Huy Nghiem

Huy Nghiem
PhD Student in AI, NLP and Computational Sociology

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

Blog

Selected Publications

HireWatch: Evaluating LLM Compliance with U.S. Employment Law under Contextual Pressure
Huy Nghiem, M. Kostylew, Noam Kolt
NeurIPS 2026, Evaluation & Datasets Track
Huy Nghiem, Phuong-Anh Nguyen-Le, Sy-Tuyen Ho, Hal Daumé III
EMNLP 2026
Subtle race-gender evaluative framing in LLM-generated resume summaries undermines hiring fairness.
Huy Nghiem, Advik Sachdeva, Hal Daumé III
ACL 2026
Data-efficient toxicity detection via LLM-based synthetic explanations.
Huy Nghiem, Swetasudha Panda, Devashish Khatwani, Huy V. Nguyen, Krishnaram Kenthapadi, Hal Daumé III
ML4H 2025★ Best Paper
Simple and modular iterative frameworks to improve safety of healthcare AI assistants post-deployment via preference optimization
Huy Nghiem, Phuong Anh Nguyen-Le, John Prindle, Rachel Rudinger, Hal Daumé III
EMNLP 2025
How LLMs contextualize socioeconomic factors in college admissions.
Huy Nghiem, John Prindle, Jieyu Zhao, Hal Daumé III
EMNLP 2024
Our empirical results indicate a preference among these models for hiring candidates with White female-sounding names over other demographic groups across 40 occupations