Renzhi Wu

Klaus 3319

266 Ferst Dr NW

Atlanta, GA 30332

My name is Renzhi Wu (吴仁智). I’m a third year Ph.D. student in Computer Science at Georgia Tech, advised by Prof. Xu Chu and Prof. Kexin Rong. I was fortunate to intern at Google, Celonis, Adobe, and Alibaba. I am on the job market!

Research Interests: I am generally interested in machine learning and data management. I work on applying ML to data management problems (e.g. entity resolution and cardinality estimation) and improving ML models from a data-centric perspective (e.g. via programmatic data labeling and data cleaning). My research is partly supported by a Meta (Facebook) Fellowship.

Previously: I hold M.Sc. in Thermophysics from Tsinghua University where I worked on numerical simulation algorithms for the formation of water dews. I also hold M.Eng. in Production System Engineering from RWTH Aachen University. Before that, I obtained my bachelor’s degrees in Energy/Power Engineering and in Economics from Tsinghua University.


Mar 17, 2022 We are organizing the SIGMOD 2022 programming contest. Check it out and happy coding!
Feb 2, 2022 I received the Meta PhD Research Fellowship. Thank you Meta!
Sep 16, 2021 Our work on learned sampling-based cardinality estimator has been accepted to VLDB 2022.
Jul 31, 2021 I received the SCS Incubator Graduate Fellowship.
May 17, 2021 We are excited to win the Runner-up award at SIGMOD 2021 Programming Contest!


* denotes equal contribution.


  1. ICLR
    Learning Hyper Label Model for Programmatic Weak Supervision
    Renzhi Wu, Shen-En Chen, Jieyu Zhang, and Xu Chu
    To appear in ICLR 2023
    Ground Truth Inference for Weakly Supervised Entity Matching
    Renzhi Wu, Alexander Bendeck, Xu Chu, and Yeye He
    To appear in SIGMOD 2023


  1. VLDB
    Learning to be a Statistician: Learned Estimator for Number of Distinct Values
    Renzhi Wu, Bolin Ding, Xu Chu, Zhewei Wei, Xiening Dai, Tao Guan, and Jingren Zhou
    Proc. VLDB Endow. 2022


  1. VLDB
    Demonstration of Panda: A Weakly Supervised Entity Matching System
    Renzhi Wu, Prem Sakala, Peng Li, Xu Chu, and Yeye He
    Proc. VLDB Endow. 2021
  2. VLDB
    Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions
    Bojan Karlas*, Peng Li*, Renzhi Wu, Nezihe Merve Gürel, Xu Chu, Wentao Wu, and Ce Zhang
    Proc. VLDB Endow. 2021


    ZeroER: Entity Resolution using Zero Labeled Examples
    Renzhi Wu, Sanya Chaba, Saurabh Sawlani, Xu Chu, and Saravanan Thirumuruganathan
    In Proceedings of the 2020 International Conference on Management of Data, SIGMOD Conference 2020, online conference [Portland, OR, USA], June 14-19, 2020 2020
    Dynamic pattern matching with multiple queries on large scale data streams
    Sergey Sukhanov*, Renzhi Wu*, Christian Debes, and Abdelhak M. Zoubir
    Signal Processing 2020
    GOGGLES: Automatic Image Labeling with Affinity Coding
    Nilaksh Das, Sanya Chaba, Renzhi Wu, Sakshi Gandhi, Duen Horng Chau, and Xu Chu
    In Proceedings of the 2020 International Conference on Management of Data, SIGMOD Conference 2020, online conference [Portland, OR, USA], June 14-19, 2020 2020
Reviewer/PC Services: ICLR, Neurips, KDD, SDM, Scientific Reports