Innovative Projects Realized

Artificial Empathy in Fundraising

Many nonprofit organizations struggle to create compelling stories that inspire people to donate. This project aims to develop an AI-powered assistant that helps nonprofits improve their fundraising campaigns by using more empathetic and emotionally engaging language. Since people are more likely to donate when they feel a personal connection to a cause, this tool will suggest ways to make campaign messages more impactful.

The AI assistant will analyze and enhance fundraising messages, making them more effective in engaging potential donors. By testing different approaches across causes like environmental conservation, health, education, and community development, the tool will learn which types of messaging work best. Nonprofits will be able to use this AI-powered support to create more persuasive campaigns, build stronger connections with donors, and ultimately increase funding for their important work.

Vancouver Cycling Without Age Program Coordinator

Vancouver Cycling Without Age Society is a registered charity and volunteer-powered organisation based in Vancouver that provides recreational trishaw (3-wheeled bikes with a 2-person bench on the front) rides to seniors, and their families. Our goal is to break seniors free from social isolation and loneliness and help them to smile and reconnect with their loved ones and their community with the simple act of a bike ride. The Program Coordinator wants to bring joy to seniors who are socially isolated and marginalized. They are passionate about getting seniors outside into fresh air and feeling the wind in their hair. The Program Coordinator is responsible for coordinating the day to day operations and supporting the volunteers who are giving the rides. They are responsible for the delivery and quality of our care home and community ride programs.

Détection des rampes de puissance éolienne à l’aide de l’apprentissage machine

Le projet porte sur le développement et l’adaptation d’un modèle d’apprentissage profond existant afin de prévoir de façon automatique à partir de cartes de prévisions météorologiques à haute résolution la probabilité d’occurrence de rampes de puissance éolienne. Ces événements soudains de changement rapide de la production éolienne, difficiles à prévoir, peuvent avoir des impacts significatifs sur la stabilité du réseau électrique et poser des défis au niveau de sa gestion. Plusieurs études ont montré que l’occurrence de rampes est reliée à l’occurrence de phénomènes météorologiques précis. L’apprentissage profond sera ici utilisé pour analyser de façon automatique les patrons associés à ces phénomènes sur des cartes de prévisions et évaluer si les conditions sont propices ou non à l’occurrence de rampes, imitant ainsi le travail d’un météorologue expérimenté. Les avantages de ce projet pour l’organisation partenaire Hydro-Québec (HQ) seront d’augmenter la valeur économique de l’éolien et de faciliter son intégration dans le réseau, l’éolien étant appelé dans les années à venir à composer une part plus importante du mix énergétique au Québec avec des plans de développement ambitieux dont HQ sera le maître d’œuvre, et à remplacer une grande partie de l’électricité produite à partir de combustibles fossiles dans les réseaux

Optimization of photodynamic inactivation (PDI) compounds for control of plant pathogens in vitro and in vivo

This project aims to develop innovative and effective photodynamic inactivation (PDI) formulations as environmentally friendly biopesticides for managing plant diseases in vegetables, fruits, and ornamental crops grown in field and greenhouse settings. By addressing key challenges such as phytotoxicity and optimizing efficacy, the project is expected to create new agrochemical solutions that expand Nutrien’s product portfolio. These sustainable biopesticides, which can function independently or as part of integrated pest management (IPM) programs, align with growing demands for eco-friendly pest control methods. The anticipated outcomes include improved agricultural yields, enhanced food security in the face of climate change, and economic growth through job creation and increased competitiveness for Canada’s agricultural sector.

AI-Driven Ingredient Recognition & Measurement for Meal Planning

MealLens Inc. is developing an AI-driven meal-planning solution designed to help users efficiently manage food ingredients and optimize meal preparation. This project focuses on enhancing the system’s ability to recognize and process ingredient information while improving overall accuracy, user experience, and scalability. The expected outcome is a more intelligent and adaptable platform that will contribute to reducing food waste, streamlining kitchen management, and supporting healthier eating habits. This innovation will strengthen MealLens’ position in the food-tech industry and enhance its commercial potential.

Extrusion cooking of yellow pea starch- and fibre-based puffed snacks: Linking variability in raw materials to extrusion performance

This research project aims to develop nutritious, sustainable puffed snacks using yellow pea starch and fiber—valuable by-products of pea protein production. As Canada strengthens its plant protein industry, finding innovative ways to utilize these underused ingredients is essential for economic and environmental sustainability.
Using advanced extrusion processing, this study will examine how different raw material properties influence the texture, nutrition, and consumer appeal of these snacks. By optimizing processing conditions such as moisture content, temperature, and extrusion screw speed, we aim to enhance product quality and expand the market potential for pulse-based ingredients.
Our industry partner, Pulse Canada, is dedicated to promoting the use of Canadian pulses. A key challenge in incorporating pea starch and fiber into extruded snacks is their unpredictable behavior, which can lead to issues with texture, expansion, and consistency. This project will systematically address these concerns, providing data-driven solutions to improve production efficiency.
The expected outcomes include healthier, high-fiber, low-glycemic snack options, reduced food waste, and new economic opportunities for Canada’s pulse sector. By bridging food science innovation with industry needs, this research supports sustainable food development and strengthens Canada’s leadership in plant-based nutrition.

