Huy Nghiem
I am a PhD student in Computer Science at the University of Maryland, College Park, advised by Professor Hal Daumé III. My research focuses on Safety, Robustness and Trustworthiness in AI, and on applying NLP systems to social problems. I am currently a Research Fellow at the Cambridge Boston Alignment Initiative, working on technical AI governance with Professor Dylan Hadfield-Menell at MIT, and I was a MATS 9.0 & 9.1 scholar with Dr. Noam Kolt, auditing how LLMs align with legal requirements. My recent and upcoming work investigates and strengthens the robustness of Large Language Models (LLMs) in tasks that require complex reasoning. Before UMD, I earned my Master's degree at the University of Southern California and conducted research in social media under Dr. Fred Morstatter and Dr. Emilio Ferrara. I also worked at the Children's Data Network as a Data Scientist, investigating and enhancing public policy in the child welfare system.
In my free time, I enjoy casual hiking along scenic routes 🌲, exploring quaint towns ⛰️ and electrifying cities 🗽, sipping matcha 🍵 while reading manga or scientific articles...
If you are interested in collaborating in causal reasoning or using AI agents for social good, please feel free to reach out.
News & Updates
- 10/2026 🧭 Joined the Cambridge Boston Alignment Initiative as a Research Fellow, working with Professor Dylan Hadfield-Menell at MIT.
- 09/2026 🎉 First-authored paper accepted to the NeurIPS 2026 Evaluation & Datasets Track! Preprint coming soon.
- 08/2026 💼 Completed my Applied Scientist internship at Oracle in New York City.
- 07/2026 🎉 Bias in the Tails accepted to EMNLP 2026 in Budapest!
- 06/2026 📄 New paper: Trait-space Monitoring for Emergent Misalignment During Supervised Finetuning.
- 04/2026 ✍️ First blog on finetuning and Emergent Misalignment out! 🔥
- 04/2026 🎉 2 papers accepted to ACL 2026!
Blog
- 04/2026Why Some Models Shatter and Others Don't: The Geometry of Emergent Misalignment Across ArchitecturesFinetuning 4 model families on 7 perturbation types, we find alignment drift follows one shared axis, yet the same drift yields up to 4x different emergent misalignment rates across architectures.
Selected Publications





