Student Assignment Design and Public Sector Research Collaborations
About This Video
Student assignment algorithms determine where children go to school – having far reaching implications for families, the education system, and for society as a whole. Motivated by operational challenges faced by the San Francisco Unified School District (SFUSD), our research team applies data science tools to develop, operationalize, and streamline algorithmic assignment policies in San Francisco. Our findings have informed policy decisions made by the SFUSD Board of Education, including guidelines for a new student assignment policy to be enacted in 2026. As an important part of successful applied research and public-sector collaboration, we work to maintain a strong working relationship with our partner organization and to navigate the challenges of balancing impact objectives and research goals.
In This Video
Research Engineer, Granica
Kaleigh is a research engineer working on data compression and machine learning problems for Granica. She recently completed her Ph.D. from ICME at Stanford with Professor Irene Lo and Professor Itai Ashlagi. Her doctoral work focused on computational approaches to improving equitable access to education in the San Francisco Unified School District, and the project has influenced recent education policy decisions in the city.