About Me

I am an assistant professor in the Department of Computer Science and Data Science Institute at UChicago. I direct the Sigma Lab (Strategic IntelliGence for Machine Agents).

Prior to UChicago, I was an assistant professor at UVA and (even before) a postdoc at Harvard hosted by Yiling Chen and David Parkes. I received a PhD in Computer Science from USC advised by Shaddin Dughmi and Milind Tambe (now at Harvard), after writing this dissertation which was recognized by the ACM SIGecom Dissertation Award and IFAAMAS Victor Lesser Distinguished Dissertation Award. My CV can be found here.


Office: Crerar 260

E-mail: haifengxu AT uchicago DOT edu

We are looking for 2-3 PhD students starting Fall 2023. Please read this and check out UChicago CS if interested!

About My Research

My recent research focus is to develop an economic foundation for data and machine learning, elaborated below. More broadly, I study decision making and machine learning in multi-agent setups, particularly in informationally complex environments (e.g., with asymmetric or limited access to information). Please see our Sigma Research Lab for more details.

  1. The economics of data (e.g., selling, acquiring, and exploiting data/information)

  2. Representative Papers:
    1. Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan and Haifeng Xu

      EC 2022: Proc. 23th ACM Conference on Economics and Computation, 2022.

    2. (α-β) Shuze Liu, Weiran Shen and Haifeng Xu

      EC 2021: Proc. 22th ACM Conference on Economics and Computation, 2021.

    3. (α-β) Shaddin Dughmi, Haifeng Xu

      SICOMP: SIAM Journal on Computing (Invited to special issue for STOC 2016)

  3. The economics of machine learning (e.g., addressing mis-aligned incentives in multi-agent learning)

  4. Representative Papers:
    1. (α-β) Jibang Wu, Haifeng Xu and Fan Yao

      COLT 2022: Proc. 35th Annual Conference on Learning Theory, 2022.

    2. (α-β) Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao

      The Journal of Machine Learning Research (JMLR), minor revision (appeared at ICML 2021 as long oral, 3%)

    3. Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang and Haifeng Xu

      ICML 2022: Proc. 39th International Conference on Machine Learning, 2022.

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