About Me

I’m currently an MS research student at Carnegie Mellon University, co-advised by Prof. Virginia Smith and Prof. Artur Dubrawski and also working with Prof. Steven Wu. Before CMU, I spent a gap year at Google Research where I was fortunate to work closely with Peter Kairouz and Jakub Konečný on federated learning, with amazing collaborators Thomas Steinke and Naman Agarwal. At Google, I drove the development efforts that led to the deployment of our distributed DP via SecAgg algorithms on mobile devices. Before that, I did my undergrad in computer science at the University of Sydney, where I was advised by Prof. Wanli Ouyang and received First-Class Honours and the University Medal. I’ve also worked on various things at Apple, AWS, and Facebook.

I’m interested in developing practical and useful intelligent systems. My current research explores privacy-preserving machine learning via federated learning and differential privacy. I’m also interested in computer vision, deep learning, and theory.

Email / LinkedIn / GitHub / Google Scholar

Research

(*equal contribution, alphabetical authorship)
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ziyu Liu, Zheng Xu, Virginia Smith
Preprint
PDF / BibTeX / Code
On Privacy and Personalization in Cross-Silo Federated Learning
Ziyu Liu, Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
Preprint
To appear at TPDP 2022
PDF / BibTeX / Code
The Skellam Mechanism for Differentially Private Federated Learning
Naman Agarwal, Peter Kairouz, Ziyu Liu
NeurIPS 2021: Conference on Neural Information Processing Systems
Oral Presentation at PPML 2021
PDF / BibTeX / Code / Talk 1 / Talk 2
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
Peter Kairouz, Ziyu Liu, Thomas Steinke
ICML 2021: International Conference on Machine Learning
Oral Presentation at TPDP 2021
PDF / Short Ver. / BibTeX / Code / Talk
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
Meng Zhou*, Ziyu Liu*, Pengwei Sui, Yixuan Li, Yuk Ying Chung
NeurIPS 2020: Conference on Neural Information Processing Systems
PDF / BibTeX / Code
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
Ziyu Liu, Hongwen Zhang, Zhenghao Chen, Zhiyong Wang, Wanli Ouyang
CVPR 2020: Conference on Computer Vision and Pattern Recognition
Oral Presentation
PDF / Supp / BibTeX / Demo / Code Star

Experience

Google Research
AI Resident Researcher, 2020-2021 (Left early for grad school deferred from 2020)
Facebook, Menlo Park, United States
Software Engineer Intern, Messenger Ranking, Winter 2019/2020 (Summer in 🦘🇦🇺🌏)
Amazon Web Services, Sydney, Australia
Software Engineer Intern, Safety Engineering, Winter 2018/2019 (Summer in 🦘🇦🇺🌏)
Apple, Cupertino, United States
Software Engineer Intern, Core OS, Summer 2018

Professional Service

I’m serving as a conference/journal reviewer for:

  • NeurIPS 2022 (Datasets & Benchmarks Track): Conference on Neural Information Processing Systems
  • TIP 2022: IEEE Transactions on Image Processing
  • ECCV 2022: European Conference on Computer Vision
  • CVPR 2022: IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • IJCV 2021: International Journal of Computer Vision

Teaching

I have been a teaching assistant for the following courses at the University of Sydney: