Deep Sea Fish Detection (Fish ID)
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.

Student researchers: Zachary Johnson, Kamron Aggor, Matthew Connors

Screenshot of the model annotations

AI in Education
A research project that explores the impact of Artificial Intelligence Tools in student learning environments. A self-made survey, approved from the IRB, was administered to college students to reflect their observations of impacts AI Tools had in classroom settings. The combination of closed and open ended responses have various computational methods applied to them in order to find patterns in the survey data. The implications of the research are significant and can broadly improve current academic experiences. Our hope is to learn how to promote these new AI Tools in an equitable and beneficial manner.

Student researchers: Callum Magarian, Brandon Cordon

Document Analyzer
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
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 Albergo McGovern, Chun Wen

Data Visualization
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.

Student researchers: Essam Abdulraouf, Zaid Shahzad, Kiryl Filipau, Paul Peralta Mordan, Jason Fopiano

Microplastic detection in water

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. 

Student researchers: Kamron Aggor