Liuyi Yao
算法工程师 at Alibaba Group
Buffalo-Niagara Falls Area United States
Summary
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Rockstar🎓
Top SchoolLiuyi Yao is an algorithm engineer with seven years of experience specializing in machine learning and privacy-aware systems, currently working at Alibaba Group. He holds a Ph.D. in Computer Science from the University at Buffalo and began his career bridging academia and industry through research and internships, including IBM. Liuyi contributes to open-source federated learning tooling—most notably improvements to Alibaba’s FederatedScope attack module, where he fixed GAN loss bugs and implemented membership inference features—highlighting a focus on adversarial analysis and reproducible experiments. Based in the Buffalo-Niagara region, he combines a strong statistical foundation from Nanjing University with practical engineering that moves privacy research toward production-ready platforms.
7 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at University at Buffalo Graduate SchoolBachelor's degree, Statistics, Bachelor's degree, Statistics at Nanjing University
Github Skills (7)
pytorch10
machine-learning10
federated-learning10
python10
dcgan9
cgan9
documentation7
Programming languages (2)
Github contributions (5)
alibaba/FederatedScope
Apr 2022 - Nov 2022
An easy-to-use federated learning platform
Role in this project:
ML Engineer
ML EngineerContributions:31 reviews, 16 commits, 24 PRs in 7 months
Contributions summary:Liuyi's commits primarily focus on modifications and improvements within the federated learning framework, specifically targeting the attack module. They addressed a bug related to loss calculations in the GAN-based attack and improved documentation for the attack module. The user also implemented features related to membership inference attacks and included examples of loss comparison for in/out cases, and updated the configurations file.
federated-learningmachine-learningpytorch
Osier-Yi/FederatedScope
Aug 2022 - Nov 2024
An easy-to-use federated learning platform
Contributions:53 pushes, 16 branches in 2 years 3 months
federated-learning
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