I am currently a student at the College of William and Mary. I am fortunate to be supervised by Anh Totti Nguyen, Hy Truong Son, and Thiago Serra. My research focuses on explainable and trustworthy AI, specifically quantifying and understanding the limitations and biases of LLMs.

Previously, I was an intern at the Machine Learning Research team at CodaMetrix in Summer 2024 and Summer 2025, where I developed LLM agents that (1) extract medical entities from EHR notes and (2) evaluate and correct entities extracted by human experts and other LLMs.

Selected Publications

♠ denotes equal contribution

Vision-Language Models are Biased

An Vo^♠, Khai-Nguyen Nguyen^♠, Mohammad Reza Taesiri, Vy Tuong Dang, Anh Totti Nguyen, Daeyoung Kim

AI for Math Workshop@ ICML 2025, Submitted to NeurIPS 2025

paper / website / dataset

We demonstrate that state-of-the-art LLMs are strongly biased toward well-known patterns and propose VLMBias, a VQA benchmark focusing on evaluating visual biases in VLMs.

Sentiment Reasoning for Healthcare

Khai-Nguyen Nguyen^♠, Khai Le-Duc^♠, Bach Phan Tat, Duy Le, Long Vo-Dang, Truong-Son Hy

ACL 2025, Industry Track (Oral)

paper

We demonstrate that chain-of-thought distillation improves LLMs performance in sentiment analysis and enables LLMs to produce human-like explanation.

Medical Spoken Named Entity Recognition

Khai Le-Duc, David Thulke, Hung-Phong Tran, Long Vo-Dang, Khai-Nguyen Nguyen, Truong-Son Hy, Ralf Schluter

NAACL 2025, Industry Track (Oral)

paper

We propose a multilingual dataset for the medical named entity recognition task.

Real-time Speech Summarization for Medical Conversations

Khai Le-Duc^♠, Khai-Nguyen Nguyen^♠, Long Vo-Dang, Truong-Son Hy

Interspeech 2024 (Oral)

paper

We improve cascaded medical speech summarization LLMs using high-quality synthetic data.

Getting away with more network pruning: From sparsity to geometry and linear regions

Jeffrey Cai^♠, Khai-Nguyen Nguyen^♠, Nishant Shrestha, Aidan Good, Ruisen Tu, Xin Yu, Shandian Zhe, Thiago Serra

Workshop on Sparsity in Neural Networks, ICLR 2023 & CPAIOR 2023

paper

We propose a mathematical theorem of the geometric properties of neural networks and apply it to model pruning.