Zhengjie Miao

DB research@Simon Fraser UniversityHow to pronounce my first name?

  
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Office: TASC 1 9407

Email: {fname}@sfu.ca

I am an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU). My research interest broadly lies in data management and artificial intelligence, with a focus on developing algorithms and tools that enhance the usability and efficiency of data systems. I am part of the SFU Data Science Research Group.

Before joining SFU, I spent one year as a Research Scientist at Megagon Labs. I finished my Ph.D. in Computer Science from Duke University, where I was fortunate to work with Prof. Sudeepa Roy and Prof. Jun Yang. I received M.S. from Columbia University under the advisory of Prof. Eugene Wu and my B.S. from Peking University.

current research

selected work

Query Semantics & Verification

  1. VLDB
    ParSEval: Plan-aware Test Database Generation for SQL Equivalence Evaluation
    Chunyu Chen, Zhengjie Miao, Yong Zhang, and Jiannan Wang
    Proceedings of the VLDB Endowment, 18(11), 2025
  2. SIGMOD
    Understanding Queries by Conditional Instances
    Amir Gilad*, Zhengjie Miao*, Sudeepa Roy, and Jun Yang
    In ACM SIGMOD International Conference on Management of Data, 2022

Query Optimization & Performance

  1. VLDB
    Benchmarking the Full Pipeline of Materialized-View-Based Query Rewriting
    Xinjie Hu, and Zhengjie Miao
    Proceedings of the VLDB Endowment, 19(11), 2026
  2. arXiv
    SPA: A SQL-Plan-Aware Reinforcement Learning Framework for Query Rewriting with LLMs
    Xinyi Huang, and Zhengjie Miao
    arXiv preprint, 2026

AI for Data Science

  1. CHI
    Human-LLM Collaborative Annotation Through Effective Verification of LLM Labels
    Xinru Wang, Hannah Kim, Sajjadur Rahman, Kushan Mitra, and Zhengjie Miao
    In ACM CHI Conference on Human Factors in Computing Systems, 2024
  2. SIGMOD
    Watchog: A Light-Weight Contrastive Learning Based Framework for Column Annotation
    Zhengjie Miao, and Jin Wang
    Proceedings of the ACM on Management of Data, 1(4), 2023 (presented at SIGMOD 2024)

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