Current Projects 

Title: AI-FEED - Connecting Food Charities to End Hunger: Artificial-Intelligence-Based Decision Support for Equitable Food and Nutrition Security in the Houston Area

Web site: ai-feed.ai

Description: Combating Food Insecurity Through A.I.: Although a well-resourced nation, 12% of Americans lack nutritious food to live a healthy life. AI-FEED is an innovative artificial intelligence platform to fix the broken food charity ecosystem. Beginning in Texas and moving across the U.S., AI-FEED will be a game changer for significant action to support nutrition security. It connects food charities, donors, clients, and community leaders to optimize food resources and support the flourishing of the greater community.

Sponsor: NSF

Title: AI-MET - Artificial Intelligence-driven differentiation between Multisystem Inflammatory Syndrome in Children and Endemic Typhus in pediatric patients: The PreVAIL-KIDS Common Protocol

Web site: ai-met.io

Description: The AI-MET project is a groundbreaking endeavor that focuses on developing a Deep Learning-based method to accurately differentiate between Multisystem Inflammatory Syndrome in Children (MIS-C) and Endemic Typhus, two diseases with similar clinical presentations but vastly different treatment approaches. MIS-C is a rare but severe condition primarily affecting children, often post-COVID-19, while Endemic Typhus is a bacterial infection transmitted by fleas or mites. These similarities in symptomatology often lead to misdiagnoses and delayed treatment, posing a serious threat to patient outcomes.

Sponsor: NIH

Title: AI-SERVE - Revolutionizing Emergency Management with AI-Driven Food Distribution: Artificial-Intelligence-Based Decision Support for Equitable and Resilient Food Distribution During Pandemics and Extreme Weather Events

Web site: ai-serve.org

Description: The AI-SERVE project stands at the forefront of innovation, aiming to transform the emergency management landscape. Leveraging the power of Artificial Intelligence, AI-SERVE seeks to revolutionize how we approach food distribution during pandemics and extreme weather events. Its mission is clear: to bring automation technology to the heart of crisis response, ensuring equitable and resilient food distribution for all, especially those in dire need.

Sponsor: NSF

Title: AISNIPS - AI Support for Network Intelligence-based Pharmaceutical Security: Financial and Network Disruptions in Illicit and Counterfeit Medicines Trade

Web site: ai-snips.org

Description: AI-SNIPS is a forward-thinking initiative and comprehensive applied research project designed to combat illicit drug sales through innovative methods. The critical components of the project include web scraping, machine learning, and network analysis, each contributing to a robust data pipeline. AI-SNIPS seeks to provide a unique and physics-based perspective for identifying and disrupting networks of illicit pharmaceutical sellers. By combining web scraping, machine learning, and network analysis, the project aims to uncover patterns and connections that may not be apparent through traditional methods. AI-SNIPS emphasizes the importance of clear communication through data visualization by producing visually compelling representations of the modeling decisions made during the analysis. This will enable stakeholders, including pharmaceutical companies and law enforcement, to quickly understand the insights derived from the data. Lastly, AI-SNIPS recognizes illicit networks' dynamic nature and incorporates performance analysis into its framework. By continually assessing the network dynamics, the project aims to enhance its machine learning models, ensuring they remain effective in identifying optimal points for disruption.

Sponsor: NSF

Title: AIM-AHEAD Resource Center of Excellence at U.H. for Data Curation, Linkages, and Harmonization of Datasets

Web site: arch-d2h.ai

Description: The project aims to establish the AIM-AHEAD Resource Center of Excellence at the University of Houston (UH) for Data Curation, Linkages, and Harmonization of Datasets. We will assess UH's data and infrastructure capacity, provide AI/ML training for staff, and build multidisciplinary and multi-institution partnerships.

Sponsor: NIH

Title: Computational social listening: application to healthcare information: TEXAS CLEAL Alliance Member

Web site: ireadusa.ai

Description: iReadUSA is an AI-powered project that compiles and verifies health data from various sources. It creates a website tailored to the specific needs of a Texas county (or the entire USA), ensuring the accessibility of accurate health information to vulnerable populations.

Sponsor: NIH

Title: CRASA - Community Responsive Algorithms for Social Accountability

Web site: uh-crasa.org

Description: CRASA is a multidisciplinary, community-based participatory research program to develop an algorithm accountability benchmark to meet societal and legal needs and guide best practices. The research program involves several key steps. Firstly, stakeholders are interviewed, and a Community Advisory Board is established to gain insight into public policy needs. Legal frameworks have been reviewed to shape AI algorithm accountability. Following this, an algorithm accountability benchmark is being developed, outlining specific standards for community, auditors, and legal evaluation. Behavioral experiments are conducted to ensure alignment of algorithm design with social concerns. Finally, software scoring tools are being developed based on the accountability benchmark to assess public policy applications such as criminal recidivism assessment and facial recognition.

Sponsor: NSF