A Good System for Smart Cities


AI technologies can build safer and smarter cities and help us monitor and evaluate how new and critical infrastructure will affect access to housing, jobs, and public services, but city datasets are fragmented across domains and practically inaccessible to the general public. This project seeks to build an AI system that will link city datasets – extracting useful information, identifying data bias, making better and safer decisions within complex and dynamic environments, and ensuring responsible data use. Building on these different datasets, this research will also develop digital twin models for different critical infrastructures (e.g. fire, transportation, water, energy) to monitor the status, visualize different scenarios, and mitigate emerging challenges in critical infrastructures.

Team Members

Civil, Architectural and Environmental Engineering
Arya Farahi
Statistics and Data Science
Devrim Ikizler
Dev Niyogi
Geological Sciences and Civil, Architectural, and Environmental Engineering
Andrew Waxman
Lyndon B. Johnson School of Public Affairs
Mingyuan Zhou
Information, Risk and Operations Management
Electrical and Computer Engineering