About

I am Huy Nguyen, a final-year Ph.D. candidate in Statistics at The University of Texas at Austin, where I am fortunate enough to be advised by Prof. Nhat Ho and Prof. Alessandro Rinaldo.

My research develops statistical foundations for modern AI, with a particular focus on mixture-of-experts (MoE), multimodal learning, and efficient adaptation of large-scale models, which is organized around three closely connected themes:

• Statistical understanding of modern AI architectures. I study how gating mechanisms, expert structures, and routing rules determine statistical sample complexity and expert specialization in MoE. Beyond MoE, I also investigate the statistical properties of self-attention mechanisms, including how different attention formulations affect sample efficiency.
• Statistical principles for AI architecture design. I study how these statistical insights can be translated into architectural principles for modern AI systems. This perspective motivates new designs for sparse MoE, multimodal learning, and attention-based models that aim to improve scalability, specialization and efficiency.
• Efficient adaptation of large-scale models. I investigate how the structure and capacity of lightweight adaptation mechanisms affect the efficiency of adapting large pretrained models. My work focuses on methods such as low-rank adaptation and prompt-based tuning, with an emphasis on their statistical efficiency and principled design.

Recent News

• [Sep 2026] I was selected as a recipient of the Outstanding Graduate Research Fellowship at UT Austin.
• [Aug 2026] I will serve as a Senior Program Committee member for AAAI 2027.
• [Jun 2026] Our new paper On the Geometry of Separation in Finite Gaussian Mixtures is available on arXiv.
• [May 2026] I was recognized as an ICML 2026 Silver Reviewer.
• [Jan 2026] Two papers on prompt-based tuning (1, 2) were accepted to ICLR 2026.
• [Dec 2025] Our paper Convergence Rates for Softmax Gating Mixture of Experts was accepted to IEEE Transactions on Information Theory.
• [Sep 2025] Our paper On Minimax Estimation of Parameters in Softmax-Contaminated Mixture of Experts was accepted to NeurIPS 2025.
• [May 2025] Two papers on parameter-efficient adaptation (1, 2) were accepted to ICML 2025.

Industrial Experience

• Morgan Stanley, Machine Learning Research — Summer Associate, Summer 2026.
• Microsoft AI — Research Intern, Summer 2024.

Professional Service

• Senior Program Committee: AAAI 2027.
• Conference Reviewer: ICML (2022–2026), NeurIPS (2022–2026), AISTATS (2022–2026), ICLR (2024–2027), AAAI (2025–2026).
• Journal Reviewer: Journal of Machine Learning Research (JMLR), Electronic Journal of Statistics (EJS), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Transactions on Machine Learning Research (TMLR).
• Seminar Co-organizer: StatML@UT, The University of Texas at Austin.