CV

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Education

• Ph.D candidate in University of Virginia, 2025-
• M.E. in Sichuan University University, 2021-2024
• B.E. in Sichuan University University, 2015-2019

Work experience

• May-August 2026: AI R&D Intern at Nokia
  • Sunnyvale, California, United States (on-site)
  • Research focus: long-context compression and robustness.
• Winter 2025: Research Intern at HoumoAI
  • DUties includes: Research on Resource Preserving Data Application
• Winter 2023: Research Intern at Lenovo
  • Based in Beijing
  • Duties includes: Research on Resource Preserving Data Application, Decentralized Data Analyses
• Winter 2022: Intern Student at NUS HPC Lab
  • Duties includes: Research on Resource Preserving Data Application
• Summer 2018: Research Intern at Lenovo
  • Based in Chengdu
  • Duties included: Decentralized Data Analyses

Publications

Saliency-Aware Multi-Route Thinking: Grounding and Reasoning on Vision-Language Agents

M. Shi, Y. He, Y. Zhu, J. Li. Saliency-Aware Multi-Route Thinking: Grounding and Reasoning on Vision-Language Agents. NeurIPS 2026.

Dive into the Implicit Biases of Low-rank Vision-language Alignment

Dive into the Implicit Biases of Low-rank Vision-language Alignment. ECCV 2026. M. Shi, S. Wang, X. Wang, S. Zhou, K. Wang, T. Fu, C. Zhao, A. Su, P. Jiang, M. Wu

A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training

A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training. CVPR 2025. K. Wang*, M. Shi*, Y. Zhou*,Z. Yuan, Y. Shang, X. Peng, H. Zhang, Y. You

Make Optimization Once and for All with Fine-grained Guidance

Make Optimization Once and for All with Fine-grained Guidance. Arxiv. M. Shi, R. Lin, X. Chen, Y. Zhou, Z. Ding, P. Li, T. Wang, K. Wang, Z. Wang, J. Zhang, T. Chen.

Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training

Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training. Arxiv. M. Shi*, Y. Zhou*, R. Yu, Z. Li, Z. Liang, X. Zhao, X. Peng, T Rajpurohit, R. Vedantam, W. Zhao, K. Wang, Y. You.

PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning

PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. NeurIPS 2023. M. Shi, Y. Zhou, K. Wang, H. Zhang, S. Huang, Q. Ye, J. Lv

Unconstrained Feature Model and Its General Geometric Patterns in Federated Learning: Local Subspace Minority Collapse

Unconstrained Feature Model and Its General Geometric Patterns in Federated Learning: Local Subspace Minority Collapse. ICONIP 2023. M. Shi, Y. Zhou, Q. Ye, J. Lv

DD-Ranking: Rethinking the Evaluation of Dataset Distillation

DD-Ranking: Rethinking the Evaluation of Dataset Distillation. NeurIPS 2026 (Oral). Z. Li, X. Zhong, S. Khaki, Z. Liang, Y. Zhou, M. Shi, Z. Wang, X. Zhao, W. Zhao, Z. Qin, M. Wu, P. Zhou, H. Wang, D. J. Zhang, J. Liu, S. Wang, D. Liu, L. Zhang, G. Li, K. Wang, Z. Zhu, Z. Ma, J. T. Zhou, J. Lv, Y. Jin, P. Wang, K. Zhang, L. Lyu, Y. Huang, Z. Akata, Z. Deng, X. Wu, G. Cazenavette, Y. Shang, J. Cui, J. Gu, Q. Zheng, H. Ye, S. Wang, X. Wang, Y. Yan, A. Yao, M. Z. Shou, T. Chen, H. Bilen, B. Mirzasoleiman, M. Kellis, K. N. Plataniotis, Z. Wang, B. Zhao, Y. You, K. Wang

Grounding Memory Summarization in Utility Intent

Z. Lei, M. Shi, X. Fu, H. He, Q. R. Wang, J. Li. Grounding Memory Summarization in Utility Intent. EMNLP 2026.

IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning

IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning. ICML 2026. Y. He, Y. Zhu, M. Shi, W. Zheng, L. Su, X. Wang, Q. Guo, J. Li

FedSH: Tackling Staleness By Scheduling High-order Approximation in Asynchronous Federated Learning

FedSH: Tackling Staleness By Scheduling High-order Approximation in Asynchronous Federated Learning. IJCNN 2025. H. Gao, M. Shi, Y. Zhou, D. Xiong, Q. Ye, J. Lv

Pruning-Robust Mamba with Asymmetric Multi-Scale Scanning Paths

Pruning-Robust Mamba with Asymmetric Multi-Scale Scanning Paths. NeurIPS 2025. J. Lv, Y. Zhou, M. Shi, Z. Liang, P. Zhang, X. Peng, W. Zhao, Z. Zhu, J. Lv, Q. Ye, K. Wang

Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights

Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights. NeurIPS 2025. Z. Liang, D. Tang, Y. Zhou, X. Zhao, M. Shi, W. Zhao, Z. Li, P. Wang, K. Schürholt, D. Borth, M. M. Bronstein, Y. You, Z. Wang, K. Wang

Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints

Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints. CVPR 2025. Y. Zhou, Y. Tian, J. Lv, M. Shi, Y. Li, Q. Ye, S. Zhang, J. Lv

REPA Works Until It Doesn’t: Early-Stopped, Holistic Alignment Supercharges Diffusion Training

REPA Works Until It Does not: Early-Stopped, Holistic Alignment Supercharges Diffusion Training. NeurIPS 2025. Z. Wang, W. Zhao, Y. Zhou, Z. Li, Z. Liang, M. Shi, X. Zhao, P. Zhou, K. Zhang, Z. Wang, K. Wang, Y. You

GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning

GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning. Arxiv. S. Zhou*, S. Wang*, Z. Yuan*, M. Shi, Y. Shang, D. Yang.

E-3SFC: Communication-Efficient Federated Learning With Double-Way Features Synthesizing

E-3SFC: Communication-Efficient Federated Learning With Double-Way Features Synthesizing. IEEE TNNLS 2025. Y. Zhou, Y. Tian, M. Shi, Y. Li, Y. Sun, Q. Ye, J. Lv

Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation

Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation. NeurIPS 2025. X. Wu, J. Niu, X. Liu, M. Shi, G. Zhu, S. Tang.

Federated CINN Clustering for Accurate Clustered Federated Learning

Federated CINN Clustering for Accurate Clustered Federated Learning. ICASSP 2024. Y. Zhou, M. Shi, Y. Tian, Y. Li, Q. Ye, J. Lv

DeFTA: A Plug-and-Play Peer-to-Peer Decentralized Federated Learning Framework

DeFTA: A Plug-and-Play Peer-to-Peer Decentralized Federated Learning Framework. InfoSci. Y. Zhou, M. Shi, Y. Tian, Q. Ye, J. Lv

Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence.

Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence. ICCV 2023. Y. Zhou, M. Shi, Y. Li, Y. Sun, Q. Ye, J. Lv

DLB: a dynamic load balance strategy for distributed training of deep neural networks

DLB: a dynamic load balance strategy for distributed training of deep neural networks. Trans.ETCI. Q. Ye, Y. Zhou, M. Shi, Y. Sun, J. Lv

FLSGD: free local SGD with parallel synchronization

FLSGD: free local SGD with parallel synchronization. JoSc. Q. Ye, Y. Zhou, M. Shi, J. Lv