Hoai Chau held a BSc at the University of Science Ho Chi Minh City. He is currently working as a Research Assistant at VinUniversity under the guidance of Prof. Doan Dang Khoa and Prof. Heng Ji (UIUC). His research interest is low-resource and deep-learning model compression.
His current research focuses on techniques to compress Transformer-based models, including Large Language Model (LLMs) and Vision Transformers (ViTs). This includes approaches like quantization, token merging, KV cache compression, and developing more efficient decoding algorithms.
Deep Learning
Efficient AI
DetectVul: A statement-level code vulnerability detection for Python
Hoai-Chau Tran, Anh-Duy Tran, Kim-Hung Le
North-Holland, 2025
Accelerating Transformers with Spectrum-Preserving Token Merging
Hoai-Chau Tran, Duy MH Nguyen, Duy M Nguyen, Trung-Tin Nguyen, Ngan Le, Pengtao Xie, Daniel Sonntag, James Y Zou, Binh T Nguyen, Mathias Niepert
2024
Energy Minimizing-based token merging for accelerating Transformers
Hoai-Chau Tran, Duy Minh Ho Nguyen, Manh-Duy Nguyen, Ngan Hoang Le, Binh T Nguyen