Innovative Projects Realized

Graph Neural Networks for Wind Power Modelling

This project aims to develop new machine learning models that can help wind energy companies design wind farms more efficiently and at lower cost. Today, planning a wind farm requires running large, high-resolution computer simulations to understand how wind flows around turbines and how much power a proposed layout can produce. These simulations are accurate but extremely slow and financially expensive, especially when companies need to test a large number of different layouts or new locations to optimize the power production and transmission. Our research will create advanced prediction models that learn directly from existing wind flow simulations and real atmospheric data. By using graph-based neural networks and advanced generative methods, our models can adapt to different turbine layouts and geographic domains, while also estimating uncertainty in the predicted wind and power output. This makes them more flexible and scalable than current tools. For Veer Renewables, the partner organization, this project will produce a new commercial product that can deliver fast, early-stage assessments for new wind farm layouts and new geographic domains, reducing reliance on costly physical simulations.

L2M – RepGen: Towards Automated Reproduction of Deep Learning Bugs Leveraging an Intelligent Agent

This project aims to explore whether RepGen, an academic tool that helps software teams automatically reproduce bugs in deep learning and AI systems, can become a useful product for industry. By interviewing more than 100 developers, companies, and technical teams, the project will identify who needs this tool the most, what features they care about, and how RepGen can fit into real software development workflows. The results will guide the creation of a business model, a product roadmap, and a list of early adopters. This work will benefit the partner organization, Lab2Market, by providing clear, evidence-based insight into the commercial potential of RepGen and supporting their goal of turning university research into practical, market-ready innovations.

L2M – Development of a modular AI voice architecture for automated dental front desk operations

Orbis Assist is developing “Ava,” an intelligent voice assistant designed to function as a fully automated receptionist for dental clinics. This internship project focuses on building the secure software that allows the AI to manage phone calls, schedule appointments, and answer patient questions by directly connecting with the clinic’s internal calendar systems. By automating these critical front-desk tasks, this technology ensures that dental practices can operate 24/7 without being limited by the current shortage of administrative staff, ultimately helping clinics reduce wait times and ensuring more Canadians have timely access to oral healthcare.

Co-adaptation d’une formation sur la gestion du stress pour les producteurs

Le projet vise à co-adapter une formation sur la gestion du stress pour les producteurs agricoles. Ce projet correspond à la première étape d’un plus grand programme, où l’objectif sera d’évaluer l’intérêt et d’identifier les attentes des producteurs laitiers concernant la formation. La perspective sera de concevoir la formation à partir des réponses des producteurs laitiers.

Les versions antérieures de cette formation, qui ne sont pas adaptées pour les producteurs, ont permis aux participants d’augmenter leur proactivité dans la gestion de leur stress. Cela a contribué à diminuer significativement leur symptômes de stress et d’anxiété.

Jimmy Zee’s Distributors – Enhance Sales Efficiency through CRM Optimization and Digital Lead Intelligence

Jimmy Zee’s Distributors Inc. is a Vancouver-based distributor specializing in confectionery and general merchandise that celebrate Canadian culture and identity. The company supplies a broad network of retail partners—from convenience and grocery stores to hardware and tourist outlets offering products that bring joy and pride to consumers across the country. As Jimmy Zee’s continues to grow, the organization faces the challenge of scaling its customer acquisition strategy in a way that is both efficient and data-driven. Currently, lead generation and client outreach are handled through traditional, manual processes that limit the ability to strategically identify high-value prospects, assess conversion potential, and measure the performance of sales initiatives. This project introduces an innovative approach to modernizing these operations by developing and implementing a structured lead generation system that integrates customer relationship management (CRM) tools, data validation methodologies, and performance metrics to guide decision-making. The innovation lies in transforming how Jimmy Zee’s collects, analyzes, and leverages customer data to generate leads more effectively moving from intuition-based outreach to evidence-based strategy. This initiative goes beyond day-to-day sales activities by creating a replicable framework for digitalized lead generation, enabling long-term scalability and market expansion. Solving this challenge requires expertise in marketing analytics, sales process design, and CRM system optimization, along with creative problem-solving and communication skills to translate data insights into actionable business outcomes.

L2M – BIM Integrated Workflow for Defect Detection and Feedback in 3D Concrete Printing

This project will deliver a software plugin and workflow that seamlessly connects a Building Information Modeling (BIM) tool with 3D concrete printing (3DCP) systems. During the design phase, it will enable construction teams to generate, review, and refine concrete printing paths directly within the BIM environment, removing the need for separate tools. During printing, a depth-sensing camera combined with AI-driven machine vision will continuously monitor the build in real time to detect any defects, misalignments, or quality issues as they happen, allowing teams to address problems immediately. After the print is complete, the actual built geometry will be automatically fed back into the BIM model, creating an accurate digital twin of the finished structure.

This precise as-built digital record makes it easy to verify that the construction matches the intended design. This significantly reduces costly rework, material waste, and delays, while improving overall print quality through better documentation and verification. The workflow is currently at the research prototype stage. With support from the Lab2Market program, the focus will be on validating whether this idea solves real problems within the construction and 3DCP ecosystem. This will involve interviewing contractors, engineers, and 3DCP providers to understand their pain points around planning, quality assurance, and coordination, and to test if a BIM-integrated monitoring and quality control tool would be useful in practice. Based on this input, the project will identify likely early adopters, outline what a minimum viable product should include, and define a clear value proposition and simple business model. For the partner organization, this work will provide a grounded view of where the solution fits in the market, how it can create value for different stakeholders, and what concrete steps are needed to move toward future pilots and, eventually, commercialization.

