Data-driven Decision-making in School Matching
Abstract: School choice mechanisms determine how students are assigned to public schools, striving to balance individual preferences, school priorities, and broader goals of fairness. While simple, well-studied algorithms have strong theoretical guarantees, real-world applications face practical constraints and policy goals that complicate implementation. We focus on three challenges in modern school choice systems: determining the optimal length of student preference lists, designing the allocation of reserved seats, and targeting scholarship allocations to support disadvantaged students. For these problems, we explore how data can be used to come up with relevant models, and how solutions to the latter lead to policy recommendations. Based on multiple works with Swati Gupta (MIT), Aapeli Vuorinen (Columbia) and Xuan Zhang (Meta).
Bio sketch: Hi. I am an associate professor in the the Department of Industrial Engineering and Operations Research and a member of the Data Science Institute at Columbia University. My research currently focuses on Discrete Optimization and Operations Research, and their interactions with / applications to other areas. I work on both fundamental properties and efficient algorithms. Topics of interests include Knapsack, Matching, Extended Formulations, and the interplay of Mathematical Programming and Computer Science. My favorite application is Market Design, especially in the context of School Choice. I also investigate the interactions between Discrete Optimization and Machine Learning. My projects have been funded by the NSF (including an NSF CAREER award), the AFOSR, the ONR, the Swiss NSF, and by a Meta Research Award. You can find my CV here.
I am / have been in the program committees of ALGA 2025, EC (2025, 2024), APPROX 2023, IPCO (2023, 2020), MIP (2022 (chair), 2021), CTW 2020. I am an associate editor for Mathematical Programming, Operations Research Letters, and Discrete Optimization. I am in the organizing commitee of the Cargese Workshop in Combinatorial Optimization (odd years).
I joined Columbia in Fall 2016. Prior to that, I was a SNSF Ambizione fellow, and a post-doctoral researcher at the University of Brussels, EPFL, and University of Padua. I received my PhD in Operations Research from the Sapienza University of Rome, under the supervision of Gianpaolo Oriolo.
Bio sketch: Hi. I am an associate professor in the the Department of Industrial Engineering and Operations Research and a member of the Data Science Institute at Columbia University. My research currently focuses on Discrete Optimization and Operations Research, and their interactions with / applications to other areas. I work on both fundamental properties and efficient algorithms. Topics of interests include Knapsack, Matching, Extended Formulations, and the interplay of Mathematical Programming and Computer Science. My favorite application is Market Design, especially in the context of School Choice. I also investigate the interactions between Discrete Optimization and Machine Learning. My projects have been funded by the NSF (including an NSF CAREER award), the AFOSR, the ONR, the Swiss NSF, and by a Meta Research Award. You can find my CV here.
I am / have been in the program committees of ALGA 2025, EC (2025, 2024), APPROX 2023, IPCO (2023, 2020), MIP (2022 (chair), 2021), CTW 2020. I am an associate editor for Mathematical Programming, Operations Research Letters, and Discrete Optimization. I am in the organizing commitee of the Cargese Workshop in Combinatorial Optimization (odd years).
I joined Columbia in Fall 2016. Prior to that, I was a SNSF Ambizione fellow, and a post-doctoral researcher at the University of Brussels, EPFL, and University of Padua. I received my PhD in Operations Research from the Sapienza University of Rome, under the supervision of Gianpaolo Oriolo.
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Organizer
- Daniel Kuhn, Andres Cristi
Contact
- cdm-seminars@epfl.ch