This is a report summarizing the activities and impact of the University of Rhode Island’s Artificial Intelligence Lab, specifically regarding our work during the Fall 2025 academic semester.
EXECUTIVE SUMMARY
At the Artificial Intelligence Lab, we strive to promote the opportunities and cautions regarding the use of artificial intelligence and technological sciences. With the inevitable and existing embrace of artificial intelligence services in professional industries, we prepare college students for their next steps with the adoption of these hard skills. Our commitment to projects that serve the benefit of other academic or professional entities allows us to be at the forefront of artificial intelligence in higher education.
AI Lab at the URI Quad Fest
PROJECTS
SUMMER 2025
| Fish Identification project Christine de Silva PI: Dr. Andy Davies | URI Risk Management project Lori Johnson Dan Keating | Exeter Pet Evacuation Dorian Boardman Exeter EMA Director | AI Usage in Education A&S Fellow, Summer 2025 |
| Zachary Johnson | Terran AlbergoMcGovern | Zachary Johnson | Callum Magarian |
| Kamron Aagor | Chun Wen | Brandon MacDonald | Brandon Cordon |
| Matthew Connors |
Data Viz project
| Kiryl Filipau | Ivan Ortiz | Paul Peralta Mordan | Jason Fopiano |
FALL 2025
| AI Tools in Student Learning (A&S Fellow program) | Game Design: Data Visualization for DS Roy Bergstrom | Workshops: AI Tools / Gen AI | Fish Classifier (GSO) Christine de Silva PI: Dr. Andy Davies |
| Callum Magarian | Matthew Barbrack | Matthew Connors | Zachary Johnson |
| Lauren Holly | Adam Meyisa | Ashraf Alashwal | Kamron Aggor |
| Nick Lockhart | Rafael Lacerda | Sarah Salvas | Nathan Murphy |
| Chatbot & Meeting muncher (Engineering) Prof. Resit Sendag | Communications Project (Comm) | Iceberg reconstruction (GSO & Ocean Engg) Dr. Russell Shomberg | Plastic-degrading proteins detection (CMB) Prof. Ying Zhang |
| Jack DeMarinis | Patrick Sullivan | Matthew Barbrack | Aidan McCrillis |
| Richard Buckley | Liam McKenzie | Omar Dovesi | |
| McKenzie Allen | Rafael Lacerda | Arybella Theul | |
| Jack Light |
Shadow Volunteer Hours
| Thomas T | Zihara Delgado | Lia Saldivar | D’juan Lawton |
| Jad Alsassa | Jeena Weber Langstaff |
SPRING 2026
| RI Small Business Development Center (Division of Research and Economic Development) | Fish Classifier (GSO) Christine de Silva PI: Dr. Andy Davies | GenAI Workshops (AI Lab) | BlueTIDE Data Visualization (401 TechBridge) |
| Zachary Johnson | Matthew Connors | Callum Magarian | |
| Callum Magarian | Kamron Aggor | Ashraf Alashwal | Sean Marois |
| Patrick Sullivan | Zihara Delgado | Lauren Holly | Courtney Poon |
| Jeena Weber Langstaff | Jad Alsassa | Jeena Weber Langstaff | Kam / Zach |
| Chatbot & Meeting Muncher (Engineering) Prof. Resit Sendag | Structural Biology Information Extraction(Pharmacy) Dr. Chris Hemme | Iceberg reconstruction (GSO and Ocean Engg) Dr. Russell Shomberg | Plastic-degrading proteins detection (CELS) Prof. Ying Zhang |
| Jack DeMarinis | Edwin Hernandez-Zanella | Matthew Barbrack | Aidan McCrillis |
| Richard Buckley | Liam McKenzie | Omar Dovesi | |
| McKenzie Alle | Rafael Lacerda | Arybella Theul | |
| Jack Light |
AI TOOLS IMPACT IN STUDENT LEARNING
This undergraduate research project involved distributing a survey to the University of Rhode Island student population. Students have the option of answering questions related to what AI tools they use in academic settings, how they use these AI tools, and open-ended questions about the impact that they see AI tools have in these learning environments. This survey data was turned into a dataset where extensive Exploratory Data Analysis and Natural Language Processing was conducted to find patterns and themes.
