๐Ÿ‘‹ About Me

Iโ€™m currently an MS research student at Carnegie Mellon University working on privacy-preserving machine learning, 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 was fortunate to work closely with Peter Kairouz and Jakub Koneฤnรฝ on differentially private federated learning, with amazing collaborators Thomas Steinke and Naman Agarwal. At Google, I led the efforts to deploy our distributed DP via SecAgg algorithms [1, 2] to 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 also worked on various things at Apple, AWS, and Facebook.

Iโ€™m interested in developing and understanding intelligent systems that are simple, practical, and socially responsible. My current research focuses on privacy-preserving machine learning via federated learning and differential privacy. Iโ€™m also interested in computer vision, deep learning, and theory.

Email / LinkedIn / GitHub / Google Scholar / CV (Nov 2022)

๐Ÿฅ Recent News

  • Dec 2022: DP2 is now on arXiv and was selected as an oral presentation at OPT 2022
  • Nov 2022: Will be in-person at NeurIPS in New Orleans, excited to meet people around! We are showcasing three papers: private silos, Motley, and DP2.
  • Sept 2022: Our paper on private cross-silo FL will be presented at NeurIPS this year
  • Aug 2022: Gave a talk about private cross-silo FL at Google Research and presented it at TPDP 2022
  • Jun 2022: We released Motley, a benchmark for heterogeneous and personalized federated learning and will present it at FL-NeurIPS'22 [code]

๐Ÿ“„ Papers

(*equal contribution, alphabetical authorship)
Differentially Private Adaptive Optimization with Delayed Preconditioners
Tian Li, Manzil Zaheer, Ziyu Liu, Sashank Reddi, Brendan McMahan, Virginia Smith
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
(Camera-ready version available)
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
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 / Slides / Poster
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
PDF / Short Ver. / BibTeX / Code / Talk / Poster
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

๐ŸŽ“ Professional Service

Iโ€™m serving as a conference/journal reviewer for:

  • 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

๐Ÿ’ผ 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 ๐Ÿฆ˜๐Ÿ‡ฆ๐Ÿ‡บ)
Amazon Web Services, Sydney, Australia
Software Engineer Intern, Safety Engineering, Winter 2018/2019 (Summer in ๐Ÿฆ˜๐Ÿ‡ฆ๐Ÿ‡บ)
Apple, Cupertino, United States
Software Engineer Intern, Core OS, Summer 2018

๐Ÿง‘โ€๐Ÿ’ป Teaching

I have been a teaching assistant / tutor for the following courses 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

โ˜• Misc