About

Welcome to my homepage! I’m Huy Nguyen, a third-year Ph.D candidate at the Department of Statistics and Data Sciences, The University of Texas at Austin where I am fortunate to be advised by Professor Nhat Ho and Professor Alessandro Rinaldo. Before that, I graduated from Ho Chi Minh City University of Science with a Bachelor’s degree in Mathematics (Summa Cum Laude). In Summer 2024, I worked as a research intern at Microsoft AI.

I co-organize the Statistical Machine Learning seminar at UT Austin (StatML@UT).

Email: huynm@utexas.edu

Research Interests

My current research focuses on theoretical foundations for the Mixture-of-Experts models. In particular, I investigate the effects of various gating functions (namely the softmax gate, the Top-K sparse softmax gate, the dense-to-sparse gate, the sigmoid gate, etc) on the convergence of expert estimation under the Mixture-of-Experts models. Based on insights from these results, I aim to design novel gating functions and characterize expert networks which help improve the efficiency and scalability of the Mixture-of-Experts applications, including Large Language Models, Multi-modal Learning and Parameter-efficient Fine-Tuning. Additionally, I am also interested in Optimal Transport theory.

(*) denotes equal contribution, (**) denotes equal advising.

Selected Publications on the Theory of Mixture of Experts

[T.1] Convergence Rates for Softmax Gating Mixture of Experts. Under review (Part of it has been in the Proceedings of the ICML, 2024)
Huy Nguyen, Nhat Ho**, Alessandro Rinaldo* *

[T.2] Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts. Advances in NeurIPS, 2024
Huy Nguyen, Nhat Ho**, Alessandro Rinaldo* *

[T.3] Sigmoid Self-Attention is Better than Softmax Self-Attention: A Mixture-of-Experts Perspective. Under review
Huy Nguyen*, Fanqi Yan*, Pedram Akbarian, Nhat Ho**, Alessandro Rinaldo* *

[T.4] Demystifying Softmax Gating Function in Gaussian Mixture of Experts. Advances in NeurIPS, 2023 (Spotlight)
Huy Nguyen, TrungTin Nguyen, Nhat Ho

[T.5] Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts?. Proceedings of the ICML, 2024
Huy Nguyen, Pedram Akbarian, Nhat Ho

[T.6] Statistical Advantages of Perturbing Cosine Router in Mixture of Experts. Proceedings of the ICLR, 2025
Huy Nguyen, Pedram Akbarian*, Trang Pham*, Trang Nguyen*, Shujian Zhang, Nhat Ho

[T.7] Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts. Proceedings of the ICLR, 2024
Huy Nguyen, Pedram Akbarian, Fanqi Yan, Nhat Ho

[T.8] On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functions. Under review
Huy Nguyen*, Xing Han*, Carl William Harris, Suchia Saria**, Nhat Ho* *

[T.9] Quadratic Gating Functions in Mixture of Experts: A Statistical Insight. Under review
Pedram Akbarian*, Huy Nguyen*, Xing Han*, Nhat Ho

[T.10] A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts. Proceedings of the ICML, 2024
Huy Nguyen, Pedram Akbarian, TrungTin Nguyen, Nhat Ho

[T.11] Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts. In AISTATS, 2024
Huy Nguyen*, TrungTin Nguyen*, Khai Nguyen, Nhat Ho

[T.12] Understanding Expert Structures on Minimax Parameter Estimation in Contaminated Mixture of Experts. In AISTATS, 2025
Fanqi Yan*, Huy Nguyen*, Dung Le*, Pedram Akbarian, Nhat Ho

[T.13] On Parameter Estimation in Deviated Gaussian Mixture of Experts. In AISTATS, 2024
Huy Nguyen, Khai Nguyen, Nhat Ho

Selected Publications on the Applications of Mixture of Experts

[A.1] FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion. Advances in NeurIPS, 2024
Xing Han, Huy Nguyen*, Carl Harris*, Nhat Ho, Suchi Saria

[A.2] 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

[A.3] 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

[A.4] CompeteSMoE - Effective Training of Sparse Mixture of Experts via Competition. Under review
Quang Pham, Giang Do, Huy Nguyen, TrungTin Nguyen, Chenghao Liu, Mina Sartipi, Binh T. Nguyen, Savitha Ramasamy, Xiaoli Li, Steven Hoi, Nhat Ho

