URI is part of the NSF-funded Open Storage Network (OSN)—a collaboration among 17 top research institutions. URI’s “Pod,” hosted at an OSHEAN data center, provides over a petabyte of high-speed, high-capacity storage for secure research data sharing across the region and beyond.
This long-term partnership enhances URI’s research infrastructure, enabling greater collaboration and more efficient data workflows.
Read more about OSNThe Institute for AI & Computational Research (IACR)—formerly known as CCR—and ITS Research Computing have expanded URI’s high-performance computing capacity with a major update to the Unity Cluster.
This upgrade includes:
With this expansion, Unity now supports approximately 520 nodes, totaling 28,000 CPU cores and 1,400 GPUs.
The newly added Grace and Grace-Hopper nodes feature energy-efficient ARM-based processors, designed to deliver strong performance while improving power efficiency—ideal for complex scientific workloads, AI research, and data modeling.
Need help getting started or want more information? Contact the team at iacr-group@uri.edu or connect via the Unity Community Slack.
ITS Research Computing continues to play a vital role in supporting Rhode Island’s coastal communities through powerful, research-driven tools. Recent state funding will ensure the continued operation of three critical programs at URI:
These programs are essential for improving coastal resilience and emergency preparedness. CHAMP, in particular, relies heavily on ITS Research Computing’s infrastructure and services to deliver accurate, high-performance modeling and data processing.
This funding reflects URI’s leadership in applying computational research to real-world challenges—and highlights the value of strong infrastructure support for impactful science.
The Center for Computational Research (CCR) has officially transitioned into the Institute for AI & Computational Research (IACR)—uniting CCR, ITS Research Computing, and the URI AI Lab into one collaborative entity.
This change reflects URI’s growing investment in artificial intelligence, data science, and advanced computing. It also recognizes the Institute’s expanding role across the University and its partnerships with external research institutions and industry.
The IACR aims to support faculty and researchers in every discipline. Whether you’re modeling climate systems, analyzing language data, or building AI-driven tools, the Institute provides:
We are happy to share that the AI Lab’s Dr. Abdeltawab Hendawi has won the best demo paper award for his paper titled, “Harnessing Crowdsourced Mobile Data And LLM for Dynamic and Accessible Pedestrian Routing,” at the 26th IEEE International Conference on Mobile Data Management (MDM).
The IEEE MDM is a prestigious forum for the exchange of innovative and significant research results in mobile data management.
Congratulations, Abdeltawab!
Below are the details of our CCR / AI Lab summer workshops. Again, these are open to faculty, staff and students and are totally free. Please feel free forward this announcement to anyone who may benefit.
As usual, you can find these on the URI events calendar (and subscribe): https://events.uri.edu/group/ai
Or here is a webpage link:
https://docs.unity.uri.edu/news/2025/05/uri-summer-25-workshops/
We have some relatively new workshop offerings on AI Tools, Gen AI and Bioinformatics. A huge thanks to Indrani Mandal and our student team for planning and preparing these workshops!
Dr. Liqun Zhang, a computational scientist in URI’s Department of Chemical Engineering and affiliate of the Center for Computational Research (CCR), recently published a paper in Nature Chemistry titled: “Interaction and dynamics of chemokine receptor CXCR4 binding with CXCL12 and hBD-3”.
The paper is open access and available here: Read the full publication
Dr. Zhang’s research explores molecular interactions that have critical implications for immune system response and therapeutic development. Her work relies heavily on URI’s high-performance computing infrastructure—specifically, the Unity cluster at MGHPCC and on-campus URI HPC environments—supported by ITS Research Computing.
This achievement highlights how URI’s computational resources empower groundbreaking scientific research with global reach.
URI has been part of the National Science Foundation’s Open Storage Network (OSN) for 3 years. OSN is a collaborative between 17 top research institutions that is designed to storage vast amounts of valuable research data and allow for sharing over a high-speed network. Each institution has a storage “Pod” with over a petabyte of capacity. URI’s Pod is hosted in one of OSHEAN’s data centers that allows for extremely high-speed connectivity across the region and nationally.
More details available here: https://access-ci.org/open-storage-network-welcomes-new-campus-computing-partners/
Prof. Corey Lang and postdoctoral researcher Jarron VanCeylon just published a paper in the Journal of Environmental Economics and Management titled “Voting with their (left and right) feet: Are homebuyers’ values of neighborhood environmental amenities consistent with their politics?” that was supported by the CCR.
Full paper can be found here: https://doi.org/10.1016/j.jeem.2025.103157
Congratulations Corey and Jarron!
The Center for Computational Research is planning a series of monthly talks on AI/ML techniques and applications across various scientific domains. We envision the talks to strike a good balance between depth and breath. The goal of these talks will be to (i) introduce the particular AI/ML technique to fellow faculty and graduate students who have a basic understanding of deep learning and (ii) present a variety of applications in different domains without assuming deep domain knowledge. Each event will start with a 45 minute talk with 15 minutes for questions and a subsequent 30 minute networking session for brainstorming and further discussion. The speakers will be URI faculty from a number of colleges.
Details on the first talk appear below.
Speaker: Peter Cornillon (GSO)
Date/Time/Location: May 1st, 3pm, 112 East Hall.
Title: Machine Learning Approaches for Characterizing Global Sea Surface Temperature Fields
Abstract: Sea surface temperature (SST) fields derived from satellite-borne sensors offer an ideal dataset for exploration using machine learning techniques. In this presentation, I will describe the use of an auto-encoder, in combination with a flow equalization technique, to identify outliers in a 20-year, global, twice-daily archive of SST fields. I will then demonstrate how this same approach can be applied to evaluate the performance of a global ocean circulation model.
Switching gears, I will introduce a machine learning model based on contrastive learning applied to the same SST datasets—this time to uncover and categorize fundamental spatial patterns within the fields. Finally, I will briefly touch on an analysis of the latent space produced by the contrastive learning model, with a focus on estimating the intrinsic dimensionality of SST field variability.
