“All models are wrong, but some are useful.” George Box, 1976

I am interested in theoretically modeling (also integrating empirical validation) to explain emergent phenomena in complex systems and to uncover the underlying principles behind these "black-box" systems. Accordingly, my research focuses on complex systems within computer science, particularly in computational economics (game theory) and machine learning theory.

I am passionate about imparting knowledge in a clearer and more efficient manner. To this end, I have been extensively involved in course development work, including lecturing, writing textbooks and notes, and serving as a teaching assistant. Below are two textbooks I have primarily authored. For a complete overview of my teaching experience, please visit this page.

1. Linear Algebra Left Undone. Yihang Wu et al.
   • Textbook for the peer-assisted learning course in the CKC Honors College of Zhejiang University.
   • Open sourced on this page: https://github.com/yhwu-is/Linear-Algebra-Left-Undone
2. Data Element Trading. Jinfei Liu, Yihang Wu, Zhe Yuan, Kui Ren. Posts & Telecom Press, 2026.
   • Textbook for the course with the same name (CS2270M in Zhejiang University).

1. Arbitrage-free Data Pricing
   Yihang Wu, Zhengyu Jin, Yicheng Fu, Jinfei Liu, Kui Ren
   Working Paper
   Accepted at ACM EC 2026 Workshop on Foundations of Data Economics
2. Do AI Overviews Benefit Search Engines? An Ecosystem Perspective
   Yihang Wu, Jiajun Tang, Jinfei Liu, Haifeng Xu, Fan Yao
   Working Paper
3. When Data Pricing Meets Non-cooperative Game Theory
   Yuran Bi, Yihang Wu, Jinfei Liu, Kui Ren, Li Xiong
   ICDE 2024: IEEE 40th International Conference on Data Engineering

View full publications in this page

• Coming soon.

Download CV (PDF)