Bio
I am currently a postdoctoral research fellow at the National University of Singapore (NUS), collaborating with Prof. Jin Song Dong. I earned my Ph.D. from Shanghai Jiao Tong University under the supervision of Prof. Jingwen Leng.
My research interests include machine learning systems, high-performance computing, and computer architecture. I am passionate about exploring innovative solutions in these fields and contributing to the advancement of technology.
Education
- Ph.D in Shanghai Jiao Tong University, 2023
- B.S. in Huazhong University of Science and Technology, 2018
Work Experience
- 2023.10 - 2024.10: System Developer/Researcher
- Tencent AI Lab
- Summer 2022: Research Intern
- Alibaba Cloud PAI
- Summer 2021: Research Intern
- Qi Zhi Institute
- Spring 2021: Research Intern
- Peng Cheng Laboratory
- Summer 2020: Research Intern
- T-Head Alibaba
Publications
- Zihan Liu, Xinhao Luo, Junxian Guo, Wentao Ni, Yangjie Zhou, Yue Guan, Cong Guo, Weihao Cui, Yu Feng, Minyi GUo, Yuhao Zhu, Minjia Zhang, Jingwen Leng, Chen Jin. VQ-LLM: High-performance Code Generation for Vector Quantization Augmented LLM Inference. Proceedings of the 31st IEEE International Symposium on High-Performance Computer Architecture. (HPCA 2025)
- Yangjie Zhou, Honglin Zhu, Qian Qiu, Weihao Cui, Zihan Liu, Cong Guo, Siyuan Feng, Jintao Meng, Haidong Lan, Jingwen Leng, Wenxi Zhu, Minwen Deng. Vortex: Efficient Sample-Free Dynamic Tensor Program Optimization via Hardware-aware Strategy Space Hierarchization. Preprint, 2024.
- Yue Guan, Changming Yu, Yangjie Zhou, Jingwen Leng, Chao Li, Minyi Guo. Fractal: Joint Multi-Level Sparse Pattern Tuning of Accuracy and Performance for DNN Pruning. Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2024. (ASPLOS 2024)
- Yangjie Zhou, Jingwen Leng, Yaoxu Song, Shuwen Lu, Mian Wang, Chao Li, Minyi Guo, Wenting Shen, Yong Li, Wei Lin, Xiangwen Liu, Hanqing Wu. uGrapher: High-Performance Graph Operator Computation via Unified Abstraction for Graph Neural Networks. Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2. 2023. (ASPLOS 2023)
- Yangjie Zhou, Yaoxu Song, Jingwen Leng, Zihan Liu, Weihao Cui, Zhendong Zhang, Cong Guo, Quan Chen, Li Li, Minyi Guo. AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUs. 20th ACM International Conference on Computing Frontiers. (CF 2023)
- Guandong Lu, Runzhe Chen, Yakai Wang, Yangjie Zhou, Rui Zhang, Zheng Hu, Yanming Miao, Zhifang Cai, Li Li, Jingwen Leng, Minyi Guo. DistSim: A performance model of large-scale hybrid distributed DNN training. 20th ACM International Conference on Computing Frontiers. (CF 2023)
- Zhengyi Li, Cong Guo, Zhanda Zhu, Yangjie Zhou, Yuxian Qiu, Xiaotian Gao, Jingwen Leng, Minyi Guo. Efficient Adaptive Activation Rounding for Post-Training Quantization. Preprint.
- Yangjie Zhou, Mengtian Yang, Cong Guo, Jingwen Leng, Yun Liang, Quan Chen, Minyi Guo, Yuhao Zhu. Characterizing and demystifying the implicit convolution algorithm on commercial matrix-multiplication accelerators. 2021 IEEE International Symposium on Workload Characterization. (IISWC 2021)
- Yangjie Zhou, Jingwen Leng, Mengtian Yang, Zhihui Zhang, Yakai Wang, Chen Zhang, Minyi Guo, Yuhao Zhu. TPUSim: ISA Design and Optimization for Fused Architecture Based Training Accelerator. Poster. 2020 57th ACM/IEEE Design Automation Conference (DAC 2020)(Poster)
- Cong Guo, Yangjie Zhou, Jingwen Leng, Yuhao Zhu, Zidong Du, Quan Chen, Chao Li, Bin Yao, Minyi Guo. Balancing efficiency and flexibility for DNN acceleration via temporal GPU-systolic array integration. 2020 57th ACM/IEEE Design Automation Conference. (DAC 2020)
Honors and Services
- 2025 ACM Transactions on Internet Technology, Reviewer
- 2024 OSDI’24 Artifact Evaluation Committee
- 2024 ATC’24 Artifact Evaluation Committee
- 2023 MLSys’23 Artifact Evaluation Committee
- 2022 SJTU JHC Excellent Doctoral Academic Forum First Prize Scholarship
- 2020 DAC’20 Richard Newton Young Student Fellow