Yangsibo Huang
I’m a research scientist at Google NYC. Before that, I was a Ph.D. student and Wallace Memorial Fellow at Princeton University, co-advised by Prof. Kai Li and Prof. Sanjeev Arora.
I work at the intersection of machine learning, systems, and policy. My research explores how and why machine learning systems may go wrong, through the lenses of privacy and copyright violation, as well as security and safety concerns. Recently, I am also interested in model memorization and its implications on capabilities.
✰ Awards
- 🏆 The best paper award at NeurIPS’24-SoLaR, for our work An Adversarial Perspective on Machine Unlearning for AI Safety
- 🏆 The best paper award at ICLR’24-SeT LLM, for our work Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications
- 🏅 Rising Star in EECS in 2023
- 🏅 Wallace Memorial Fellowship (2023-2024)
- 📃 Winner of Meta’s Privacy-Enhancing Technologies call for proposals in 2022
🎙 Recent Talks
-
Open Technical Questions in GenAI Copyright
- 08/08/2024, Open AGI Symposium @ Columbia University
- 10/16/2024, Workshop on Alignment, Trust, Watermarking, and Copyright Issues in LLMs @ Simons Institute
-
Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy
- 09/24/2024, Unlearning Society @ Google DeepMind
-
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning
- 08/18/2024, PPML Workshop @ Crypto 2024
-
Identifying, Understanding, and Mitigating Failure Modes of Safety Alignment in Large Language Models
- 04/24/2024, VITA-group Talk Series @ UT Austin
- 04/30/2024, ML Series @ Microsoft Research, New England
-
Detecting Pretraining Data from Large Language Models
- 12/15/2023, Workshop on Regulatable ML @ NeurIPS 2023
✎ Selected Publications
Please refer to full publications or my Google Scholar profile for the full list. “(α)” stands for alphabetical order (with “⁺” standing for lead), “*” stands for equal contribution.
- Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards📍 Preprint 2025
- MUSE: Machine Unlearning Six-Way Evaluation for Language Models📍 ICLR 2025
- SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal Behaviors📍 ICLR 2025
㋡ Experiences
- [10/22-12/23] Google AI, Research Intern, hosted by Chiyuan Zhang and Badih Ghazi
- [05/22-09/22] Meta AI, Research Intern, hosted by Seyi Feyisetan and Alexandre Sablayrolles
- [09/18-04/19] Harvard Medical School, Research Intern, supervised by Prof. Quanzheng Li
- [06/18-08/18] David Geffen School of Medicine at UCLA, Research Intern, supervised by Prof. Ke Sheng
♥ Service
- Organizer of Large Language Model Memorization Workshop (L2M2) at ACL 2025
- Area Chair for
- Theoretical Foundations of Foundation Models (co-located with ICML 2024)
- Program Committee member for
- Generative AI + Law (GenLaw) ’24 (co-located with ICML 2024)
- Privacy Regulation and Protection in Machine Learning (co-located with ICLR 2024)
- Federated Learning and Analytics in Practice (co-located with ICML 2023)
- Interpretable Machine Learning in Healthcare (co-located with ICML 2021, 2022)
- Computer Vision for Automated Medical Diagnosis (co-located with ICCV 2021)
- Reviewer for ICML (2022, 2023, 2024), NeurIPS (2021, 2022, 2023), ICLR (2024), COLM (2024)
ꐕ MISC
- In my spare time, I mainly stay with my four cats 😺😻😼😽.
- I enjoy reading books about psychology.