Jiamu Morris Zhang

Duncan Hall, 2090

6100 Main St

Houston, Texas 77005

About me

I’m Jiamu ‘Morris’ Zhang (张佳慕), third-year Ph.D. student in the Department of Computer Science at Rice University, where I am a member of the Chili Lab advised by Professor Hanjie Chen.

My research centers on efficient, scalable, and reliable machine learning for large language models (LLMs) and agentic systems. I’m especially drawn to system–method co-design: pairing algorithmic ideas—model compression, efficient reasoning and test-time compute control, sparsity and Mixture-of-Experts—with the systems that actually run them, from memory- and compute-aware inference to LLM serving on low-resource hardware. My goal is to make foundation models faster and cheaper without giving up quality. I’m equally interested in how we measure these systems, developing rigorous and label-efficient ways to evaluate LLM and multi-agent behavior beyond raw accuracy.

Prior to my Ph.D., I received my B.S. in the Department of Computer and Data Science from Case Western Reserve University (advisor: Shuai Xu and Vipin Chaudhary), where I worked on model compression and adversarial robustness. I was also fortunate to be mentored by Dr. Xia “Ben” Hu.

I’m really passionate about building open, efficient, and reliable AI systems. Feel free to reach out!

See my publications for a full list, or my Google Scholar profile.

news

Aug 21, 2026	Our EMNLP 2026 paper Rethinking the Evaluation of Efficiency Methods for Multi-Agent Systems was nominated for an EMNLP 2026 award.
Aug 21, 2026	Our paper Rethinking the Evaluation of Efficiency Methods for Multi-Agent Systems was accepted to the EMNLP 2026 main conference. See you in Budapest!
May 01, 2026	Started as a full-time intern at Nokia AI Research & Development (Sunnyvale, CA), working on Efficiency & System–Method Co-design.

selected publications

EMNLP

Rethinking the Evaluation of Efficiency Methods for Multi-Agent Systems

Jiamu Zhang^*, Lingxi Zhang^*, Pengjun Lu^*, Qiyue Zhang, and 5 more authors

In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2026

Award Nominee arXiv Bib

Nominated for an EMNLP 2026 award.

@inproceedings{zhang2026maseval,
  title = {Rethinking the Evaluation of Efficiency Methods for Multi-Agent Systems},
  author = {Zhang, Jiamu and Zhang, Lingxi and Lu, Pengjun and Zhang, Qiyue and Chuang, Yu-Neng and Li, Zhengchen and Xu, Shuai and Chaudhary, Vipin and Chen, Hanjie},
  booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2026},
}

Preprint

SPECTRA: Pushing the KV Cache Beyond the 2-Bit Cliff via Spectral Transform Coding

Jiamu Zhang, Liang Wu, Kelly Wan, Hanjie Chen, and 1 more author

arXiv preprint arXiv:2608.07915, 2026

arXiv Bib

@article{zhang2026spectra,
  title = {SPECTRA: Pushing the KV Cache Beyond the 2-Bit Cliff via Spectral Transform Coding},
  author = {Zhang, Jiamu and Wu, Liang and Wan, Kelly and Chen, Hanjie and Hong, Liangjie},
  journal = {arXiv preprint arXiv:2608.07915},
  year = {2026},
}

CVPR

Flexible Group Count Enables Hassle-Free Structured Pruning

Jiamu Zhang^*, Shaochen Zhong^*, Andrew Ye, Zirui Liu, and 8 more authors

In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025

Bib PDF

@inproceedings{zhang2025flexgroup,
  title = {Flexible Group Count Enables Hassle-Free Structured Pruning},
  author = {Zhang, Jiamu and Zhong, Shaochen and Ye, Andrew and Liu, Zirui and Zhao, Sebastian and Zhou, Kaixiong and Li, Li and Choi, Soo-Hyun and Chen, Rui and Hu, Xia and Xu, Shuai and Chaudhary, Vipin},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
}

TMLR

Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, and 7 more authors

Transactions on Machine Learning Research (TMLR), 2025

arXiv Bib Code

@article{sui2025overthinking,
  title = {Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models},
  author = {Sui, Yang and Chuang, Yu-Neng and Wang, Guanchu and Zhang, Jiamu and Zhang, Tianyi and Yuan, Jiayi and Liu, Hongyi and Wen, Andrew and Zhong, Shaochen and Chen, Hanjie and Hu, Xia},
  journal = {Transactions on Machine Learning Research (TMLR)},
  year = {2025},
}