Our Work Is Local, but Our Impact Is Global

This project addresses the conflict between convenience and privacy inherent to computer vision with the goal of developing future computer vision technologies that support diverse users with visual impairments, especially those who are traditionally underserved.   

This project considers high confidence policy learning as an evaluation criterion for good systems and investigates new algorithms for meeting this criterion. 

Participation in physical activity is associated with better performance in school, but most children do not meet the recommendations of the 2008 Physical Activity Guidelines for Americans for 60 minutes of daily moderate-to-vigorous physical activity.

This project investigated the role that nonprofits based in Texas with an environmental focus play in securing a future that benefits all state residents. 

Pioneering new methods to explain and predict changes in ecosystems by monitoring wildlife, weather, soil, disease-carrying pathogens, and more to better understand the effects of climate change and human activity on nature and human health.

The record of human attempts to deal with environmental and demographic challenges is like a library of completed experiments. We can see which ones were successful and which ones weren’t, and we can trace the consequences of societal choices over hundreds of years. 

Externalizing disorders, including behaviors such as tantrums, defiance, and aggression, are among the most common and costly disorders of childhood. Behavioral indicators of these disorders often emerge by the third year of life, but current methods of identifying them are often time-intensive and expensive.

We are working with several City of Austin offices to confront the problem of homelessness. People experience homelessness as a continuum, ranging from housing instability to incarceration, couch-surfing to street homelessness, and episodic to chronic.

Reexamining the past to inspire a new vision of human resilience and effective response to the climate crisis.

This research project developed an evacuation scheduling algorithm that can determine optimal planning for large-scale, complex settings to minimize delays, with particular attention paid to the benefits of autonomous vehicles in terms of cost and efficiency.