Hieu N. Nguyen

I'm often amazed by the vastness of the ocean.

👋 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, where I’m fortunate to be advised by Prof. Rui Zhang.

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 (WWW ‘25, ICLR ‘26) and more robust systems (ICLR ‘25, NeurIPS ‘25, ACL ‘23)
Build systems capable of reasoning, planning, and creativity (upcoming work on coverage shrinkage in reasoning models).
Use AI to support scientific discovery (ICML ‘25).

See my thoughts on research and my generated tokens (blog posts). You can find my academic résumé here (last updated: April 2026).

Outside of research and work, I enjoy learning violin 🎻 as a hobby in my free time. I also like playing sports 🏸🏀🥏, so feel free to say hi or invite me if you see me around. These activities help me generate new ideas 💡 and stay creative in my research.

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

Last updated: May 1, 2026 (I sometimes forget to update this)

selected publications

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

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

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},
}

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

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

In Forty-second International Conference on Machine Learning, 2025

Awarded Bib Code

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},
}

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

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},
}

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

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 Code

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},
}

The images are generated from the paper’s title and abstract using Nano Banana. Can you infer the abstract from the visuals?