NYC School of Data 2026

Join us for a practical, data-driven look at the future of homelessness risk in New York City. In this session, four NYC students (Rohan Ramnarain, Marilyn Echeverria, Kevin Guillermo, and Alice Dong) will present three predictive models that forecast which NYC neighborhoods may face the greatest risk of increased homelessness over the next few years, and what the signals behind those predictions suggest for prevention and response.

Participants will spend the time in a live, interactive demo, exploring how the models work and how outputs can be interpreted, followed by an open Q&A with the presenters. This event is designed for anyone who cares about homelessness in New York City, including advocates, service providers, policy folks, community leaders, researchers, and residents who want to understand how predictive algorithms can support earlier and more targeted interventions.