The University of Rhode Island’s AI research efforts span several interdisciplinary hubs, including the Institute for AI & Computational Research (IACR), ITS Research Computing, the INBRE Molecular Informatics Core, and the Translational Neurorobotics Laboratory, each working to advance AI, data science, high-performance computing, bioinformatics, and medical robotics. These centers enable cutting-edge computational capabilities, equipment, and expertise to support innovation across fields ranging from environmental and biomedical research to neurorehabilitation.
URI in the News
Drew Zhang Did AI Before AI Was Cool – University of Rhode Island Magazine
NCURA Magazine: Playing it Smart with AI in Proposal Writing and Review
Institute for AI & Computational Research (IACR)
The Institute for AI & Computational Research (IACR) at the University of Rhode Island supports and advances interdisciplinary research in AI, data science, high-performance computing, and quantum computing through collaborative efforts across URI and beyond.
ITS Research Computing
ITS Research Computing is a relatively new Department with the goal of providing URI researchers access to cutting-edge computing equipment (HPC/AI/Data/Quantum), software and offering hands-on training, support and consultancy to enable computational research.
Rhode Island INBRE Molecular Informatics Core
The Rhode Island INBRE Molecular Informatics Core (MIC) at URI is an NIH-funded facility that delivers comprehensive sequencing, bioinformatics, molecular modeling, VR/3D visualization, and training services to support biomedical and environmental research across Rhode Island institutions
Translational Neurorobotics Laboratory
The Translational Neurorobotics Laboratory (TN Lab) at the University of Rhode Island specializes in developing AI‑enabled biosignal control systems and medical robotic technologies for neurorehabilitation and motor control restoration
AI Guides and Glossary
This document outlines essential best practices for researchers using generative AI tools, including mandatory disclosure of tool use, strict prohibition of AI authorship, verification of AI-generated content and citations, and caution against inputting sensitive data—while aligning with policies from funding agencies like NSF, NIH, and DOE.
This glossary provides clear, accessible definitions of key AI and machine-learning terminology, covering concepts such as algorithms, machine learning models, black‑box vs. explainable AI, hallucinations, neural networks, transformers, clustering, and beyond to support a solid conceptual foundation.