Hieu N. Nguyen

I’m learning to navigate through a blurred sea.

👋 Hi! I’m Hieu. My full name, written in Vietnamese order with diacritical marks, is Nguyễn Ngọc Hiếu. I am a first-year Ph.D. student in Computer Science at Penn State NLP, advised by Professor Rui Zhang.

You can find my academic résumé here (last updated: April 2026).

Primary Research Interests

I study the strengths and limitations of learning algorithms through carefully designed and controlled experimentation, with an emphasis on failure modes and robustness.

I believe this understanding can help us:

Develop more efficient learning algorithms (see WWW ‘25, and ICLR ‘26) and more robust systems (see ICLR ‘25, NeurIPS ‘25 and ACL ‘23)
Build systems capable of multi-step reasoning, planning, and creativity (System 2 behaviors) (upcoming work on coverage shrinkage in reasoning models).
Use AI to support scientific discovery and high-impact real-world applications (see ICML ‘25).

🔎 I am currently seeking research internship or visiting research opportunities in LLM reasoning and AI for scientific discovery.

Last updated: April 22, 2026

selected publications

For an up-to-date list of my research papers, please see my Google Scholar profile. * denotes equal contribution.

NeurIPS

Mitigating Reward Over-optimization in Direct Alignment Algorithms with Importance Sampling

Nguyen Minh Phuc, Ngoc-Hieu Nguyen, Duy Minh Ho Nguyen, and 5 more authors

In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025

Bib

@inproceedings{phuc2025mitigating,
  title = {Mitigating Reward Over-optimization in Direct Alignment Algorithms with Importance Sampling},
  author = {Phuc, Nguyen Minh and Nguyen, Ngoc-Hieu and Nguyen, Duy Minh Ho and Liu, Anji and Mai, An and Nguyen, Binh T. and Sonntag, Daniel and Doan, Khoa D},
  booktitle = {The Thirty-ninth Annual Conference on Neural Information Processing Systems},
  year = {2025},
  url = {https://openreview.net/forum?id=ltPRj2nthL}
}

ICML

LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models

Parshin Shojaee^*, Ngoc-Hieu Nguyen^*, Kazem Meidani, and 3 more authors

In Forty-second International Conference on Machine Learning, 2025

Awarded Bib

Oral presentation: 1% acceptance

@inproceedings{shojaee2025llmsrbench,
  title = {{LLM}-{SRB}ench: A New Benchmark for Scientific Equation Discovery with Large Language Models},
  author = {Shojaee, Parshin and Nguyen, Ngoc-Hieu and Meidani, Kazem and Farimani, Amir Barati and Doan, Khoa D and Reddy, Chandan K.},
  booktitle = {Forty-second International Conference on Machine Learning},
  year = {2025},
  url = {https://openreview.net/forum?id=SyQPiZJVWY},
}

ICLR

Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks

Nguyen Hung-Quang, Ngoc-Hieu Nguyen, The-Anh Ta, and 4 more authors

In The Thirteenth International Conference on Learning Representations, 2025

Bib

@inproceedings{hung-quang2025wicked,
  title = {Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks},
  author = {Hung-Quang, Nguyen and Nguyen, Ngoc-Hieu and Ta, The-Anh and Nguyen-Tang, Thanh and Wong, Kok-Seng and Thanh-Tung, Hoang and Doan, Khoa D},
  booktitle = {The Thirteenth International Conference on Learning Representations},
  year = {2025},
  url = {https://openreview.net/forum?id=1Z3C49JQVf},
}

WWW

Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training

Ngoc-Hieu Nguyen, Tuan-Anh Nguyen, Tuan Nguyen, and 3 more authors

In Proceedings of the ACM Web Conference 2024, Singapore, Singapore, 2024

Awarded DOI Bib

Oral presentation

@inproceedings{10.1145/3589334.3645702,
  author = {Nguyen, Ngoc-Hieu and Nguyen, Tuan-Anh and Nguyen, Tuan and Hoang, Vu Tien and Le, Dung D. and Wong, Kok-Seng},
  title = {Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training},
  year = {2024},
  isbn = {9798400701719},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3589334.3645702},
  doi = {10.1145/3589334.3645702},
  booktitle = {Proceedings of the ACM Web Conference 2024},
  pages = {3940–3951},
  numpages = {12},
  keywords = {communication efficiency, federated learning, recommendation system},
  location = {Singapore, Singapore},
  series = {WWW '24},
}