Developing a validated questionnaire-based clinical scoring system for Feline Cognitive Dysfunction Syndrome

This project aims to develop a new clinical scoring system to help veterinarians and cat owners identify early signs of age-related cognitive decline in domestic cats. The study combines a questionnaire-based measure of behaviour and cognitive function with a full veterinary health check and experimental cognitive assessment tasks to accurately identify subtle changes in behaviour, memory, and problem-solving ability. By creating a reliable and easy-to-use tool, the project will support earlier diagnosis of cognitive aging and more effective treatment planning to improve quality of life for senior cats. The collaboration between the University of British Columbia and the University of Edinburgh will benefit both institutions by sharing clinical, theoretical, and experimental research expertise, providing valuable training opportunities for students, and strengthening international partnerships in animal welfare and veterinary behavioural medicine.

Religious Women of Quattrocento Florence: Staying Out of Sight but Not Out of Mind

Circa 1480 the Abbess of the convent of Sant’ Ambrogio in Florence commissioned the artist Cosimo Rosselli, to paint the fresco Procession of the Holy Miracle in her convent church. In the painting the nuns of Sant’ Ambrogio gather on the threshold of the church before a crowd which includes portraits of well-connected Florentine men. Visible to the public the women break the church law mandating they live apart from secular society. Under my Globalink project I will investigate whether the fresco depicts the real-world secular network maintained by the nuns of Sant’ Ambrogio and affirms the women’s agency and engagement in urban society. Revealing the use of an artistic commission to promote the secular standing of a community of nuns in early modern Florence, I will advance feminist art history scholarship in the fields of patronage and identity in Canada. Disclosing strategies by which early modern women overcame gender barriers to build secure lives for themselves, I will contribute to contemporary studies of the strategies by which gender inequality is fostered and overcome.

Grid integration, analytical assessment, and optimization of the KETTI-developed wind turbine.

The proposed project aims to establish a technical and scientific foundation for integrating KETTI’s innovative wind turbines into the Microgrid Research and Testing Facility at the University of Regina (UofR). The research involves three complementary subprojects:
1. Feasibility and Engineering Requirements Study — defining the integration framework, electrical and mechanical interfacing, and environmental conditions necessary for effective deployment of KETTI turbines within the University of Regina’s microgrid testbed.
2. CFD Modeling and Experimental Validation — investigating the aerodynamic interactions between two small-scale turbines operating in tandem, with a focus on verifying the claimed downstream performance enhancement.
3. Generative AI–Based Turbine Design Optimization — leveraging artificial intelligence to improve KETTI turbine geometries for turbulent flow conditions and enhanced stress-fatigue resistance.
This multi-intern project combines computational modeling, experimental validation, and advanced AI-driven optimization to accelerate the readiness of KETTI’s technology for scalable, distributed renewable energy applications.

L2M – Smart Concrete for Structural Health Monitoring (SCSHM)

This project explores the development and application of smart concrete an innovative construction material enhanced with advanced sensing and self-monitoring capabilities. Unlike conventional concrete, smart concrete integrates conductive or functional additives that enable it to detect stress, strain, cracks, and environmental changes in real time. This technology addresses critical global challenges by improving infrastructure durability, reducing maintenance costs, and enhancing public safety. Our research focuses on optimizing material composition, evaluating mechanical and electrical properties, and testing performance under various environmental and structural conditions. By combining sustainability with intelligent functionality, this project aims to create greener, longer-lasting, and safer infrastructure solutions while paving the way for future smart cities and environmentally responsible construction practices.

L2M-Designing a Stable CD19 Protein Mimic for CAR T-cell Therapy

CAR T-cell therapy is a type of immunotherapy that displays remarkable potential to treat B-cell cancers. In this treatment, a protein on the surface of B-cells called CD19 is targeted, allowing for the specific killing of B-cells in patients. However, CD19 is a “difficult to express” protein, characterized by high levels of aggregated protein and low yields of active, folded protein.3 Furthermore, CD19 must currently be expressed and purified using mammalian cell lines which are expensive and time consuming. Overall, the use of CD19 for analyzing and testing CAR T-cell cultures is a major bottleneck in the developmental pipeline, and these inefficiencies greatly hinder the process of working with CAR T-cells. In this project, we have designed a CD19-like protein mimic that allows researchers to streamline CD19 production for CAR T-cell therapies by cutting down on costs and time currently required in this process.

L2M- Potatoleaf Doctor( AI model for potato leaf detection)

Agriculture supports food security for 60% of the global population, with potatoes, the 4th most consumed crop, feeding around 1.5 billion people daily (Afakh et al. and LeCun et al.). Canada produces about 5.7 million tonnes of potatoes each year, making it the 12th-largest producer in the world. The country’s potato exports generate around $1.6 billion annually. However, Canadian potato producers lose a significant amount of yield each year due to preventable leaf diseases such as Late Blight and Early Blight. Most small and mid-scale farmers still rely on manual scouting, which is slow, subjective, and often too late to prevent damage. To solve this problem, we plan to design a lightweight AI framework (using computer vision technology) that identifies leaf diseases directly from smartphone/drone images, which also works offline, providing instant and affordable diagnostics. We believe that this model will work at the early stage of detection and contribute to potato growth.