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
- July 2023: Spent five weeks teaching programming & algorithms in Ethiopia as part of AddisCoder!
- July 2023: Gave a talk at NITRD Privacy R&D Interagency Working Group
- June 2023: Gave a talk about model personalization at SWIFT
- May 2023: Gave a talk about our entry to the PETs challenge at the Royal Society in London, UK
- Mar 2023: I led our awesome CMU team ("puffle") to win 1st place at the US-UK Privacy-Enhancing Technologies (PETs) Prize Challenge, Pandemic Forecasting Track (USD $100,000). See news by the White House, UK Government, Summit for Democracy, DrivenData, NSF, and CMU news!
- May 2023: We wrote a blog post about privacy and personalization in cross-silo FL and the recent PETs challenge on ML@CMU. Check out the extended version if you're feeling adventurous.
- Mar 2023: Our research on distributed differential privacy for federated learning [1,2] is officially deployed to Android and featured on the Google AI blog!
- Jan 2023: DP2 is accepted to ICLR'23 and also presented as an oral presentation at OPT 2022. Check out our code!
- Nov 2022: I'll be in-person at NeurIPS in New Orleans, excited to meet people around! We are showcasing three papers: private silos, Motley, and DP2.
- Aug 2022: Gave a talk about private cross-silo FL at Google Research and presented it at TPDP 2022
Research & Papers
![]() | 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
- In my free time, I try to read, travel, lift weights, bake cheesecakes, and Dota 2, among other things
- On the side, I advised/part-timed with DynamoFL, a YC startup working on federated learning
- Twitter, Mastodon, Instagram
- My Erdős number is 4 via three paths
- Consider using JAX! It's a beautiful thing