Our Work Is Local, but Our Impact Is Global

This project examined how children from groups underrepresented in STEM programs understand, interact with, and evaluate AI-driven digital assistants. 

Information and cultural heritage professionals have become increasingly interested in using data science, machine learning, and AI applications in the management of data and operations at libraries, archives, museums.

Artificial intelligence systems increasingly automate and assist people in making managerial and governance decisions. AI manages worker routines and tasks, determines how to distribute resources within cities, and assists in transportation management.

One of the main tasks of any city government is to keep infrastructure such as roads, signs, accessibility ramps, and sewers in perpetual working order. The first step of maintenance is the timely identification of a problem. However, many infrastructure defects are left undetected and unattended for long periods of time

The widespread use of service robots has been predicted to be just around the corner for decades. This project seeks to overcome the prevailing technical and social hurdles to deploying these robots by building and studying them in communities where they will be used rather than just in laboratories and warehouses.

Advances in AI are shaping the future of work, but how can we ensure that employees, organizations, and society will benefit from the changes? Instead of automating tasks, which can reduce or eliminate the need for human workers, this project seeks to design smart hand tools that have embedded sensing and AI.

How can scientists effectively measure mood and stress over time? What are the best ways to partner with communities with access to fewer resources? A team from the UT School of Nursing partnered with Del Valle Independent School District to help answer those questions over the course of an 8-week mindfulness program in 2019.

The Austin History Center, the City of Austin, and UT Libraries maintain substantial collections of scanned maps, which are needed for historical reference when conducting research or planning new projects. Unfortunately, these scanned maps can be difficult to use due to the challenges involved with discovering, processing, and analyzing map images in their current digitized form.

In Texas, the most deadly and costly natural disaster is flooding. Preparation and response are limited because existing flood and risk maps don't account for the ways that river flooding, surface flooding, storm surges, and terrain interact.

Optimizing ambulance allocation and routing is one of the most efficient ways for EMS to save more lives at virtually no cost. However, current EMS software was developed under models that assume normal demands.