Minh Huynh Nguyen

PhD Student in Computer Science at National University of Singapore

Email: minh.nghminh [at] gmail [dot] com | minhnh [at] u [dot] nus [dot] edu

prof_pic.jpg

Hi (Xin chào 🇻🇳)! I’m Minh, a first-year PhD student at the National University of Singapore (NUS), advised by Prof. Bryan Kian Hsiang Low. My research focuses on Large Language Models, Multimodality, Agent-based systems, and AI4Code.

Before that, I earned my bachelor’s degree from the University of Technology, VNU-HCM. I then worked as an AI resident at FPT Software AI Center under the supervision Dr. Nghi D. Q. Bui.

news

Jan 15, 2026 Our paper TIGER: Bridging the Multimodal Reasoning-Access Gap via Modality Counterfactuals has been accepted to the ICML 2026 FoGen Workshop! :tada:
May 01, 2025 Our paper AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology has been accepted to Forge@ICSE 2025! :tada:
Aug 01, 2024 Our paper Functional Overlap Reranking for Neural Code Generation has been accepted to Findings of ACL 2024! :tada:
Mar 01, 2024 Our paper HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations has been accepted to Findings of EACL 2024! :tada:

selected publications

  1. FoGen@ICML
    TIGER: Bridging the Multimodal Reasoning-Access Gap via Modality Counterfactuals
    Gregory Kang Ruey Lau*, Minh Huynh Nguyen*, and Bryan Kian Hsiang Low
    In ICML 2026 FoGen Workshop, 2026
  2. Forge@ICSE
    AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology
    Minh Huynh Nguyen, Thang Phan Chau, Phong X. Nguyen, and 1 more author
    In Forge@ICSE, 2025
  3. ACL
    Functional Overlap Reranking for Neural Code Generation
    Hung Quoc To, Minh Huynh Nguyen, and Nghi D. Q. Bui
    In Findings of the Association for Computational Linguistics: ACL 2024, 2024
  4. EACL
    HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations
    Minh Huynh Nguyen*, Nghi D. Q. Bui*, Truong Son Hy, and 2 more authors
    In Findings of the Association for Computational Linguistics: EACL 2024, 2024