Feeling belonging is important for all people, including children. Many organizations want to help children feel they belonging in their communities and the programs they attend (such as recreation or education programs). But to do this, organizations need a way to measure belonging and see how it changes over time. This project addresses this need […]

Transforming Architecture: Pluripotent and Quasigeostrophic Space is an interdisciplinary research program investigating new materials, tools, methods and frameworks in the field of living responsive architecture, an emerging sub-discipline of architecture that focuses on how buildings respond to their occupants. Part of that work involves creating and disseminating kits and publications to help individuals access and […]

This project “Multimodal RAG Explainability (MRAGE)” aims to develop an interactive tool for explaining Large Multimodal Models (LMMs) augmented with retrieval capabilities. LMMs represent a significant advancement in AI, capable of understanding and generating content across multiple modalities like text and images. By employing the retrieval-augmented generation (RAG) methodology, this project queries external knowledge sources […]

It is shown that the rapid administration of treatment in acute stroke patients improves clinical outcomes. In particular, the time it takes to administer treatment once the patient has arrived at the Emergency Department with stroke symptoms is called Door to Needle Time (DTN). The median DTN time at the Grand River Hospital (GRH) currently […]

This project aims to develop a method for delivering reprogramming transcription factor genes to astrocytes in the central nervous system using Intelligent Phagemid-Assembled Gene Expression (iPhAGE) technology. By constructing specific gene blocks and fusion helper plasmids, synthesizing iPhAGE DNA, and purifying iPhAGE preparations, the project seeks to enhance transfection efficiency and assess immune responses and […]

The project “Developing Data Strategies to Enable Healthcare Machine Learning” aims to develop effective strategies to collect, curate, and maintain data in healthcare. A data strategy which enables Artificial Intelligence (AI)/Machine Learning (ML) models plays a pivotal role in building response healthcare solutions. The project is focused on understanding the fairness aspects of care quality […]

This project aims to develop a system that improves how robots with restricted operating ranges plan their indoor paths for cleaning or surveying. By dividing indoor spaces into manageable areas, the system will enable robots to complete their jobs more efficiently, optimally covering all necessary ground. This project involves both algorithm development and its implementation […]

Cyanobacterial and microalgal host cells produce a range of bioactive compounds that makes them good candidates for drug discovery. At the same time, they themselves get infected by a range of different viruses. This means that understanding the interplay of these hosts with the viruses that infect them is crucial in order to optimize bioactive […]

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The project “Design and Implementation of a Type System for Enhanced Mathematical Operations in Rings” aims to develop a Java library with a straightforward application programming interface (API) that simplifies complex mathematical computations within rings, including solving matrix equations. While this library is designed as a part for the bigger research project called “Symmetric system […]

The project “Evaluating LLMs for Sentence Encoding and Clustering to support Thematic Analysis in Qualitative Research” aims to provide qualitative researchers with advanced machine learning techniques for social media data analysis while maintaining autonomy and ownership of their data analysis. By benchmarking modern Language and Large Language Models against established metrics such as coherence and […]

The project “Exploring Age-Related Changes in Multisensory Integration using Machine Learning tools” aims to investigate how changes in neurotransmitter concentrations influence the way young and older adults integrate multisensory information and perceive time. Its main method of investigation involves applying machine learning tools to analyze the extensive dataset collected from behavioral tasks, Magnetic Resonance Spectroscopy, […]