About Me


I am an assistant professor in the Computer Science Department at UChicago, and direct the Sigma Research 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.


Contact


Office: Crerar Building, Room 260

E-mail: haifengxu AT uchicago DOT edu


About My Research


I study intelligent decision making and machine learning in non-cooperative multi-agent environments, particularly in informationally complex setups (e.g., with asymmetric or limited access to information). The following are the main themes of my recent research. 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 incentive conflicts and information asymmetry in 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

      A shorter version appeared at ICML 2021 (long oral, 3%)


    3. (α-β) Zhe Feng, David C. Parkes, Haifeng Xu

      ICML 2020: Proc. 37th International Conference on Machine Learning, 2020.


  5. Resource Allocation in Adversarial Domains (with applications to security, privacy protection, etc.)

  6. Representative Papers:
    1. Haifeng Xu, Kai Wang, Phebe Vayanos and Milind Tambe

      AAAI 2018: Proc. 32nd AAAI Conference on Artificial Intelligence. (oral presentation)


    2. Haifeng Xu

      EC 2016: Proc. 17th ACM Conference on Economics and Computation, 2016.


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


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