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
- Oct 2024: Gave a guest lecture on intro to ML privacy at Northeastern University CS7375 (slides)
- Jul/Aug 2024: Gave three talks at Google around LLM training data membership, privacy, and unlearning, respectively
- June 2024: Spent summer at Google DeepMind Privacy & Security team
- May 2024: Wrote a long post on machine unlearning (tweet); the field is rapidly evolving and clarity is much needed!
- Jun 2024: Press coverage and interview by Politico
- May 2024: Discussed unlearning as a guest on The Data Exchange Podcast
- May 2024: People seem interested over on Hacker News
- May 2024: At ICLR'24 presenting on data contamination and fairness of LoRA. Received a best paper award at the DPFM Workshop and postered at SeT LLM, R2-FM, ME-FoMo, PML4LRS workshops.
- Aug 2023: Taught programming & algorithms in Ethiopia as part of AddisCoder 🇪🇹!
- July 2023: Gave a talk at the NITRD Privacy R&D Interagency Working Group of the US government
- 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 $120,000)!
- Mar 2023: See news by White House, UK Government, Summit for Democracy, DrivenData, NSF, and CMU
- May 2023: We wrote a blog post about it on ML@CMU. See the extended version if you're feeling adventurous.
- Mar 2023: Our research on distributed DP [1,2] is officially deployed to Android and featured on the Google AI blog
Research
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Machine Unlearning in 2024
Ken Ziyu Liu An edcuational and position piece Blog Post / PDF version / BibTeX / Tweet / Hacker News / Podcast / Politico |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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Stanford Artificial Intelligence Laboratory (SAIL), Stanford CA, United States PhD student, 2023-Present |
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Carnegie Mellon School of Computer Science, Pittsburgh PA, United States Research Assistant (RI/MLD), 2021-2023 |
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Google Research (remote from Sydney) AI Resident Researcher, 2020-2021 (Left early for grad school deferred from 2020) |
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Facebook, Menlo Park CA, United States Software Engineer Intern, Messenger/Instagram Ranking, Winter 2019/2020 (Summer in 🦘🇦🇺) |
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Amazon Web Services, Sydney, Australia Software Engineer Intern, Safety Engineering, Winter 2018/2019 (Summer in 🦘🇦🇺) |
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Apple, Cupertino CA, United States Software Engineer Intern, Core OS, Summer 2018 |
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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
- While not feeling the AGI, I try to read, travel, do olympic weightlifting, bake cheesecakes, and Dota 2, among other things
- I co-organize the weekly lunch/seminar for Stanford ML group
- I'll be performing Waltz/Polka as part of the Stanford Viennese Ball Openning Committee!
- Twitter, Mastodon, Instagram, Goodreads
- My Erdős number is 4 via three paths
- Consider using JAX! It's a beautiful thing
- Check out cs-sop.org if you're a prospective CS PhD applicant; I have benefited from this initiative and I have shared my statement there too
Visits since COVID: