About

I’m a 1st-year PhD student at Stanford Computer Science and Stanford AI Lab, rotating with Prof. Percy Liang as part of Stanford NLP and previously with Prof. Sanmi Koyejo as part of STAIR. I’m supported in part by Stanford School of Engineering Fellowship.

Before Stanford, I did my MS thesis at Carnegie Mellon University (RI/MLD) on differential privacy and federated learning and led the CMU team to a 1st place at the US-UK PETs Prize. I was very fortunate to be co-advised by Prof. 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 worked closely with Peter Kairouz, Jakub Konečný, Thomas Steinke, and Naman Agarwal. Before Google, I did my undergrad at University of Sydney, where I worked with Wanli Ouyang and received First Class Honours and the University Medal. During my undergrad, I designed and shipped various things at Apple, AWS, and Meta.

I am broadly interested in the privacy, security, efficiency, and personalization aspects of current AI systems and their interplay. More generally, I like to build, understand, and apply machine learning methods and systems that are simple, practical, and trustworthy.

Blog / GitHub / Google Scholar / LinkedIn / Twitter

Feel free email me for research, collaborations, or a casual chat, especially if you are a junior, disadvantaged, or underrepresented student. Please consider giving me anonymous feedback.

Recent News

Research & Papers

(*equal contribution, alphabetical authorship)
Investigating Data Contamination for Pre-training Language Models
Minhao Jiang, Ken Ziyu Liu, Ming Zhong, Rylan Schaeffer, Siru Ouyang, Jiawei Han, Sanmi Koyejo
Tech Report
PDF / BibTeX / Code / Tweet
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 / Blog Post
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 & Mentoring

I love teaching! Most recently, I was part of the teaching team of AddisCoder 2023 🇪🇹, an intensive summer school in Ethiopia for middle/high school students interested in programming and computer science. I helped create lab exercises, gave lab lectures, and graded exams. I was also the main IT guy responsible for managing 100+ lab machines and making sure students can do exercises under poor technical infrastructure.

I’m also involved in the following teaching/mentoring activities:

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

Professional Service

  • PC member for Private ML-ICLR'24: Privacy Regulation and Protection in Machine Learning Workshop
  • Reviewer for ICML 2024: International Conference on Machine Learning
  • 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

Stanford Artificial Intelligence Laboratory (SAIL), Stanford CA, United States
PhD student, 2023-Present
Carnegie Mellon School of Computer Science, 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


Visits since COVID: