Innovative Projects Realized

A reverse genetic platform for in-vitro assessment of lead vaccine candidates for poultry

High-stock density farming is a common strategy in the poultry industry to support the upgrowing global consumption of meat. However, this type of intensive practice favors the transmission and spread of infectious diseases such us the Infectious Bursal Disease (IBD), which outbreaks are massive and have devastating economic consequences.
Current vaccines against IBD have variable efficiency in the field, mainly due to the interference from maternal antibodies, and the emergence of very virulent strains. The economic impact of IBDV variants in Canadian poultry farms has raised concerns about the effectiveness of existing breeder vaccination programs, casting doubt on the adequacy of current immunization tools. The consensus among stakeholders is that novel approaches must be explored to increase the safety and efficacy of IBD vaccines.
The current proposal leverages the use of in-house generated IBDV reverse genetic systems, using them as valuable tools to screen for and understand the role of key viral proteins in the virus life cycle. Ultimately, the knowledge gained from these studies will be fundamental to inform the rational design of novel vaccine candidates for poultry.

Investigating Protein Turnover in Athletes During Low Energy Availability

Low energy availability (LEA) is a major challenge facing both sport and public health and occurs when there is a mismatch between dietary energy intake and exercise energy expenditure, leaving inadequate energy to support physiological processes. Chronic or severe LEA can negatively affect health, including adverse effects on skeletal muscle metabolism. Athletes are especially susceptible to periods of LEA, either intentionally (e.g. to manipulate body composition) or unintentionally due to high training volumes. The purpose of this project is to investigate changes to skeletal muscle and whole-body protein turnover in response to short-term, severe LEA exposure in elite-level male and female athletes, using different methods (diet- and exercise-induced) to achieve LEA. The study design leverages the unique infrastructure available at Australian Catholic University, including access to an elite athlete population, a human metabolic chamber, and support facilities to control diet, while drawing on the methodological expertise of McMaster University in tracer applications for protein turnover. The integration of mechanistic and applied research approaches opens the door to future large-scale, multi-site projects examining nutrition and exercise interactions across different populations.

Pour des recherches véritablement inclusives : élaboration d’un programme de formation codéveloppé avec des adultes ayant une déficience intellectuelle

(1) Activités principales du partenaire : Vivre Grand est un organisme à but non lucratif dont la mission est de favoriser la qualité de vie, l’inclusion sociale et le mieux-être des personnes ayant une déficience intellectuelle, des personnes autistes et de leurs proches. L’organisme veille à la défense de leurs droits, soutient leur participation sociale et contribue à bâtir une société inclusive et bienveillante.
(2) Défis à résoudre : Vivre Grand souhaite renforcer son rôle actif en recherche tout en favorisant une participation plus diversifiée et équitable de ses membres. L’organisme fait face à une demande croissante de collaboration avec les milieux académiques, ce qui nécessite des outils adaptés pour soutenir l’implication des personnes concernées.
(3) Avantages escomptés : Ce projet permettra à Vivre Grand de bonifier son expertise en recherche inclusive, de développer un programme de formation adapté à ses membres, et de se positionner comme un leader en démocratisation de la recherche. Il contribuera à accroître l’impact social de l’organisme et à élargir son offre de services.

Autonomous Code Refactoring and SQL Optimization with an Agentic AI-Powered Editor

This project will develop an advanced AI-powered code editor that can automatically improve and optimize large software projects. Unlike current tools that only help with small code suggestions, this system will be able to review entire codebases, fix problems, and make them easier to maintain. It will also improve the speed and efficiency of database queries, which are critical for business applications. By building this tool to work at scale and in cloud environments, the project will help Farpoint Technologies strengthen its position as a leader in AI-driven software solutions and create a product that can support enterprises and researchers around the world.

Talent CRM

This project aims to build a comprehensive Talent Customer Relationship Management (CRM) system, a business intelligence tool designed for organizations to track and optimize talent progression through data-driven insights. The system will synthesize user profiles, market data, and engagement metrics from our AI coach, recruiter, and mentor agents to deliver actionable insights for career development optimization.

The Talent CRM addresses a critical gap in how career centers and companies manage talent development. Currently, organizations struggle with fragmented data across multiple systems, resulting in missed opportunities for timely interventions and support. Our solution will provide unified dashboards displaying individual and cohort progression, enabling data-driven decision-making for career services professionals.

The project will be executed across three semesters with a Master of Management Analytics student:
Phase 1 (Semester 1): Foundation and data architecture development. Focus on establishing the analytics infrastructure, integrating data sources from AI agents, and building core talent tracking capabilities with basic cohort management and reporting dashboards.

Phase 2 (Semester 2): Advanced analytics and automation implementation. Development of machine learning models for journey detection/generation, predictive analytics for student success factors, and automated intervention recommendations. This phase emphasizes sophisticated analytical models for talent trajectory prediction.

