About

I’m a PhD student at Stanford Computer Science, in part supported by a Stanford School of Engineering Fellowship. Previously, I was an MS student at Carnegie Mellon University (RI/MLD) working on differential privacy and federated learning. I was extremely fortunate to be co-advised by Virginia Smith, Artur Dubrawski, and Steven Wu.

Before grad school, I was at Google AI/Research working on distributed differential privacy algorithms [1, 2] and led the efforts to deploy them to Android devices and TensorFlow. I had the fortune to work closely with Peter Kairouz, Jakub Konečný, Thomas Steinke, and Naman Agarwal. I did my undergrad at University of Sydney, where I worked with Wanli Ouyang and received First Class Honours and the University Medal. While there, I designed and shipped various things at Apple, AWS, and Meta.

I like to build, understand, and apply machine learning methods that are simple and practical. I’m currently interested in the intersection of ML and privacy/security. I’m also interested in efficient ML systems, computer vision, and a bit of CS theory.

Blog / GitHub / Google Scholar / LinkedIn / Twitter

Recent News

Research & Papers

(*equal contribution, alphabetical authorship)
Differentially Private Adaptive Optimization with Delayed Preconditioners
Tian Li, Manzil Zaheer, Ziyu Liu, Sashank Reddi, Brendan McMahan, Virginia Smith
ICLR 2023: International Conference on Learning Representations
Oral Presentation at OPT 2022 of NeurIPS'22
PDF / BibTeX / Code
On Privacy and Personalization in Cross-Silo Federated Learning
Ziyu Liu, Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
NeurIPS 2022: Conference on Neural Information Processing Systems
Presented at TPDP 2022 of ICML'22
PDF / BibTeX / Code / Poster
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ziyu Liu, Zheng Xu, Virginia Smith
Preprint
Presented at FL-NeurIPS'22
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 of ACM CCS'21
PDF / BibTeX / Code / Talk 1 / Talk 2 / Poster / Slides
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 of ICML'21
Full PDF / Short PDF / BibTeX / Code / Talk / Poster / Slides
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
Presented at RL Theory Workshop of ICML'20
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

Teaching

I enjoy teaching! Most recently, I have been part of the teaching team of AddisCoder, an intensive summer school in Ethiopia for middle/high school students interested in programming and computer science.

While an undergrad at USyd, I was a teaching assistant (academic tutor) for the following classes:

Professional Service

  • Reviewer for ICLR 2024: International Conference on Learning Representations
  • PC member for FL-ICML'23: Workshop on Federated Learning and Analytics in Practice
  • Reviewer for NeurIPS 2023: Conference on Neural Information Processing Systems
  • Reviewer for ICCV 2023: International Conference on Computer Vision
  • Reviewer for ICML 2023: International Conference on Machine Learning
  • Reviewer for CVPR 2023: IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • Reviewer for AISTATS 2023: International Conference on Artificial Intelligence and Statistics
  • Reviewer for NeurIPS 2022: Conference on Neural Information Processing Systems
  • Reviewer for TIP 2022: IEEE Transactions on Image Processing
  • Reviewer for ECCV 2022: European Conference on Computer Vision
  • Reviewer for CVPR 2022: IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • Reviewer for IJCV 2021: International Journal of Computer Vision

Experience

Carnegie Mellon University, Pittsburgh PA, United States
Research Assistant (RI/MLD), 2021-2023
Google Research (remote from Sydney)
AI Resident Researcher, 2020-2021 (Left early for grad school deferred from 2020)
Facebook, Menlo Park CA, 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 CA, United States
Software Engineer Intern, Core OS, Summer 2018

☕ Misc