- Project Lead: Callum Magarian
- Student Researchers: Lauren Holly, Nick Lockhart, Brandon Cordon
Objective: Provide insights regarding how students are currently using AI tools in the context of their academic experience and possible interventional methods on improving AI tool adoption.
Project achievements:
- Obtaining 140+ survey responses for analysis
- Applying topic modeling for finding patterns using student text responses
- Utilizing industry standard data cleaning and analytical methods
AI TOOLS WORKSHOPS
Awaiting response from Matt C
- Student Researcher: Matthew Connors, Ashraf Ashawal, Sarah Salvas
DEEP SEA FISH CLASSIFIER
Project Partner: GSO, Christine de Silva, PI: Dr. Andy Davies
This project uses Machine Learning to analyze underwater video to automatically spot and label fish living 200–800 meters below the surface. An AI model trained on both public footage and our own hand-tagged clips recognizes each species and tracks their numbers over time. The goal is to give marine scientists a quick, reliable way to monitor deep-sea life in partnership with URI’s Bay Campus.
Screenshot of the model annotations
Students researchers: Zachary Johnson, Kamron Aggor, Zihara Delgado, Jad Albatal, Nathan Murphy
GAME DESIGN: DATA VISUALIZATION
Project Partner: Roy Bergstrom
This project is a 3D educational game built with Unity that helps students learn about data science concepts through an immersive VR experience. The project focuses on Exploratory Data analysis in an interactive environment. Players explore a virtual school environment where they interact with data and visualize key machine learning ideas. In this VR experience, players walk through different classrooms, each focused on a specific data science concept. In each room, they are presented with interactive datasets visualized in 3D. Players interactively filter the data, explore how the graphs change, and then answer short multiple-choice questions to test their understanding before moving on. Support Vector machine was one of the Machine learning models that was visualized in this immersive environment.
Students researchers: Essam Abdulraouf, Zaid Shahzad, Kiryl Filipau, Paul Peralta Mordan, Jason Fopiano, Matthew Barbrack, Adam Meyisa, Rafael Lacerda
DOCUMENT ANALYZER
Project Partner: City of Exeter
This AI-powered tool streamlines the upkeep of large documents by ingesting a Word file, preserving their formatting, and instantly comparing them against the approved master. It flags sections that have changed, are missing, or clash with new laws or internal policies. It generates plain language summaries, updates suggestions with citation, and returns a clean, revision-tracked file alongside a dashboard that logs who changed what and when. By condensing a lengthy manual review into minutes, it reduces errors, accelerates decision-making, and ensures critical documents remain current, consistent, and audit-ready.
Students researchers: Zachary Johnson, Brandon MacDonald
AUTOMATED DATA EXTRACTION
Project Partner: URI Risk Management
This project involved reading large text documents, mostly PDFs, and extracting important information from them. These documents were motley invoices for orders. Originally, this information was entered manually which could potentially introduce errors. In this project students developed an AI-enhanced tool that could read these documents and convert important information into a tabular form.
Students researchers: Terran AlbergoMcGovern, Chun Wen
MICROPLASTIC DETECTION in WATER
Project Partner: Engineering
This project supported an existing research project on microplastic detection in water. The engineering team set up the experiments and took pictures of the plastic particles in high resolution. The particles were glitter particles in various colors. Our team helped with the detection of these particles from these photographs. The images were sharpened and the particles were annotated for model training. The model was trained to detect these particles in various colors.
Students researchers: Kamron Aggor
Plastic-degrading proteins detection
Project Partner: CMB, Prof. Ying Zhang
The pollution of plastics has become a universal problem with an estimated 399,000 tons of plastic that has accumulated in the ocean, with 69,000 tons of these being microplastics. One of the most promising solutions to this problem is to identify plastic degrading genes (PDGs) in microbes to degrade plastics. In previous work, a computational pipeline was designed to take in a set of protein sequences and predict which proteins were likely to be able to degrade plastics.
