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

Arya Farahi
Statistics and Data Science
Project Co-Lead
Catherine Cubbin
Steve Hicks School of Social Work
Devrim Ikizler
Paul Navratil
Texas Advanced Computing Center
Dev Niyogi
Geological Sciences and Civil, Architectural, and Environmental Engineering
Kijin Seong
Texas Advanced Computing Center
Yuke Zhu
Computer Science


Feb. 8, 2023
UT-Austin researchers partner with Austin Fire Department to predict, map smoke impact on communities
University researchers partnered with the Austin Fire Department to strengthen responses to fires and analyze the smoke’s environmental impact in various communities to instill a proper plan for communities at risk of unhealthy air quality.