Haifeng Xu 2023 Early Career Fellow
Affiliation Assistant Professor, University of Chicago Hard Problem Solved the economic challenges and opportunities resulting from AI and its related technologies.

Haifeng Xu is an assistant professor in computer science at the University of Chicago, and directs the Strategic Intelligence for Machine Agents (SIGMA) lab which focuses on designing intelligent AI systems that can effectively learn and act in informationally complex multi-agent setups. His research interests include computational economics, machine learning, algorithm design, and multi-agent systems. Haifeng has been recognized by multiple awards, including Early Career Spotlight at the International Joint Conference on Artificial Intelligence (IJCAI), a Google Faculty Research Award, ACM SIGecom Dissertation Award (honorable mention) and IFAAMAS Distinguished Dissertation Award (runner-up). Prior to UChicago, he was the Alan Batson Assistant Professor at the University of Virginia, and even before that a postdoc at Harvard. Haifeng obtained his PhD in Computer Science from the University of Southern California, advised by Profs. Milind Tambe and Shaddin Dughmi.

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

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.