Texas Water Stories studied and produced narratives about water in Texas’ past, present, and future. The project illustrated the diverse symbolic ties between water, environmental perception, and environmental change due to the climate crisis and shows how water is a central element of human and other-than-human environmental relations.

The Texas Metro Observatory (TMO) is a communication and data platform dedicated to sharing information and ideas about Texas’s communities, understanding common problems related to urbanization processes in these communities, and developing solutions across the state’s metropolitan areas. 

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. 

The importance of communicating the dangers resulting from impending climate collapse is perhaps the most vital issue to be communicated. This project seeks to explore the greeting card as an everyday cultural form that can be used to express ideas and connect people around the subject of climate change. 

This project explored how theater and community engagement can help develop a context-specific understanding of climate change to empower Texan communities and individuals to become resilient and adapt to a changing climate.

This project develops methodology and workflows for libraries, archives, and museums to use machine learning and supercomputing resources to generate metadata for AV materials in the humanities. 

This project examines how human-centered approaches to assess bias and fairness can address a critical gap to inform research on algorithmic fairness. 

This project designs and prototypes new ways to find, interpret, and evaluate online information with the goal of helping to combat rampant misinformation. It studies how people evaluate and integrate information from disparate online sources, focusing on fact-checking as it relates to the COVID-19 pandemic. 

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

This project reports on how media representations shape public perceptions of AI and then uses findings to explore how Good Systems might better represent everyday interactions with AI to the public.