Guanli Liu

Guanli Liu

I am a Postdoctoral Research Fellow at the University of Melbourne, working on AI for Databases, including spatial indexing, reinforcement learning-based query optimization, and LLM applications. My PhD research focused on learned spatial indexing, and has been published at top venues such as VLDB, ICDE, and TKDE. I am supervised by Dr. Jianzhong Qi and Prof. Lars Kulik.

Previously, I worked as a Senior Software Engineer at Baidu, and obtained my M.S. and B.Eng. degrees from Northeastern University, China.

CV (English)

📬 Contact

  • Email: [first name] [dot] liu1 [at] unimelb [dot] edu [dot] au
  • GitHub: Liuguanli
  • LinkedIn: Guanli Liu

📄 Publications

  1. Guanli Liu, Lars Kulik, Christian S. Jensen, Tianyi Li, Renata Borovica-Gajic, Jianzhong Qi.
    Efficient Cost Modeling of Space-filling Curves. PVLDB 2024.
  2. Guanli Liu.
    Learning Spatial Indices Efficiently. PhD Thesis, University of Melbourne, 2023.
  3. Guanli Liu, Jianzhong Qi, Lars Kulik, Kazuya Soga, Renata Borovica-Gajic, Benjamin I. P. Rubinstein.
    Efficient Index Learning via Model Reuse and Fine-tuning. ICDEW 2023.
  4. Guanli Liu, Jianzhong Qi, Christian S. Jensen, James Bailey, Lars Kulik.
    Efficiently Learning Spatial Indices. ICDE 2023.
  5. Jianzhong Qi, Guanli Liu, Christian S. Jensen, Lars Kulik.
    Effectively Learning Spatial Indices. PVLDB 2020.
  6. Yu Gu, Guanli Liu, Jianzhong Qi, Hongfei Xu, Ge Yu, Rui Zhang.
    The Moving K Diversified Nearest Neighbor Query. IEEE TKDE 2016.

👨‍💻 Work Experience

  1. Postdoctoral Research Fellow, University of Melbourne, 2024–Present
  2. Data Scientist, nftDb, 2023–2024
  3. Research Assistant, University of Melbourne, 2022–2023
  4. Senior Software Engineer, Baidu, 2015–2017
  5. Software Engineer, Neusoft Inc., 2012

🎓 Teaching

  • Tutor, COMP90041 Programming and Software Development – 2020 & 2021 S1
  • Tutor, COMP90018 Mobile Computing – 2019 S2

🛠 Skills

  • Languages: Python, Java, C++
  • Databases: PostgreSQL, MySQL, MongoDB, BigQuery
  • Machine Learning: TensorFlow, PyTorch, Scikit-learn
  • Cloud: Google Cloud Platform
  • Research Areas: Spatial indexing, query optimization, database systems