Renaissance Computing Institute

MM-ARIEL is a cutting-edge research project aimed at revolutionizing the diagnosis and treatment of lupus and related autoimmune diseases through advanced multimodal machine learning approaches. This initiative focuses on integrating diverse patient data—including text, images, omics, activity monitor data, and electronic health records—into unified models that support early, equitable, and efficient diagnosis. The project prioritizes reducing health disparities, improving diagnostic accessibility, and addressing biases in data and models, particularly for racially and ethnically minoritized women who are disproportionately affected by lupus. MMR-Lupus is designed to enhance diagnostic capabilities for primary care physicians, empowering them to assess complex diseases without requiring immediate specialist intervention.

Trust Not Included is an ongoing RENCI umbrella initiative investigating the relationship between consumer trust and health data in the AI age. Initial investigations began with the Breathe Easy project. Further stages of this project include embedding investigations in larger, funded projects; securing funding for further investigations into the relationship between trust and health data and identifying practical ways to apply rubrics and standards to health data consumption. The initiative is also investigating the ways in which data generators and producers can signal trust messages to data consumers.

Trust Not Included hosts open sessions and discussions on trust and health data, particularly relating to AI use. RENCI staff are presenting on this work at conferences in Europe and the US. The Trust Not Included team is curating published resources relating to this work.

This work continues to collaborate with researchers internationally including Associate Professor Lynelle Watts, of Curtin University, Australia.

The project will benefit scientific applications that rely on change detection. Moreover, this project will enhance the viability of making programmable networks an inherent computing element in the scientific data processing pipeline, effectively making the technology widely available to the scientific community. Similar to cloud environments, in-transit computing environments distributed across campuses will onboard the scientific computing community to leverage the benefits of high speed programmable networks.

Monkey Health Explorer is a participatory science project that is part of a larger collaborative project (with the NC Museum of Natural Sciences and colleagues from numerous universities from around the world) studying monkey health, social behavior, and aging. The larger multidisciplinary team includes geneticists, behavioral scientists, neuroscientists and others, studying rhesus macaque monkeys on Cayo Santiago island in Puerto Rico. Rhesus macaque monkey behavior has been studied for decades, and genetics and health assessments are now expanding. Blood smear slides help assess monkey health, and scanned images are run through an AI pipeline so individual white blood cell images can be uploaded to the people-powered science platform, Zooniverse.org, for classification help. Public participants trained in medical or veterinary fields greatly enhance the knowledge gained, by identifying monkeys with potential health conditions not originally investigated. This project engages K-12 classrooms across North Carolina and Puerto Rico to expand participant reach, while providing real-world research examples and early career training to students interested in health and data science careers.