Our existing pipeline identifies a high number of putative PDGs from diverse environment microbiome data. However, more advanced analytical tools are required to differentiate true PDGs from potential false negatives (i.e. genes that are homologous to known PDGs but have no plastic-degrading function). Our goal for the AI lab project is to create a machine learning algorithm that is able to differentiate true plastic-degrading proteins from other non-plastic degrading homologs. To do this, we want to integrate protein structural modeling with sequence alignments to identify key residues with essential roles in the plastic degrading pathway and use this information to create and train an AI model that is able to more accurately predict plastic degrading proteins.
The goal of this project is to integrate the latest developments in AI-based sequence data analysis, such as gLM2, to make more accurate protein function predictions by considering catalytic sites and genomic contexts of proteins. To use these tools, we want to leverage the Gaia method, which takes advantage of the protein embeddings that are provided by gLM2 and compare the embeddings of our unknown proteins and known plastic degrading proteins. We also hope to implement our knowledge of the mechanism and active site for the plastic degrading proteins and confirm that putative PDGs contain a functional active site.
Students researchers: Aidan McCrillis, Omar Dovesi, Arybella Theul
Chatbot and Meeting Muncher
Partner: Engineering, Prof. Resit Sendag
This project aims to advance the retrieval and question-answering capabilities of a chatbot designed to assist students with technical questions related to graduation requirements and coursework. Some specific enhancements include: integrating lightweight or low latency LLMs to speed up intermediary queries, enhancing the document ingestion process by experimenting with different embedding models, etc. These enhancements will allow us to provide faster, more accurate academic support to students.
Participating students working on this project will gain a wide set of skills, including knowledge of RAG systems, creating and orchestrating multi-agent systems, and applying techniques used within research to enhance the capabilities of LLM question answering. Students will also gain some experience in setting up, and deploying, an application to a production environment using Docker containers.
As this project matures, we aim to expand the application to other academic departments and domains, helping students access knowledge more efficiently. While the project has been primarily supported by the ECBE Generative AI Research group (College of Engineering), it has recently evolved into a collaborative effort with the AI Lab at the University of Rhode Island, which has joined to contribute additional resources and student researchers. Currently, the application’s scope is limited to the knowledge base of the URI Electrical, Computer and Biomedical Engineering graduate program, available at https://www.ele.uri.edu/chat.
Students researchers: Jack DeMarinis, Richard Buckley, McKenzie Allen, Jack Light
Arctic Iceberg Reconstruction
Partner: Ocean Engg and GSO, Dr. Russell Shomberg
Use of AI through Neural Radiance Field (NeRF) based rendering and reconstruction methods applied to Arctic sea ice and icebergs. NeRFs employ AI-driven neural networks to represent a 3D scene as a light field, enabling reproduction of complex optical effects such as translucency and reflections and allowing output as 3D meshes or point clouds. Our work evaluates AI-based NeRF performance against traditional methods while providing a dataset for benchmarking. The data was collected from Arctic sea ice north of Barrow and icebergs in Disko Bay on the Greenland west coast. Imagery was collected over multiple years and platforms. The Arctic presents unique challenges due to sea ice and iceberg optical properties and the dynamic motion of icebergs, which often degrade traditional photogrammetry results. While lidar and sonar have been used for reconstruction, NeRFs leverage low-cost optical cameras and, with advances in AI-based sensor fusion, offer opportunities to integrate multi-modal datasets. This capability is increasingly relevant as Arctic maritime activity grows, requiring improved situational awareness, mapping, and forecasting to support safe navigation, infrastructure protection, and climate research. Our results demonstrate the potential of AI-based NeRFs to overcome long-standing limitations in sea ice reconstruction while laying a foundation for future advances in Arctic data collection.
Students: Liam McKenzie, Matthew Barbrack, Rafael Lacerda
RI Small Business Development Center
Project Partner: Division of Research and Economic Development
The Rhode Island Small Business Development Center (RISBDC), part of the Division of Research and Economic Development provides no-cost, confidential business advising to aspiring entrepreneurs and established small businesses across the state. In 2024, the RISBDC served nearly 800 clients, provided 4,700 counseling hours, helped secure $10 million in capital, and supported the creation of 2,700 jobs.
Recently, Google.org announced a $10 million funding initiative for America’s SBDC—our national association. The goal is to develop an AI curriculum and AI clinics that will help 100,000 small businesses adopt and benefit from artificial intelligence across all 50 states, Puerto Rico, and U.S. territories. You can learn more here: https://www.americassbdc-ai-u.org/.
