Abstract: More than 65 years after the “Brown v. Board of Education” ruling that school segregation is unconstitutional, public schools across the U.S. are resegregating. In attempts to disentangle school segregation from neighborhood segregation, many cities have adopted policies for city-wide choice. However, these policies have largely not improved patterns of segregation. From 2018-2020, we worked with the San Francisco Unified School District (SFUSD) to design a new policy for student assignment system that meets the district’s goals of diversity, predictability, and proximity. To develop potential policies, we used optimization techniques to augment and operationalize the district’s proposal of restricting choice to zones. We compared these to district-wide choice approaches typically suggested by the school choice literature. We find that appropriately-designed zones with minority reserves can achieve all the district’s goals, at the expense of choice, and choice can resegregate diverse zones. Using predictive choice models developed using historical choice data, we show that a zone-based policy can decrease the percentage of racial minorities in high-poverty schools from 29% to 11%, decrease the average travel distance from 1.39 miles to 1.29 miles, and improve predictability, but reduce the percentage of students assigned to one of their top 3 programs from 80% to 59%. Traditional district-wide choice approaches can improve diversity and choice at the expense of proximity. Our work informed the design and approval of a zone-based policy for use starting the 2024-25 school year. Bio: Irene is an assistant professor in Management Science & Engineering at Stanford University. Her research is on designing matching markets and assignment processes to improve market outcomes, with a focus on public sector applications and socially responsible operations research. She is also interested in mechanism design for social good and graph theory.