About

I’m a 2nd-year Computer Science Ph.D. student at Stanford University and a Student Researcher at Google DeepMind. I’m grateful to be advised by Prof. Percy Liang and Prof. Sanmi Koyejo. I’m part of Stanford AI Lab, Stanford NLP, Stanford ML, and Stanford Trustworthy AI Research (STAIR). I also worked with Prof. Dan Boneh at Applied Cryptography Group.

Before Stanford, I did my MS thesis at Carnegie Mellon University (RI/MLD) where I led the CMU team to 1st place at the US-UK Privacy-Enhancing Technologies Challenge. I was very fortunate to be advised by Prof. Virginia Smith, Artur Dubrawski, and Steven Wu. I did my undergrad in CS at University of Sydney and received First Class Honours and the University Medal. I also spent some time at Google Research working on distributed differential privacy and led the efforts to deploy them to Android and TensorFlow; I was lucky to work with Peter Kairouz, Jakub Konečný, Thomas Steinke, and Naman Agarwal.

I think broadly about modern AI systems; how they may (or may not) be private, secure, and trustworthy; and the implications thereof. This intersects areas such as unlearning, membership, memorization, localization, personalization, distributed learning, fairness, and AI safety.

Blog / GitHub / Google Scholar / LinkedIn / Twitter

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

News and Olds

Research

(*equal contribution, alphabetical/random authorship)
Machine Unlearning in 2024
Ken Ziyu Liu
An edcuational and position piece
Blog Post / PDF version / BibTeX / Tweet / Hacker News / Podcast / Politico
On Fairness of Low-Rank Adaption of Large Models
Zhoujie Ding*, Ken Ziyu Liu*, Pura Peetathawatchai, Berivan Isik, Sanmi Koyejo
COLM 2024: Conference on Language Modeling
PDF / BibTeX / Code / Tweet
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
Best Paper Award & Oral Presentation at DPFM @ ICLR'24
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 @ 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 @ 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 @ 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 @ 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 @ 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 with student admissions, created 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:

Experience

Google DeepMind, Mountain View CA, United States
Student Researcher, 2024-Present
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/Instagram 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

Professional Service

  • Reviewer for TMLR: Transactions on Machine Learning Research
  • Program Committee 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
  • Program Committee 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

☕ Misc


Visits since COVID: