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.


Interested in joining our group? Please read this.


Contact


Office: Crerar 260

E-mail: haifengxu AT uchicago DOT edu


About My Research


I work on the economics of machine learning — i.e., the economic aspects of machine learning itself and, conversely, designing ML algorithms for economic problems (see my survey paper here and also example research below). More broadly, I am interested in decision making and machine learning in multi-agent setups, particularly in informationally complex environments with limited or asymmetric access to information. Please see our Sigma Research Lab for more details.

  1. Economics for machine learning (e.g., acquiring, selling data or machine learning models)

    2. Representative Papers:

    1. Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning

      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. Optimal Pricing of Information

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

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


    3. Algorithmic Bayesian Persuasion

      (α-β) Shaddin Dughmi, Haifeng Xu

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


  2. Machine learning for economics (e.g., multi-agent learning for game-theoretic decision making)

    3. Representative Papers:

    1. How Bad is Top-K Recommendation under Competing Content Creators?

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

      ICML 2023: Proc. 40th International Conference on Machine Learning, 2023 (live presentation)


    2. Multi-Agent Learning for Iterative Dominance Elimination: Formal Barriers and New Algorithms

      (α-β) Jibang Wu, Haifeng Xu and Fan Yao

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


    3. PAC-Learning for Strategic Classification

      (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%)



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