CV

Education and research and engineering experience of Guangyu Xiang.

Contact Information

Name Guangyu Xiang
Professional Title Ph.D. Student in Data Science and Analytics
Email gxiang190@connect.hkust-gz.edu.cn

Professional Summary

Ph.D. student at HKUST(GZ) working on machine learning systems, efficient and elastic LLM training and inference, GPU computing, distributed systems, and communication-computation co-optimization.

Experience

  • 2023 - 2023

    Beijing, China

    Research Intern, Systems Research Group
    Microsoft Research Asia
    Studied Multi-Head Attention and FlashAttention optimization, developed CUDA kernels, and explored compiler cost models.
  • 2022 - 2023

    Beijing, China

    R&D Engineer Intern, AI Framework Team
    OneFlow
    Reworked MLIR operator fusion and rewrite patterns with PDLL, and supported model compatibility across frameworks and accelerators.
  • 2022 - 2022

    Beijing, China

    AI Compiler Engineer Intern
    AMD
    Built TVM Relay passes for ASR model compilation, including quantization-flow optimization and AIE deployment evaluation.
  • 2020 - 2021

    Shenzhen, China

    AI Development Engineer Intern
    Peng Cheng Laboratory
    Contributed to resource management and job scheduling for high-performance computing clusters.

Education

  • 2025 - present

    Guangzhou, China

    Ph.D.
    The Hong Kong University of Science and Technology (Guangzhou), HKUST(GZ)
    Data Science and Analytics, Information Hub
    • Advisor: Prof. Xiaowen Chu
    • Machine Learning Systems; Distributed LLM Training and Serving; GPU Computing
  • 2021 - 2024

    Beijing, China

    Master's Degree
    Peking University (PKU)
    Software Engineering
    • School of Software and Microelectronics
    • High-Performance Computing, AI Compiler Optimization, and CUDA Programming
  • 2017 - 2021

    Chengdu, China

    Bachelor's Degree
    University of Electronic Science and Technology of China (UESTC)
    Software Engineering
    • School of Information and Software Engineering
    • GPA 3.86/4.00; ranked in the top 5%