Hello, I'm Huy Nguyen

I am a Ph.D. researcher specializing in the systems-level architecture and distributed acceleration of Large Language Models (LLMs). My work focuses on pushing the physical limits of hardware utilization, from bare-metal CUDA/Cutlass kernel engineering, to designing topology-aware 5D parallel training engines. I build the underlying computational and cross-GPU interconnect infrastructure required to eliminate network bubbles and compute bottlenecks at massive scale.

Prior to this, I completed my B.S. in Computer Science at Hanoi University of Science and Technology with an Excellent Degree (GPA 3.71/4.0), advised by Dr. Linh Ngo Van.


News

  • Summer 2026: I received a research internship offer from Amazon for Summer 2026.
  • Jun 2025 - Sep 2025: I completed a Research Internship at Microsoft Research, working on GPU kernel optimization for LLM inference.
  • Sept 2024: I started my Ph.D. in Computer Science at the University of Oregon, advised by Assoc. Prof. Thien Huu Nguyen.
  • Mar 2024: I implemented a faster RoPE embedding for Unsloth (PR #238).
  • Jan 2024: We introduced Vistral, a state-of-the-art conversational LLM for Vietnamese.
  • Oct 2023: Two papers were accepted at EMNLP 2023.
  • July 2022: I joined VinAI Research as a Research Resident.

Selected Publications

Towards Fast and Accurate Modeling for Cross-Lingual Label Projection

ACL 2026

Fast and accurate modeling for cross-lingual label projection.

Massively Multilingual Instruction-Following Information Extraction

ACL 2025 Findings

Multilingual instruction-following for information extraction.

Taipan: Efficient and Expressive State Space Language Models with Selective Attention

Arxiv

Efficient and expressive state space language models with selective attention.

Transitioning Representations between Languages for Cross-lingual Event Detection via Langevin Dynamics

EMNLP 2023 (Findings)

We explored a novel alignment method for cross-lingual transfer learning in Event Detection.

A Spectral Viewpoint on Continual Relation Extraction

EMNLP 2023 (Findings)

A novel method for Continual Relation Extraction (CRE) with Feature Decorrelation.