[A.5] Adaptive Prompt: Unlocking the Power of Visual Prompt Tuning. Under review
Minh Le*, Anh Nguyen*, Huy Nguyen, Chau Nguyen, Nhat Ho

[A.6] RepLoRA: Reparameterizing Low-rank Adaptation via the Perspective of Mixture of Experts. Under review
Tuan Truong*, Chau Nguyen*, Huy Nguyen*, Minh Le, Trung Le, Nhat Ho

[A.7] On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation. Under review
Nghiem T. Diep*, Huy Nguyen*, Chau Nguyen*, Minh Le, Duy M. H. Nguyen, Daniel Sonntag, Mathias Niepert, Nhat Ho

Selected Publications on Optimal Transport

[O.1] Entropic Gromov-Wasserstein between Gaussian Distributions. Proceedings of the ICML, 2022
Huy Nguyen*, Khang Le*, Dung Le*, Dat Do, Tung Pham, Nhat Ho

[O.2] On Multimarginal Partial Optimal Transport: Equivalent Forms and Computational Complexity. In AISTATS, 2022
Huy Nguyen*, Khang Le*, Khai Nguyen, Tung Pham, Nhat Ho

[O.3] On Robust Optimal Transport: Computational Complexity and Barycenter Computation. Advances in NeurIPS, 2021
Huy Nguyen*, Khang Le*, Quang Minh Nguyen, Tung Pham, Hung Bui, Nhat Ho

[O.4] Fast Approximation of the Generalized Sliced-Wasserstein Distance. IEEE ICASSP, 2024
Huy Nguyen*, Dung Le*, Khai Nguyen*, Trang Nguyen*, Nhat Ho

Recent News

• [Jan 2025] Three papers on Mixture of Experts are accepted to ICLR 2025 ([1], [2]) and AISTATS 2025 ([3]).
• [Dec 2024] I was recognized as a top reviewer at NeurIPS 2024. I was also promoted to PhD candidate at UT Austin.
• [Oct 2024] Four new papers on Mixture of Experts are out, [1], [2], [3] and [4].
• [Sep 2024] Three papers on Mixture of Experts, [1], [2] and [3], are accepted to NeurIPS 2024. See you in Vancouver, Canada this December!
• [May 2024] I start my research internship at Microsoft AI where I will work on the applications of Mixture of Experts in Large Language Models.
• [May 2024] Three new papers on Mixture of Experts [1], [2] and [3] are out!
• [May 2024] Three papers on Mixture of Experts, [1], [2] and [3], are accepted to ICML 2024.
• [Apr 2024] I was offered the AISTATS 2024 registration grant. See you in Valencia, Spain this May!
• [Mar 2024] I received the ICLR 2024 Travel Award. See you in Vienna, Austria this May!
• [Feb 2024] Two new papers on the applications of Mixture of Experts in Medical Images [1] and Large Language Models [2] are out!
• [Feb 2024] Two new papers on the theory of Mixture of Experts, [1] and [2], are out!
• [Jan 2024] Two papers on Mixture of Experts, [1] and [2], are accepted to AISTATS 2024.
• [Jan 2024] Our paper “Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts” is accepted to ICLR 2024.
• [Dec 2023] Our paper “Fast Approximation of the Generalized Sliced-Wasserstein Distance” is accepted to ICASSP 2024.
• [Oct 2023] I received the NeurIPS 2023 Scholar Award. See you in New Orleans this December!
• [Oct 2023] Our new paper “A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts” is out.
• [Sep 2023] Our new paper “Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts” is out.
• [Sep 2023] We have two papers accepted to NeurIPS 2023, [1] as spotlight and [2] as poster.
• [Jul 2023] We will present the paper “Fast Approximation of the Generalized Sliced-Wasserstein Distance” at the Frontier4LCD workshop, ICML 2023.
• [May 2023] Three new papers on the Mixture of Experts theory are out! See more at [1], [2] and [3].
• [Feb 2023] Our new paper on Mixture Models theory “Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models” is out.

Professional Services

• Conference Reviewer: ICML (2022-2025), NeurIPS (2022-2025), AISTATS (2022-2025), ICLR (2024-2025), and AAAI (2025).
• Journal Reviewer: Electronic Journal of Statistics, Transactions on Machine Learning Research.