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featuring Introduction to UVA RC GenAI

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News and Events

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Your First API Call? Intro to High Performance Computing (AI Exchange @ UVA) (opens in a new tab)

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Update

HPC Maintenance: Oct 13, 2026

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Join Us for Our Fall Training Series

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Discussion about hardware at 2025 Research Computing Open House

What to Expect at Research Computing's Open House on Sept. 9

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Featured Collaborations

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Bradycardia and Desaturation Events in Infants

Episodes of bradycardia and oxygen desaturation (BD) are common among preterm very low birthweight (VLBW) infants and their association with adverse outcomes such as bronchopulmonary dysplasia (BPD) is unclear. A better understanding of this relationship could lead to improved clinical interventions.
Bradycardia and Desaturation Events in Infants
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Binding Analysis for Regulation of Transcription

BART (Binding Analysis for Regulation of Transcription) Web

Working with researchers in the Zang Lab in the Center for Public Health Genomics (CPHG), RC helped launch BARTweb, an interactive web-based tool for users to analyze their Genelist or ChIP-seq datasets. BARTweb is a containerized Flask front-end (written in Python) that ingests files and submits them to a more robust Python-based genomics pipeline running on Rivanna, UVA’s high performance computing cluster (HPC).
BART (Binding Analysis for Regulation of Transcription) Web
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PLAN-EO

Planetary Child Health and Enterics Observatory (Plan-EO)

The Planetary Child Health and Enterics Observatory (Plan-EO) is an interdisciplinary research initiative at UVA. Its mission is to provide researchers and public health stakeholders with diverse spatial data tools for the geographical targeting of enteropathogen-specific child health interventions. 
Planetary Child Health and Enterics Observatory (Plan-EO)
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Featured Collaboration One

Deep Learning for Glaucoma Patient Risk Stratification

Glaucoma is a chronic eye disease that can lead to irreversible vision loss if not properly managed. Effectively stratifying patient risk is a major clinical challenge, as clinicians need tools to identify patients at high risk of progression from sparse and irregularly-sampled electronic health records.
Deep Learning for Glaucoma Patient Risk Stratification
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