My name is Peng Shi and I am an Assistant Professor in the Department of Data Science and Operations at the USC Marshall School of Business.

I am interested in developing mathematical models and techniques that can significantly benefit society. My current focus is prediction and optimization in matching markets, which include systems that match students to schools, applicants to subsidized housing, workers to jobs, and organ donors to recipients. Characteristics of these systems include heterogeneous supply and demand, potential strategic behavior of agents, and inability of obtaining the desired allocation by only setting prices. My PhD thesis, "Prediction and optimization in school choice", was motivated by school choice in Boston, for which I proposed an assignment plans that was adopted in March 2013 (for news coverage, see Boston Globe 2012/10/27, NY Times 2013/3/13, and NY Times 2013/3/15). I am currently working on developing theories and methodologies to assist future school choice reforms, and on applying optimization to other matching markets, with applications for subsidized housing allocation, digital platforms, and ride-sharing.

Working Papers

How (Not) to Allocate Affordable Housing (with Nick Arnosti). Updated 2017/11.

  • An earlier version appeared in EC' 17.

How Well Do Structural Demand Models Work? Counterfactual Predictions in School Choice (with Parag Pathak). Updated 2017/11. See here for Part I report.

Communication Requirements and Informative Signaling in Matching Markets (with Itai Ashlagi, Mark Braverman, and Yash Kanoria). Updated 2017/07.

  • An earlier version appeared in EC' 17.

Assortment Planning in School Choice. (Preliminary Draft). Updated 2016/01.

Journal Publications

Optimal Allocation without Money: an Engineering Approach (with Itai Ashlagi). Management Science, 62(4), 2016.

Guiding School-Choice Reform through Novel Applications of Operations Research. Interfaces, 45(2), 2015.

Improving Community Cohesion in School Choice via Correlated-Lottery Implementation (with Itai Ashlagi). Operations Research , 62(6), 2014.

Approximation algorithms for restless bandit problems (with Kamesh Munagala and Sudipto Guha). Journal of the ACM (JACM) , 58(1), 2010.

Refereed Conference Proceedings

Prediction Mechanisms that Do Not Incentivize Undesirable Actions (with Vincent Conitzer and Mingyu Guo). Appeared in WINE'09.

Approximation algorithms for restless bandit problems (with Kamesh Munagala and Sudipto Guha). Appeared in SODA'09.

The Stochastic Machine Replenishment Problem (with Kamesh Munagala). Appeared in IPCO'08.


USC Marshall School of Business:

  • Spring, 2018: Instructor for DSO 570 (The Analytics Edge: Data, Models, and Effective Decisions).

MIT Sloan School of Management:

Duke University: