About
Welcome to my homepage! My full name is Duc-Minh Le, you can call me Minh 👋. I am currently a first year CS PhD at Georgia Tech, working with Professor Celine Lin. Previously, I was a research resident at Qualcomm AI Research 🤖, where I had the privilege of being advised by Professor Nhat Ho 🏛️. I earned my Bachelor’s degree in Computer Science from Hanoi University of Science and Technology.
Email: minh611002@gmail.com
Research Interests
My research focuses on advancing Artificial Intelligence and Machine Learning toward systems that can learn and adapt continuously and efficiently. I am particularly interested in Parameter-Efficient Fine-Tuning, Mixture of Experts, and Continual Learning, and I am excited to explore related directions that enhance the scalability, robustness, and practical impact of modern AI models.
(*) denotes equal contribution.
Recent News
- [Aug 2026] I officially start my PhD at Georgia Tech.
- [Jan 2026] Two papers are accepted to ICLR 2026 and one paper is accepted to Neurocomputing.
Selected Preprints
Leveraging Hierarchical Taxonomies in Prompt-based Continual Learning
Under review
Quyen Tran, Hoang Phan*, Minh Le*, Tuan Truong, Dinh Phung, Linh Ngo, Thien Nguyen, Nhat Ho, Trung Le
Selected Publications on Continual Learning
One-Prompt Strikes Back: Sparse Mixture of Experts for Prompt-based Continual Learning
Proceedings of the ICLR, 2026
Minh Le, Bao-Ngoc Dao, Huy Nguyen, Quyen Tran, Anh Nguyen, Nhat Ho
WAVE++: Capturing Within-Task Variance for Continual Relation Extraction with Adaptive Prompting
Neurocomputing, 2026
Bao-Ngoc Dao*, Quang Nguyen*, Luyen Ngo Dinh*, Minh Le*, Nam Le, Linh Ngo Van
Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective
Proceedings of the AAAI Conference on Artificial Intelligence 39, 2025 (Oral)
Minh Le*, Tien Ngoc Luu*, An Nguyen The*, Thanh-Thien Le, Trang Nguyen, Thanh Tung Nguyen, Linh Ngo Van, Thien Huu Nguyen
Mixture of Experts Meets Prompt-Based Continual Learning
Advances in NeurIPS, 2024
Minh Le, An Nguyen*, Huy Nguyen*, Trang Nguyen*, Trang Pham*, Linh Van Ngo, Nhat Ho
Selected Publications on Efficient AI
Revisit Visual Prompt Tuning: The Expressiveness of Prompt Experts
Proceedings of the ICLR, 2026
Minh Le*, Anh Nguyen*, Huy Nguyen, Chau Nguyen, Anh Tran, Nhat Ho
RepLoRA: Reparameterizing Low-rank Adaptation via the Perspective of Mixture of Experts
Proceedings of the ICML, 2025
Tuan Truong*, Chau Nguyen*, Huy Nguyen*, Minh Le, Trung Le, Nhat Ho
On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation
Proceedings of the ICML, 2025
Nghiem T. Diep*, Huy Nguyen*, Chau Nguyen*, Minh Le, Duy M. H. Nguyen, Daniel Sonntag, Mathias Niepert, Nhat Ho
Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts
Proceedings of the ICLR, 2025
Minh Le*, Chau Nguyen*, Huy Nguyen*, Quyen Tran, Trung Le, Nhat Ho
Professional Services
Conference Reviewer: ICML 2025, NeurIPS 2025, ICLR 2026, CVPR 2026, ICML 2026, ECCV 2026