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技术活，当赏~

About Me (邢铭哲 Xing Mingzhe)

I am a Ph.D. student (expected to graduate in June 2024) in the Department of Computer Science at Peking University, advised by Prof. Zhen Xiao.

Before coming to PKU, I received my Master’s degree at the University of Chinese Academy of Sciences, supervised by Prof. Qing Wang. I obtained my Bachelor’s degree at Huazhong University of Science and Technology.

Research

My research interests focus on AI for System&Software Engineering, an interdisciplinary field integrating machine learning, software engineering, and cloud systems. I am committed to investigating and addressing efficiency, effectiveness, and reliability challenges within systems and software engineering domains with machine learning algorithms.



Publications

  1. Mingzhe Xing, Rongkai Zhang, Hui Xue, Qi Chen, Fan Yang and Zhen Xiao, “Understanding the Weakness of Large Language Model Agents within a Complex Android Environment”. (arXiv 2402.06596).
  2. Pengzi Li, Mingxuan Song, Mingzhe Xing, Zhen Xiao, Qiuyu Ding, Shengjie Guan and Jieyi Long, “SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement”. (WWW’2024).
  3. Mingzhe Xing, Hangyu Mao, Shenglin Yin, Lichen Pan, Zhengchao Zhang, Zhen Xiao and Jieyi Long, ”A Dual-Agent Scheduler for Distributed Deep Learning Jobs on Public Cloud via Reinforcement Learning”. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD ‘2023).
  4. Mingzhe Xing, Hangyu Mao and Zhen Xiao, “Fast and Fine-grained Autoscaler for Streaming Jobs with Reinforcement Learning,” In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI ‘2022).
  5. Mingzhe Xing, Ziyun Wang and Zhen Xiao, “Analysis of Resource Management Methods Based on Reinforcement Learning,” 2021 International Conference on High Performance Big Data and Intelligent Systems (HDIS ‘2021).
  6. Mingzhe Xing, Shuqing Bian, Waney Xin Zhao, Zhen Xiao, Xinji Luo, Cunxiang Yin, Jing Cai and Yancheng He. “Learning Reliable User Representations from Volatile and Sparse Data to Accurately Predict Customer Lifetime Value.” In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD ‘2021).
  7. Lin Shi*, Mingzhe Xing*, Mingyang Li, Yawen Wang, Shoubin Li and Qing Wang, “Detection of Hidden Feature Requests from Massive Chat Messages via Deep Siamese Network,” In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (ICSE ‘20, Equal Contribution)
  8. Lin Shi, Mingyang Li, Mingzhe Xing, Yawen Wang, Qing Wang, Xinhua Peng, Weimin Liao, Guizhen Pi and Peisheng Li. ”Learning to Extract Transaction Function from Requirements: An Industrial Case on Financial Software”. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2020).