Tianshi Xu

PhD Student (2023)

Peking University

Interests
  • Privacy-Preserving AI
Education

  • B.S. in Software Enigneering, 2023

    Beihang University

Publications

Yifan Zhou, Tianshi Xu, Jue Hong, Ye Wu, Meng Li (2025). CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing. In Conference on Neural Information Processing Systems (NeurIPs) 2025.

Paper

Zebin Yang, Sunjian Zheng, Tong Xie, Tianshi Xu, Bo Yu, Fan Wang, Jie Tang, Shaoshan Liu, Meng Li (2025). EfficientNav: Towards On-Device Object-Goal Navigation with Navigation Map Caching and Retrieval. In Conference on Neural Information Processing Systems (NeurIPs) 2025.

Paper

Chenqi Lin, Kang Yang, Tianshi Xu, Ling Liang, Yufei Wang, Zhaohui Chen, Runsheng Wang, Mingyu Gao, Meng Li (2025). Ironman: Accelerating Oblivious Transfer Extension for Privacy-Preserving AI with Near-Memory Processing. In MICRO 2025.

Paper

Tianshi Xu, Wenjie Lu, Jiangrui Yu, Chenqi Lin, Yi Chen, Runsheng Wang, Meng Li (2025). Breaking the Layer Barrier: Remodeling Private Transformer Inference with Hybrid CKKS and MPC. In Usenix Security Symposium 2025.

Paper

Tianshi Xu, Lemeng Wu, Runsheng Wang, Meng Li (2024). PrivCirNet: Efficient Private Inference via Block Circulant Transformation. In Conference on Neural Information Processing Systems (NeurIPs) 2024.

Paper

Jiangrui Yu, Wenxuan Zeng, Tianshi Xu, Renze Chen, Yun (Eric) Liang, Runsheng Wang, Ru Huang, Meng Li (2024). FlexHE: A flexible Kernel Generation Framework for Homomorphic Encryption-Based Private Inference. In International Conference on Computer-Aided Design (ICCAD) 2024.

Paper

Tianshi Xu, Shuzhang Zhong, Wenxuan Zeng, Runsheng Wang, Meng Li (2024). PrivQuant: Communication-Efficient Private Inference with Quantized Network/Protocol Co-Optimization. In International Conference on Computer-Aided Design (ICCAD) 2024.

Paper

Chenqi Lin, Tianshi Xu, Zebin Yang, Meng Li, Runsheng Wang, Ru Huang (2024). FastQuery: Communication-efficient Embedding Table Query for Private LLMs inference. In Design Automation Conference (DAC) 2024.

Paper

Tianshi Xu, Meng Li, Runsheng Wang, Ru Huang (2023). Falcon: Accelerating Homomorphically Encrypted Convolutions for Efficient Private Mobile Network Inference. In ACM/IEEE International Conference on Computer Aided Design (ICCAD) 2023.

Paper

Yu Fu, Yu Tong, Yijing Ning, Tianshi Xu, Meng Li, Jingqiang Lin, Dengguo Feng (0001). Swift: Fast Secure Neural Network Inference with Fully Homomorphic Encryption.