Yuzhen Mao
yuzhenm [at] stanford [dot] edu

I am a Ph.D. student in Computer Science at Stanford University, fortunately advised by Prof. Azalia Mirhoseini and Prof. Christos Kozyrakis. My current research interests focus on efficient and agentic LLM systems, especially for long-context reasoning.

If you find any research interests that we might share, feel free to drop me an email. I am always open to potential collaborations.

Email  /  Google Scholar  /  GitHub  /  X

profile photo
News
  • [2026] IceCache is accepted to ICLR 2026.
  • [2025] Started my Ph.D. at Stanford University.
Selected Publications  (* denotes equal contribution)

Decentralized Multi-Agent Systems with Shared Context
Yuzhen Mao, Azalia Mirhoseini
arXiv preprint, 2026
paper


IceCache: Memory-Efficient KV-cache Management for Long-Sequence LLMs
Yuzhen Mao, Qiao Wang, Martin Ester, Ke Li
International Conference on Learning Representations (ICLR), 2026
project page

Mem-α: Learning Memory Construction via Reinforcement Learning
Yu Wang, Ryuichi Takanobu, Zhiyu Liang, Yuzhen Mao, Yuanzhe Hu, Julian McAuley, Xiaojian Wu
arXiv preprint, 2025
paper

IceFormer: Accelerated Inference with Long-Sequence Transformers on CPUs
Yuzhen Mao, Martin Ester, Ke Li
International Conference on Learning Representations (ICLR), 2024
project page

Phenotype prediction from single-cell RNA-seq data using attention-based neural networks
Yuzhen Mao*, Yen-Yi Lin*, Nelson K. Y. Wong, Stanislav Volik, Funda Sar, Colin Collins, Martin Ester
Bioinformatics, 2024
paper

Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks
Yuzhen Mao, Zhun Deng, Huaxiu Yao, Ting Ye, Kenji Kawaguchi, James Zou
ICML Workshop on Spurious Correlations, Invariance, and Stability, 2023
paper

Augmenting Knowledge Transfer across Graphs
Yuzhen Mao, Jianhui Sun, Dawei Zhou
IEEE International Conference on Data Mining (ICDM), 2022
paper


Design and source code from Jon Barron. Style adapted from Zhijian Liu and Ligeng Zhu.