Projets novateurs réalisés

Developing Data Strategies to Enable Healthcare Machine Learning

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 at Grand River Hospital in the K-W region. To bring the AI revolution to healthcare systems, it is necessary to accomplish the critical task of understanding state of data collection and establish solid data foundations. The primary goal in the project will be to address this foundational need, which can impact the success of efforts beyond this project. The project will develop and build innovative solutions to strategically foster and stimulate the future advances at the hospital and the University of Waterloo to build healthcare systems for the digital age.

Self-supervised Learning Applied to Geological Data

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Canadian Parks and Wilderness Conservation Education and Outreach

??The Canadian Parks and Wilderness Society – New Brunswick Chapter is a non-profit organization dedicated to the permanent protection of land, rivers, and ocean areas in New Brunswick. We work to ensure that parks are managed to protect the nature within them, and we promote awareness of our connection to nature and the inherent values of wilderness through our education and outreach programs. ?We work collaboratively with community groups, governments, industry, Indigenous Nations, and individuals across the province, to encourage conservation action. ?Through this project, the intern will work on various outreach and education projects, such as facilitating in-parks, nature education events and activities, and drafting communication materials for our terrestrial and marine conservation campaigns. This work is critical in expanding our reach to more communities in New Brunswick, and increasing environmental awareness, nature stewardship, and support for conservation. ?

Targeting bacteriophage delivery of reprogramming transcription factor gene(s) to astrocytes in CNS by Intelligent Phagemid-Assembled Gene Expression (iPhAGE) technology

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 protection. The participating institutions will benefit from the advancement of biotechnological techniques and potential therapeutic applications for neurological disorders.

User Profile generation for Mobile Ad Targeting

Personalized ad targeting is one of the most important features to ensure a successful advertising campaign—e.g., F-150 ford pickup trucks are best shown to construction workers than teenage girls. Another important aspect of ad personalization is frequency capping the ads. For example, showing the same ad 100 times to 1 user will result in not having any budget left over for others, and this can make or break an advertising campaign. To generalize the targeting strategy across users that have not been observed in the past, either due to lack of data or due to new users, we aim to develop clustering methods based on similarity measure that receives user features as input and then assign the new user to a group of users.

Data Analytics for Improving Door-to-Needle Time in Acute Stroke at Grand River Hospital

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 exceeds the 30-minute target median set by the Canadian Stroke Best Practice Guidelines 2022. The goal of this project is to use data analytics tools to analyze past data to reduce DTN time so that most stroke patients presenting at its Emergency Department receive treatment within a median of 30 minutes.

Multimodal RAG Explainability

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 to empower the retrieval capabilities and reduce the hallucinations of the model. Counterfactual reasoning will be utilized to formulate explanations by extracting the input data that directly impacts the answer it generates. This approach will illuminate the decision-making process of multimodal LLMs, enhancing their transparency and interpretability.

The project holds the potential to significantly impact fields such as healthcare, law, and finance, where understanding the rationale behind AI outputs is crucial for trust and responsible deployment. An interactive tool enables users to understand the reasoning behind the model outputs and identify potential biases or errors.

A programmable microalgae cultivation platform for sustainable food production and waste resource recovery

The manipulation of gene activity in microalgae (cyanobacteria) offers the possibility of producing energy and materials directly from sunlight, water, and carbon dioxide, contributing directly to more holistic modes of food production, innovative bioproducts and reliable bioenergy solutions that reduce human carbon emissions. Despite their recognized potential, it has proven challenging to develop scalable genetic systems for industrial cyanobacterial strains. We are addressing this challenge, in collaboration with Purify, a local biotechnology company, using Arthrospira platensis (Spirulina). We will optimize Spirulina growth under a range of metal concentrations using a recently developed high-throughput lighting system at the University of British Columbia. Use of this lighting system will enable us to build a series of models describing optimal growth under a combination of different metal and media conditions, and isolate specific components that are essential for increased biomass yield or metal tolerance and recovery. We will use this combined platform to increase Spirulina carbon capture associated with nutraceutical or food production including overproduction of phycocyanin and selected cofactors with antioxidant or health promoting properties as well as potential applications in water treatment. Throughout this project we will work closely with Purify providing a path to market for the research while training highly qualified personnel primed for success in the emerging Canadian bioeconomy.

Exploring Age-Related Changes in Multisensory Integration using Machine Learning tools

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, and Transcranial Magnetic Stimulation. This analysis aims to uncover patterns, correlations, and insights.

Optimization of reconfigurable intelligent surfaces placement for non-reciprocal transmission

Reconfigurable intelligent surfaces (RISs) have emerged as a transformative technology in the sixth-generation (6G) wireless communications. The RIS technology can easily be realized in local area networks and disaster management scenarios where ad-hoc local area networks need to be setup to ensure mission-critical connectivity. The objective of this project is to develop the techniques to optimize the number and placement of RF network devices, such as base stations (access points) and transmissive and reflective RISs, in an indoor environment to achieve optimum wireless network performance. This objective can be achieved by assessing the effectiveness of practically feasible RIS-based systems, considering their real-world constraints, and working to bridge the gap between theoretical expectations and actual outcomes. This involves employing creative strategies and methods to simplify complexity, thereby enabling the deployment of more extensive systems. Furthermore, the implementation of an optimized network presents a chance to lower the total power usage of networks, which directly influences the reduction of greenhouse gas emissions.

Evaluating LLMs for Sentence Encoding and Clustering to support Thematic Analysis in Qualitative Research

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 topic diversity, the toolkit bridges the gap between technical expertise and qualitative research needs. This approach broadens the impact of computational tools across diverse domains like health, education, and governance.
The expected outcome of this project is a report detailing the findings from the benchmarking analysis and user feedback. This report will provide insights into the performance of Large Language Models for thematic analysis tasks and outline recommendations for researchers and practitioners seeking to implement computational tools for qualitative data analysis, contributing to the advancement of knowledge in the field of computational social science.

Improved breeding of spruce in Atlantic Canada: Genetic Strategies for Sustainable Forestry

This research project focuses on enhancing the genetic characteristics of Spruce species in Atlantic Canada. By analyzing data from various sources and reviewing family lineage, we aim to understand better the factors influencing Spruce growth traits. Through this comprehensive approach, we will propose improvements to breeding and deployment strategies, making them more effective in adapting to changing environmental conditions. Our ultimate goal is to develop a specialized Tree Breeding Management System tailored to the needs of stakeholders in Atlantic Canada. This system will empower decision-makers with valuable insights, promoting sustainable practices for managing Spruce forests in the region. Ultimately, our efforts seek to strengthen the genetic diversity of Spruce populations, ensuring their resilience and ecological importance in Atlantic Canada’s forest ecosystems.