Haifeng Xu

Haifeng Xu is an Associate Professor in Computer Science and Data Science at the University of Chicago, and directs the Strategic IntelliGence for Machine Agents (SIGMA) research lab which focuses on multi-agent AI. His works have been recognized by a few major awards including AI2050 Early Career Fellow, IJCAI Early Career Spotlight, a Best Paper at the Web Conference and a Best Student Paper at AAMAS. Haifeng publishes regularly at leading venues in machine learning and computational economics, and serves as area chair or senior program committee for venues such as ICML, NeurIPS, EC, AAAI, etc.

AI2050 Project

Web applications such as Instagram and Google enable varied ways to access rich online contents and give rise to different content ecosystems, each with its own content creators and user demand. However, generative AIs such as Midjourney and GPT-4 introduce disruptive changes to these ecosystems due to their unprecedentedly new methods for creating contents. If not addressed carefully, these shifts could result in various risks such as uneven human-AI competition and distorted creator incentives. Haifeng Xu’s AI2050 project develops computational methods to understand how generative AIs affect content creation and how to optimize the content ecosystem’s design in the generative-AI era.

Project Artifacts

AI2050 Community Perspective — Haifeng Xu (2025)

S.A. Esmaeili, K. Bhawalkar, Z. Feng, D. Wang, H. Xu. How to Strategize Human Content Creation in the Era of GenAI?. arXiv. 2024.

Y. Cheng, F. Yao, X. Liu, and H. Xu. Learning from Imperfect Human Feedback: a Tale from Corruption-Robust Dueling. arXiv. 2024.

M. Castiglioni, J. Chen, M. Li, H. Xu, and S. Zuo. A reduction from multi-parameter to single-parameter bayesian contract design. arXiv. 2024.

F. Yao, C. Li, D. Nekipelov, H. Wang, and H. Xu. Human vs. generative AI in content creation competition: symbiosis or conflict?. arXiv. 2024.