Shuran Zheng 郑舒冉

Post-Doctoral Researcher in SCS at Carnegie Mellon University

shuranzh[at]andrew.cmu.edu

I am a Post-Doctoral Researcher in the Software and Societal Systems Department at Carnegie Mellon University working with Steven Wu. Before coming to CMU, I obtained my Ph.D. in Computer Science at Harvard University, where I was advised by Yiling Chen in EconCS group. 

Broadly speaking, my research is situated at the intersection of Economics and Computer Science, a field that is also known as Economics and Computation. In particular, I am interested in understanding the value of information and designing economic mechanisms when information and uncertainty are involved. My research uses concepts and tools from Economics (especially Mechanism Design), Machine Learning, and Algorithm Design. Here are some research topics that I am actively working on:

During the fall of 2022, I was a Student Researcher in the Market Algorithms Group at Google Research NYC, where I closely worked with Renato Paes Leme, Jon Schneider, and Balasubramanian Sivan. I am going to join IIIS, Tsinghua University as a tenure-track Assistant Professor in the Spring of 2024.

News: 

Haifeng and James and I are giving a tutorial "The Economics of Data and Machine Learning" at AAAI-23 on Feb. 8th. Check out our website here!

Publications


Private Interdependent Valuations

Alon Eden, Kira Goldner, and Shuran Zheng.

In Proc. of ACM-SIAM Symposium on Discrete Algorithms (SODA 2022).


The Limits of Multi-task Peer Prediction 

Shuran Zheng, Fang-Yi Yu, and Yiling Chen. 

In Proc. of the 22nd ACM Conference on Economics and Computation (EC 2021). [arxiv] [slides at Columbia Theory Siminar] 


Optimal Advertising for Information Products 

Shuran Zheng and Yiling Chen.

In Proc. of the 22nd ACM Conference on Economics and Computation (EC 2021). [arxiv] [slides] [talk]

Invited to the 2021 INFORMS Annual Meeting.


Truthful Data Acquisition via Peer Prediction

Yiling Chen, Yiheng Shen, and Shuran Zheng.

In Proc. of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020). [arxiv] [slides] [poster]


Selling Information Through Consulting 

Yiling Chen, Haifeng Xu, and Shuran Zheng. 

In Proc. of ACM-SIAM Symposium on Discrete Algorithms (SODA 2020). [arxiv] [slides]

Presented in ACM/INFORMS Workshop on Market Design 2019. 


Prior-free Data Acquisition for Accurate Statistical Estimation 

Yiling Chen and Shuran Zheng.

In Proc. of the 20th ACM Conference on Economics and Computation (EC 2019). [arxiv] [slides]


Active Information Acquisition for Linear Optimization

Shuran Zheng, Bo Waggoner, Yang Liu, and Yiling Chen. 

In Proc. of the Conference on Uncertainty in Artificial Intelligence (UAI 2018). [arxiv] [poster]


Complexity and Algorithms of K-implementation 

Yuan Deng, Pingzhong Tang, and Shuran Zheng.

In Proc. of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016). [paper]


Teaching


AM 121 Introduction to Optimization: Models and Methods. Havard University. Teaching Fellow. Fall 2018. 

AM 122 Convex Optimization and Applications. Harvard University. Teaching Fellow (develop a new course). Spring 2021.