About

I’m a computer science Ph.D. student at Stanford University 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’m also a part-time advisor to AlphaXiv and to MLCommons.

I’m broadly interested in building tools to understand, democratize and safeguard the benefits from modern AI systems. This has led me to an eclectic set of work under AI Safety, intellectual property & copyright, behavioral specification, and building fully open source language models.

News

Research

	Extracting books from production language models Ahmed Ahmed, A. Feder Cooper, Sanmi Koyejo, Percy Liang arXiv 2025 Website / arXiv / Press Coverage / Reddit / TL;DR We show that copyrighted text can be extracted from production LLMs (Claude, GPT, Gemini, Grok) despite safety measures. Using a two-phase extraction procedure with optional jailbreaks, we achieve near-verbatim extraction of entire books in some cases (95.8% recall on Claude 3.7 Sonnet).
	Marin: A Fully Open-Weight Language Model David Hall, Ahmed Ahmed, ..., Percy Liang 2025 8B: Blog Post / Google Blog 32B: Blog Post / Retrospective
	Extracting memorized pieces of (copyrighted) books from open-weight language models A. Feder Cooper, Aaron Gokaslan, Ahmed Ahmed, Amy B. Cyphert, Christopher De Sa, Mark A. Lemley, Daniel E. Ho, Percy Liang arXiv 2025 arXiv / Press Coverage / TL;DR We measure memorization of 50 books in 17 open-weight LLMs. Most models don't memorize most books, but Llama 3.1 70B entirely memorizes some books like Harry Potter and 1984. Using just the first few tokens as a seed, we can deterministically generate the entire first Harry Potter book near-verbatim. Our results have significant implications for copyright cases, though not ones that unambiguously favor either side.
	AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons Shaona Ghosh, Heather Frase, Adina Williams, ..., Ahmed Ahmed, ..., Peter Mattson, Percy Liang, Joaquin Vanschoren arXiv 2025 arXiv
	Independence Tests for Language Models Sally Zhu*, Ahmed Ahmed*, Rohith Kuditipudi*, Percy Liang ICML 2025 SPOTLIGHT - TOP 2.6% arXiv
	SpecEval: Evaluating Model Adherence to Behavior Specifications Ahmed Ahmed, Kevin Klyman, Yi Zeng, Sanmi Koyejo, Percy Liang arXiv 2025 arXiv
	Introducing v0.5 of the AI Safety Benchmark from MLCommons Bertie Vidgen, Adarsh Agrawal, Ahmed Ahmed, ..., Percy Liang, Peter Mattson, Joaquin Vanschoren arXiv 2024 arXiv
	HELM Safety: Towards Standardized Safety Evaluations of Language Models Farzaan Kaiyom, Ahmed Ahmed, Yifan Mai, Kevin Klyman, Rishi Bommasani, Percy Liang November 2024 Website
	Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning Archit Sharma, Ahmed Ahmed, Rehaan Ahmad, Chelsea Finn CoRL 2023: Conference on Robot Learning Website / Paper / Code
	Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL Bogdan Mazoure, Ahmed Ahmed, Patrick MacAlpine, R Devon Hjelm, Andrey Kolobov ICLR 2022: International Conference on Learning Representations arXiv / Code

Outreach

Outside of my research, I’m passionate about addressing issues of diversity and inclusion in academia at large. To this end I’ve worked on improving outreach and inclusion in CS research through my work as mentor CURIS, the Stanford CS department’s REU program. I helped spearhead initiatives such as the CURIS fellows program, aimed to provide research opportunities for historically underrepresented students and PURE which provides research funding for First-Generation/Low-Income students.

Stanford CS DEI Town Hall

Website template adapted from Ken Liu. Thanks Ken!