To help bring this initiative to life, two students from the AI lab will support AI-related, student-led projects supporting small businesses across Rhode Island.
Students: Jeena Weber Staff, Patrick Sullivan, Callum Magarian
Structural Biology Information Extraction
Partner: Pharmacy, Dr. Chris Hemme
Structural biology is the science of understanding the 3-dimensional structure of macromolecules like proteins and DNA. These structures in turn provide information about the chemical work the molecules can perform such as drug interactions. An individual structure file defines thousands to tens of thousands of individual atom
representing points in 3D space, along with chemical and biological information related to the molecule. With this information, the structural data can be visualized in 2D or 3D space using a variety of visualization formats. While a trained biologist can easily extract information from these figures, the variety of visualization options make it difficult for machine learning algorithms to extract useful data. We are currently working on machine learning methods to extract structural biology data from databases, scientific texts, and figure legends, but we would like to determine if it’s feasible to also extract information from the figures themselves. The goal of our laboratory is to be able to extract the maximum amount of useful information from a scientific manuscript about the structural characteristics of the reported molecule, process the information, and visualize it in 2D, 3D, or virtual reality. This project represents one component of that workflow focusing on the images themselves.
Student: Edwin Hernandez-Zanella
WORKSHOPS
AI TOOLS
This series of general AI Tool overviews provide a comprehensive introduction to Artificial Intelligence for students of all disciplines. This course introduces students to various AI tools that can enhance their academic performance and experience. Through interactive sessions, students will learn to apply AI tools for academic and personal success.
Number of sessions: 7
Number of participants: 5-6 students per session
Target audience: Undergraduate students, Graduate students
GENERATIVE AI
This program offers an introduction to Generative AI, particularly focusing on image generation techniques. Participants will explore fundamental generative concepts, tools, and ethical considerations while engaging in interactive activities to create AI-generated images.
Number of sessions: 3
Number of participants: 1-2 per session
Target audience: Undergraduate students, Graduate students
MPI IN RUST
This series of seminars demonstrates the use cases of Rust programming language. Rust has garnered industry recognition for its high performance and accuracy of which students will learn to leverage this platform. Activities during this workshop consist of parallelizing computation across multiple cores using fundamental Rust libraries.
Number of sessions: 1
Number of participants:
Target audience: Undergraduate students, Graduate students, Doctoral students
R SERIES
This series is intended to introduce participants to the immense and powerful applications of the R programming language. It covers essential data science concepts and techniques which emphasize hands-on activities with popular R libraries. Attendees will gain practical insights into data manipulation, exploratory data analysis which allows them to extract meaningful insights from datasets.
Number of sessions: 10
Number of participants:
Target audience: Undergraduate students, Graduate students
SPSS / SAS
These presentations explain first how to begin using the Statistical Package for the Social Sciences statistical software, overviewing syntax and point and click features. Following that, there is another presentation for more experienced students to expand and apply their knowledge using SPSS. This includes advanced analysis, creating tables and figures, and creating subset data files. Additionally, a similar beginner course will be offered for Statistical Analysis System.
Number of sessions: 2
Number of participants:
Target audience: Undergraduate students, Graduate students
EVENTS
- Quadfest – Sept 30, 2025
- Internship Night organized by Women in Data Science
- Discovering AI@URI – Dec 10, 2025
- AI Club Visit – Nov 12, 205
- Hack@URI AI track
CLASS VISITS
- URI 101: Instructor Melissa Gooding
- URI 101: Instructor Lisa Kuosmanen
IMPACT STATISTICS by DEPARTMENTS SERVED
| Major | Students served | Course |
| Computer Science | 23 | CSC 499, CSC 477, ITR 302 |
| Data Science | 8 | DSP 477, ITR 302 |
| Engineering | 3 | |
| Communication | 1 | ITR 302 |
| Math | 2 | CSC 499 |
| Physics | 1 | |
| Cell Molecular Biology | 1 |
SERVED by SEMESTER
| Semester | Students served |
| Summer | 12 |
| Fall | 29 |
| Spring(current enrollment) | 23(expected 30) |