Ken Liu
Global Health Education Coordinator
San Jose, California, United States
Summary
👤
Senior🎓
Top SchoolKen Liu is a PhD student and researcher focused on privacy-preserving machine learning and federated analytics, currently based in Palo Alto and pursuing doctoral work at Stanford while a Student Researcher on Google DeepMind's Privacy/Security team. Over eight years, he has engineered and advanced federated learning systems, implementing differentially private secure aggregation with the Distributed Discrete Gaussian mechanism and integrating it with TensorFlow Privacy, with practical applications to the EMNIST dataset. His career spans CMU's RI/MLD lab, a Google AI Resident role, and industry stints at Apple, AWS, and Meta, giving him hands-on experience from research prototypes to production ML tooling. He is an active open-source contributor, with notable work on the google-research/federated project and related privacy tooling. Based in the Bay Area, he combines rigorous academic training—MS in Robotics/CS from CMU and PhD studies at Stanford—with a track record of shipping ML improvements in large-scale systems. He is also active on X (Twitter), using public channels to share insights from his research and development work.
9 years of coding experience
University of California, DavisBachelor of Medicine, Bachelor of Surgery (M.B.B.S.), Bachelor of Medicine, Bachelor of Surgery (M.B.B.S.) at Peking University Health Science CenterBurlingame High School
Github Skills (8)
tensorflow210
machine-learning10
differential-privacy10
tensorflow10
federated-learning10
python10
algorithms9
data-analysis8
Programming languages (3)
Github contributions (5)
google-research/federated
Apr 2021 - Jul 2021
A collection of Google research projects related to Federated Learning and Federated Analytics.
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 4 comments in 3 months
Contributions summary:Ken contributed to the development and enhancement of federated learning algorithms, specifically focusing on differentially private mechanisms for secure aggregation within the context of federated learning. Their work involved implementing and refining the Distributed Discrete Gaussian (DDGauss) mechanism, modifying existing code and integrating with the TensorFlow Privacy library. The user also refactored and extended existing code and integrated the new model into the EMNIST dataset.
analyticsfederated-analyticsmachine-learningkubernetesfederated-learning
kenziyuliu/ChineseCheckersAgent
Mar 2018 - Sep 2019
Contributions:37 commits, 43 PRs, 45 pushes in 1 year 6 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.