About Me

I am a fifth year PhD Student at Georgia tech, advised by Xu Chu and Kexin Rong. I am interested in the intersection of machine learning and data-centric systems, with a focus on fairness. I currently work on building a declarative system for improving fairness in machine learning. Our goal is to build a system that helps with responsible and fair machine learning model building. Also, I am interested in using machine learning techniques to accelerate database engines. Prior to joining Georgia Tech as a Ph.D student, I got my Master's degree in Computer Science at ETH Zurich in 2017 and my Bachelor's degree in Machine Intelligence at Peking University in 2014.


  • Oct, 2022: Our work "iFlipper: Label Flipping for Individual Fairness" is accepted at SIGMOD 2023.
  • Feb, 2021: Our work "OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning" is accepted at SIGMOD 2021.


  • Ph.D. Candidate, Georgia Insititute of Technology, 2019.8 - Current
  • M.S., ETH Zurich, 2014.9 - 2017.4
  • B.S., Peking University, 2010.9 - 2014.6

Professional Experiences

  • Research Engineer Intern, Celonis Inc
    New York, NY, USA, 2023.5 - 2023.8
  • Research Intern, Megagon Labs
    Mountain View, CA, USA, 2022.5 - 2022.8
  • Research Intern, DAMO Academy
    Alibaba Inc, Sunnyvale, CA, USA, 2019.1 - 2019.8
  • Research Intern, Database Group
    Microsoft Research, Redmond, WA, USA, 2018.7 - 2018.10


  • iFlipper: Label Flipping for Individual Fairness [paper]
    Hantian Zhang*, Ki Hyun Tae*, Jaeyoung Park, Xu Chu, Steven Euijong Whang
    SIGMOD 2023

  • OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning [paper]
    Hantian Zhang, Xu Chu, Abolfazl Asudeh, Sham Navathe
    SIGMOD 2021

  • FairRover: explorative model building for fair and responsible machine learning [paper]
    Hantian Zhang, Nima Shahbazi, Xu Chu, Abolfazl Asudeh
    DEEM workshop at SIGMOD 2021

  • ALEX: An Updatable Adaptive Learned Index [paper]
    Jialin Ding, Umar Farooq Minhas, Jia Yu, Chi Wang, Jaeyoung Do, Yinan Li, Hantian Zhang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David Lomet, Tim Kraska
    SIGMOD 2020

  • Accelerating generalized linear models with MLWeaving: a one-size-fits-all system for any-precision learning [paper]
    Zeke Wang, Kaan Kara, Hantian Zhang, Gustavo Alonso, Onur Mutlu, Ce Zhang
    VLDB 2019

  • Speeding up Percolator [paper]
    John T Halloran, Hantian Zhang, Kaan Kara, Cedric Renggli, Matthew The, Ce Zhang, David M Rocke, Lukas Käll, William Stafford Noble
    Journal of Proteome Research 2019

  • MLBench: Benchmarking Machine Learning Services Against Human Experts [paper]
    Yu Liu, Hantian Zhang, Luyuan Zeng, Wentao Wu, Ce Zhang
    VLDB 2018

  • Generative adversarial networks as a tool to recover structural information from cryo-electron microscopy data [paper]
    Min Su, Hantian Zhang, Kevin Schawinski, Ce Zhang, Michael A Cianfrocco
    bioRxiv 2018

  • PSFGAN: a generative adversarial network system for separating quasar point sources and host galaxy light [paper][project]
    Dominic Stark, Barthelemy Launet, Kevin Schawinski, Ce Zhang, Michael Koss, M Dennis Turp, Lia F Sartori, Hantian Zhang, Yiru Chen, Anna K Weigel.
    MNRAS 2018

  • ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning [paper]
    Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, Ce Zhang
    ICML 2017

  • Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit. [paper] [project]
    Ce Zhang, Kevin Schawinski,Hantian Zhang, Lucas Fowler, Gokula Krishnan Santhanam
    MNRAS 2017

  • Scalable Inference of Decision Tree Ensembles: Flexible Design for CPU-FPGA Platforms [paper]
    Muhsen Owaida, Hantian Zhang, Ce Zhang, Gustavo Alonso
    FPL 2017