Optimized Design of a Highly Efficient Safety Barrier for Harsh Environments

The research project focuses on improving and optimizing planar transformers used in safety devices for hazardous areas. The intern will work with HB Safety Automation Group Inc., which develops explosion-proof safety barriers. The project aims to enhance transformer efficiency and safety by optimizing core materials like Manganese-Zinc and Nickel-Zinc ferrites. This will help solve issues like low efficiency and high heat loss. The expected benefits include safer products, reduced accident risks, and better market competitiveness for the company.

Stagiaire marketing : plateformes numériques

Le projet vise à optimiser la boutique en ligne d’Alcyon sel de mer afin d’améliorer l’expérience client et d’augmenter la conversion des visiteurs en acheteurs. Le stagiaire analysera le site web et proposera des améliorations en matière de navigation, de contenu et de référencement naturel (SEO) pour accroître la visibilité et l’accessibilité des produits. Il/elle travaillera également sur l’optimisation des campagnes numériques en utilisant Google Analytics, Shopify et des stratégies de marketing numérique pour mieux comprendre le comportement des consommateurs et ajuster les actions en conséquence. Ce projet permettra à Alcyon d’améliorer sa présence en ligne et de renforcer son positionnement dans le commerce électronique, tout en offrant au stagiaire une expérience concrète et appliquée dans un environnement entrepreneurial en pleine croissance.

Furthering the development of AI based Historical Homicide files analysis with the help of Large Language Models

In this project, we are working on a large repository of historical homicide case files, which is majorly unstructured text. We are building a framework for knowledge graph extraction with the help of existing information extraction models further augmented and made robust with the help of existing state-of-the-art publicly available LLMs’ zero/few shot capabilities. We believe, our system will achieve more accuracy and coverage in terms of entity and their relationship detection embedded in those unstructured files and work as a foundational data structure to help build efficient Query and analysis tools for the domain experts, such as detectives to solve their criminal investigation roadblocks. In future, we will use the generated ideas from this project to extract structure from other types of unstructured or semi-structured documents to aid for advanced analysis of investigative files.

A Study of the Ecological Features that Contribute to Misconduct by Registered Early Childhood Educators

This project is a joint collaboration between researchers at Carleton University and the College of Early Childhood Educators (the College). The College oversees the regulation and professional conduct of over 60,000 Registered Early Childhood Educators (RECEs) in Ontario. A major challenge that the College faces is supporting the high quality and professional practice of RECEs, which may be undermined by poor working conditions, among other factors. This project seeks to answer the primary question: How do the socioecological features of early learning work settings contribute to the professional misconduct actions of RECEs? It will examine different factors such as physical space, staffing, and informal and formal workplace policies to determine their associations with different types of misconduct. This project will identify the socioecological features of high-risk workplace environments and develop strategies to improve professional conduct and regulatory enforcement. It is then expected that the College and sector partners will have a more accurate understanding of the socioecological factors that contribute to RECEs’ professional misconduct, potentially reducing these types of cases in the future.

Exploring the Determinants of Women Leader Wellbeing in a Canadian Organization: A Mixed Method Investigation into the Factors Influencing Female Leader Happiness and Wellbeing.

This mixed-method study focuses on bringing new learning to the field of women in leadership, particularly their level of happiness and well-being. Using female senior leaders at a Canadian bank, a quantitative survey will be administered to 225 leaders. The survey utilizes a combination of an adapted Cantril’s Ladder (Gallup), the Eudaimonic Workplace Well-Being Scale, and the BBC Well-being measures. The determinants of happiness/well-being among these leaders will emerge using multiple linear regression, stepwise regression, and structural equation modeling (SEM). The qualitative explorations with the happiest leaders (n=10) will provide additional colour and richness to this learning. The research contributes to the body of research on the well-being of female leaders by providing a more profound and precise understanding of their determinants of well-being. The discussion will likely be focused on a call for organizations to pay much more attention to all elements of a leader’s happiness/well-being inside and outside the work environment, given the benefits this can provide to organizational engagement and financial performance.

Multimodal Evaluation of Microcracks in Concrete Samples Using Contact/Non-Contact Ultrasonics, Infrared Imaging, Computed Tomography Scans, and Electrical Resistivity

This research project seeks to develop the Ultrasonic-Electrical Concrete Assessment Method, an innovative approach for early detection of micro-cracks in concrete structures within nuclear power plants. These micro-cracks can compromise the structural integrity of concrete, increasing vulnerability to damage and potentially affecting safety and performance. By integrating advanced technologies—including ultrasonic waves, electrical measurements, thermal imaging, and 3D scanning—this method aims to deliver a comprehensive and accurate crack detection system. Controlled concrete samples with induced cracks will be utilized to evaluate the effectiveness of each technique. Additionally, artificial intelligence will be employed to synthesize data from these methods, enabling predictive insights into crack formation and progression. This research will empower power plant operators with tools to identify and address concrete issues proactively, enhancing maintenance strategies and ensuring safer operations.