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, and customers to service providers. Characteristics of these systems include heterogeneous supply and demand, potential strategic behavior of agents, and the 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 applying optimization to other matching markets, with applications in subsidized housing allocation, organ allocation, and online two-sided marketplaces.

Working Papers

Optimal Matchmaking Strategy in Two-Sided Marketplaces. Updated 2021/07.

  • An earlier version was titled "Efficient Matchmaking in Assignment Games with Application to Online Platforms," and appeared in EC'20.

Journal Publications

Optimal Priority-Based Allocation Mechanisms. Management Science, 2021.

How Well Do Structural Demand Models Work? Counterfactual Predictions in School Choice (with Parag Pathak). Journal of Econometrics, 222(1A), 2021. See here for Part I report.

Design of Lotteries and Waitlists for Affordable Housing Allocation (with Nick Arnosti). Management Science, 66(6), 2019.

Communication Requirements and Informative Signaling in Matching Markets (with Itai Ashlagi, Mark Braverman, and Yash Kanoria). Management Science, 66(5), 2019.

  • An earlier version appeared in EC'17.

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.

Teaching

USC Marshall School of Business:

  • Fall, 2020: Instructor for DSO 570 (The Analytics Edge: Data, Models, and Effective Decisions).
  • Spring, 2020: Instructor for DSO 570 (The Analytics Edge: Data, Models, and Effective Decisions) and DSO 599 (Introduction to Python for Business Analytics).
  • Spring, 2019: Instructor for DSO 570 (The Analytics Edge: Data, Models, and Effective Decisions) and DSO 599 (Introduction to Python for Business Analytics).
  • Spring, 2018: Instructor for DSO 570 (The Analytics Edge: Data, Models, and Effective Decisions).

MIT Sloan School of Management:

Duke University: