About
Welcome to my homepage! My full name is Duc-Minh Le, you can call me Minh 👋. I am currently an AI Engineer at Trivita AI. 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.
I’m actively seeking PhD positions in Computer Science for the upcoming academic year and excited to collaborate on impactful research! 🚀
(*) denotes equal contribution.
Recent News
- [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
Towards Rehearsal-Free Continual Relation Extraction: Capturing Within-Task Variance 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
On the Expressiveness of Visual 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