Shengwei Xu

Shengwei Xu

PhD Candidate

About Me

I am Shengwei Xu, a 4th-year Ph.D. candidate at the University of Michigan’s School of Information, advised by Prof. Grant Schoenebeck. I received my B.S. in Computer Science from Peking University in 2021, advised by Prof. Yuqing Kong.

Research Interests

My current research investigates strategic behavior in interactions between agents (human and AI) and algorithm, focusing on mechanism design, information elicitation, and their intersection with Large Language Models.

Publications

  • Benchmarking LLMs’ Judgments with No Gold Standard.
    In Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025)
    S. Xu*, Y. Lu*, G. Schoenebeck, Y. Kong.
    [Link]

  • Eliciting Informative Text Evaluations with Large Language Models.
    In Proceedings of the 25th ACM Conference on Economics and Computation (EC’24)
    Y. Lu*, S. Xu*, Y. Zhang, Y. Kong, G. Schoenebeck.
    [Link] [Oral]

  • Spot Check Equivalence: An Interpretable Metric for Information Elicitation Mechanisms.
    In Proceedings of the ACM Web Conference 2024 (WWW’24)
    S. Xu*, Y. Zhang, P. Resnick, G. Schoenebeck.
    [Link] [Oral]

  • BONUS! Maximizing Surprise.
    In Proceedings of the ACM Web Conference 2022 (WWW’22)
    Z. Huang, Y. Kong, X. Liu, G. Schoenebeck, S. Xu.
    [Link]

  • SURPRISE! and When to Schedule It.
    In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)
    Z. Huang*, S. Xu*, Y. Shan, Y. Lu, Y. Kong, X. Liu, G. Schoenebeck.
    [Link]

Tutorials and Workshops

  • Ad Insertion in LLM-generated Responses. To appear in the Workshop on Frontiers of Online Advertising at the 26th ACM Conference on Economics and Computation (EC’25)

  • Information Elicitation Meets Large Language Models: Progress, Opportunities, and Challenges. Tutorial in the 20th Conference on Web and Internet Economics (WINE 2024) [Link]

Experiences

Education

  • University of Michigan, School of Information, Ph.D. candidate, 2021 - present.
  • Peking University, School of EECS, B.S. in Computer Science, Summa Cum Laude, 2017 - 2021.

Industry

  • Google, Software Engineering Intern (PhD), May 2025 – Aug 2025
    • Research and develop a policy classification model for ad safety that utilizes advertiser temporal behaviors.
    • Implement a temporal transformer neural network for multi-modality advertiser data with TensorFlow.

Teaching

  • Graduate Student Instructor
    University of Michigan
    • Mathematical Foundations for Applied Data Science (Winter 2024)
    • Applied Machine Learning (Fall 2023)
    • Data-Oriented Programming (Fall 2022)
  • Teaching Assistant
    Peking University
    • Discrete Mathematics and Structures (I) and (II) (Spring 2020 and Fall 2020)