ORIE Colloquium: Alfredo Torrico (Cornell)

Efficiency and Design Challenges in Two-sided Matching Markets

Two-sided matching markets have become essential for public and private organizations to address various societal concerns. Despite their variety, these markets share key challenges:

Efficiency: They must manage “supply” and “demand” in a context where preferences/choices are highly correlated.Design: They must account for structural requirements that determine how agents interact and are matched.On top of these challenges, organizations must be able to operationalize these markets in large-scale instances under different, and often conflicting, desiderata. As a result, trade-offs naturally appear, especially between ensuring theoretical guarantees and designing scalable algorithmic solutions.

In this talk, I will discuss how we address these challenges for:

Choice-based (decentralized) matching platforms (e.g., Airbnb, Tinder, Upwork). I will mostly focus on a two-sided assortment optimization framework where we analyze various platform types and develop efficient algorithms with provable guarantees.Centralized matching mechanisms (e.g., school choice, hospital-resident allocation). I will briefly discuss our approach to accommodate sibling applications in school choice and our results in the Chilean admission system.
Bio: Alfredo Torrico is an assistant research professor in the Center for Data Science for Enterprise and Society at Cornell University. Prior to joining Cornell, he was a postdoctoral researcher in the CERC for Data Science at Polytechnique Montreal. He obtained his Ph.D. in operations research at Georgia Tech in 2019. He also holds a mathematical engineering degree from Universidad de Chile. His research aims to bridge theory and practice by using applied modeling and methodological tools from optimization, game theory and algorithm design to analyze pressing issues that public and private services face in urban centers. Some examples of his recent work include equitable congestion pricing and its application to Bogota, capacity planning in centralized mechanisms and its application to the Chilean school choice system, and algorithmic solutions for choice-based matching platforms.