
4 years of employment as a software developer
Hangzhou Xuejun High School
Bachelor's degree, Electrical and Electronic Engineering, Minor in Mathematics, 4.8/5.0, Bachelor's degree, Electrical and Electronic Engineering, Minor in Mathematics, 4.8/5.0 at Nanyang Technological University
Master's degree, Electrical and Computer Engineering, Master's degree, Electrical and Computer Engineering at Carnegie Mellon University
Contributions summary:Yuan primarily contributed to the implementation of cryptographic functionalities, specifically focusing on TLS-1.3 within the Fizz library. Their work included adding support for EdDSA (Ed25519/Ed448) keys, which involved creating validation methods, signing, and verification functions. The user also integrated these cryptographic features within the Token Binding extension, demonstrating a deep understanding of security protocols and their application within the TLS context.
This is the project website for the TEAMMATES feedback management tool for education
Role in this project:
Back-end Developer & QA Engineer
Contributions:10 commits, 3 PRs, 10 comments in 15 days
Contributions summary:Yuan's contributions primarily focused on improving the quality and security of the application. They removed redundant code, comments, and print statements in the FeedbackSessionsLogicTest, demonstrating attention to code quality. They also introduced XSS protection by sanitizing user inputs on instructor course enroll pages and in the data displayed on the results pages. Furthermore, they added tests to verify the XSS prevention.
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