Short Bio

I'm current a PhD candidate at MIT, CSAIL, Data System Group. I'm super fortunately co-advised by Prof. Samuel Madden and Prof. Tim Kraska. Before MIT, I achieved a master degree from University of Oxford and bachelor degrees from Carnegie Mellon University. My research focus is on query optimization and query processing. I'm also working on optimizing Cloud Data Infrastructures and efficiently serving large models.

Selected Publications

• Improving DBMS Scheduling Decisions with Fine-grained Performance Prediction on Concurrent Queries.
  Ziniu Wu, Markos Markakis, Chunwei Liu, Peter Baile Chen, Balakrishnan Narayanaswamy, Tim Kraska, Samuel Madden
  In submission.

• CascadeServe: Unlocking Model Cascades for Inference Serving.
  Ferdi Kossmann, Ziniu Wu, Alex Turk, Nesime Tatbul, Lei Cao, Samuel Madden
  In submission.

• Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD.
  X Yu Geoffrey, Ziniu Wu, Ferdi Kossmann, Tianyu Li, Markos Markakis, Amadou Ngom, Samuel Madden, Tim Kraska
  VLDB 2024.

• Stage: Query Execution Time Prediction in Amazon Redshift.
  Ziniu Wu, Ryan Marcus, Zhengchun Liu, Parimarjan Negi, Vikram Nathan, Pascal Pfeil, Gaurav Saxena, Mohammad Rahman, Balakrishnan Narayanaswamy, Tim Kraska
  SIGMOD Industrial 2024.

• Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes.
  Tim Kraska*, Tianyu Li*, Samuel Madden*, Markos Markakis*, Amadou Ngom*, Ziniu Wu*, Geoffrey X Yu*
  VLDB 2023.

• FactorJoin: A New Cardinality Estimation Framework for Join Queries.
  Ziniu Wu, Parimarjan Negi, Mohammad Alizadeh, Tim Kraska, Samuel Madden
  SIGMOD 2023.

• Robust Query Driven Cardinality Estimation under Changing Workloads.
  Parimarjan Negi, Ziniu Wu, Andreas Kipf, Nesime Tatbul, Ryan Marcus, Sam Madden, Tim Kraska, Mohammad Alizadeh.
  VLDB 2023.

• Extract-Transform-Load for Video Streams.
  Ferdinand Kossmann, Ziniu Wu, Eugenie Lai, Nesime Tatbul, Tim Kraska, Samuel Madden.
  VLDB 2023.

• Lero: A Learning-to-Rank Query Optimizer.
  Rong Zhu, Wei Chen, Bolin Ding, Xingguang Chen, Andreas Pfadler, Ziniu Wu, Jingren Zhou.
  VLDB 2023.

• A Unified Transferable Model for ML-Enhanced DBMS.
  Ziniu Wu, Peilun Yang, Pei Yu, Rong Zhu, Yuxing Han, Yaliang Li, Defu Lian, Kai Zeng, Jingren Zhou.
  CIDR 2022.

• Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation.
  Yuxing Han*, Ziniu Wu*, Peizhi Wu, Rong Zhu, Jingyi Yang, Liang Wei Tan, Kai Zeng, Gao Cong, Yanzhao Qin, Andreas Pfadler, Zhengping Qian, Jingren Zhou, Jiangneng Li, Bin Cui.
  VLDB 2022.

• FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation.
  Rong Zhu*, Ziniu Wu*, Yuxing Han, Kai Zeng, Andreas Pfadler, Zhengping Qian, Jingren Zhou, Bin Cui.
  VLDB 2021.

• BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation.
  Ziniu Wu, Amir Shaikhha, Rong Zhu, Kai Zeng, Yuxing Han, Jingren Zhou.
  In Submission.

• Efficient and scalable structure learning for Bayesian networks: Algorithms and Applications.
  Rong Zhu, Andreas Pfadler, Ziniu Wu, Yuxing Han, Xiaoke Yang, Feng Ye, Zhenping Qian, Jingren Zhou, Bin Cui.
  ICDE 2021.

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Blogs

• THE CASE FOR CARDINALITY BOUNDS: PRINCIPLED CONSERVATISM IN QUERY OPTIMIZATION.

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Service

• VLDB Journal 2025 Reviewer
• TKDE 2025 Reviewer
• INFOSYS 2025 Reviewer
• INFOSYS 2024 Reviewer
• INFOSYS 2023 Reviewer
• MLSys 2022 External Reviewer
• KDD 2022 External Reviewer
• Teaching Assistant for MIT course 6.5830: Database

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