πŸ‘‹ About

I’m currently an MS research student (MSR) at Carnegie Mellon University working on the intersection of machine learning and privacy & security. I’m very fortunate to be co-advised by Prof. Virginia Smith and Prof. Artur Dubrawski and also working with Prof. Steven Wu.

Before CMU, I spent a year at Google Research where I led the efforts to deploy our distributed DP via SecAgg algorithms [1, 2] to mobile devices and as part of TensorFlow. I had the fortune to work closely with Peter Kairouz, Jakub Konečný, Thomas Steinke, and Naman Agarwal.

Before Google, I did my undergrad in computer science at University of Sydney, was advised by Prof. Wanli Ouyang, and received First-Class Honours and the University Medal. I also designed, built, and shipped various things at Apple, AWS, and Meta.

I like to build and understand ML methods and systems that are simple, practical, and can be responsibly deployed in the real world. My current research focuses on ML privacy, primarily working with differential privacy and federated learning. I’m also interested in deep learning, ML systems, computer vision, and theory.

Email / LinkedIn / GitHub / Google Scholar / Twitter

πŸ₯ Recent News

πŸ“„ 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

πŸŽ“ Professional Service

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

  • ICCV 2023: International Conference on Computer Vision
  • ICML 2023: International Conference on Machine Learning
  • CVPR 2023: IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • AISTATS 2023: International Conference on Artificial Intelligence and Statistics
  • NeurIPS 2022: 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

πŸ’Ό Experience

Carnegie Mellon University, Pittsburgh, United States
Research Assistant (RI/MLD), 2021-present
Google Research (remote from Sydney)
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 πŸ¦˜πŸ‡¦πŸ‡Ί)
Amazon Web Services, Sydney, Australia
Software Engineer Intern, Safety Engineering, Winter 2018/2019 (Summer in πŸ¦˜πŸ‡¦πŸ‡Ί)
Apple, Cupertino, United States
Software Engineer Intern, Core OS, Summer 2018

πŸ§‘β€πŸ’» Teaching

I have been a teaching assistant (academic tutor) for the following classes while an undergrad at USyd:

  • COMP9110 System Analysis and Modelling, 2020
  • COMP3027 Algorithm Design, 2018
  • SOFT2412 Agile Software Development Practices, 2019
  • COMP2017 Systems Programming, 2018
  • INFO1103 Intro to Programming, 2017

β˜• Misc