Phase 3 (Semester 3): Integration and optimization. Seamless integration with existing DotsCareer enterprise solutions, performance optimization, and development of executive-level analytics features including weekly summaries and risk alerts.

Key technical components include real-time data processing, advanced visualization tools, and privacy-compliant data handling frameworks for educational settings.

Expected outcomes include full integration with our enterprise solutions, enabling enhanced insights and value delivery to current customers. This integration will provide comprehensive analytics capabilities, validated predictive models for career outcomes, and actionable intelligence empowering organizations to make data-driven decisions for improved talent development and retention.

Transforming Drug Discovery and Regenerative Medicine with EVOP: A Platform for Precision, Efficiency, and Insight

The primary challenge addressed by 3GO is the predictability gap in preclinical drug testing. Traditional models often fail to replicate full-organ function, leading to high failure rates during clinical trials. This shortcoming has been emphasized in the drug discovery landscape as a limiting factor in accelerating therapy development. Internal consultations with Dr. Rogers’ lab and conversations with academic lab collaborations, particularly those developing treatments for organ-targeted diseases, resonate with the need for models that deliver higher translational reliability, lower cost, and shorter development timelines.

Enhancing Event Outcomes: Real-Time Attendee Matching and Engagement Strategies

PopIn is a mobile platform that helps people discover and connect with others at live events, venues, and social spaces. Whether someone is at a conference, a local bar, or a networking mixer, PopIn shows who’s around, what they’re up for, and who wants to meet — making it easier to break the ice and spark meaningful conversations.

This internship will support Popln’s next-generation mobile platform by integrating new prototyping features, applying data science principles, and architecting systems that are scalable, secure, and intuitive. This includes development of advanced, innovation-driven features like predictive machine learning models or intelligent user-matching algorithms. The project includes:

1. A smart matching system to pair attendees based on shared goals and interests.

2. A predictive event dashboard that uses machine learning to forecast attendance trends.

3. Real-time notifications to help users connect at just the right moment.

4. A full rollout to iOS and Android app stores using Expo and React.

Interns will gain practical experience working across the full technology stack — using React for the mobile interface, Python Flask for the backend, and MySQL for data management. Their work will help bring people closer together in a world where meaningful connection often gets lost in the noise.

Development of a Deep Learning Workflow for Nuclear Segmentation and Classification in IHC-Stained Breast Cancer Slides with QuPath Integration

This project aims to develop an AI-driven pipeline for nuclear segmentation and classification in immunohistochemistry (IHC)-stained cancer slides, integrated into the open-source digital pathology platform QuPath. In collaboration with Alberta Health Services (AHS) and the University of Alberta, the project addresses a pressing need for scalable and interpretable tools to assist in cancer biomarker assessment—particularly where accurate detection of tumor nuclei is critical, such as in PD-L1 scoring for immunotherapy.

The intern will begin by curating a high-quality dataset of IHC whole slide images (WSIs), ensuring consistent annotations and staining protocols. Next, leading segmentation models like StarDist and PathoSAM will be benchmarked, followed by development of classifiers for nuclear subtypes using either multi-task learning or post-segmentation inference. The final pipeline will be deployed as a QuPath plugin using Python and PyTorch for seamless use in clinical workflows.

Deliverables include trained models, evaluation reports, visualization tools for quality control, and an operational QuPath plugin. External validation from a site in London, Ontario, will support generalizability. This project will enhance diagnostic accuracy and efficiency while contributing to Canada’s digital health infrastructure through open, reusable AI tools in pathology.

Data-driven Decision Making to Balance Environmental and Economic Impacts of City Growth

The building sector represents Canada’s third-largest source of greenhouse gas emissions and is one of the most difficult sectors to decarbonize. Policymakers must reduce emissions while also addressing Canada’s severe housing affordability crisis. Without effective tools to find solutions that tackle both issues together, there’s a risk that neither may be resolved successfully. This project will result in the development of a customized tool that will help policymakers design integrated solutions that advance both affordable housing and emissions reduction goals. This tool will also support Clean Prosperity’s efforts to develop pragmatic solutions to grow the low-carbon economy in Canada.

BlueKit AI

BlueKit Software is a New Brunswick–based EdTech company developing a patent-pending, hands-on learning platform that empowers students to build real-world smart technology solutions while gaining foundational AI literacy. Its core product, BlueKit, combines custom IoT hardware, a no-code visual project builder, and standards-aligned content to deliver a fully integrated learning experience. As AI transforms education, BlueKit embeds intelligent instructional tools, real-time assessment, and adaptive learning pathways directly into the classroom—enhancing both student engagement and teacher effectiveness. This project advances the development of applied AI features that not only improve teaching and learning outcomes but also help students understand and interact with AI responsibly and creatively.

Monotuiles pentagonales et heptagonales

Depuis l’Antiquité, les mathématiciens s’intéressent aux pavages du plan, ces motifs réguliers qui permettent de recouvrir une surface sans trous ni chevauchements, à l’image des carrelages ou des mosaïques. Si les motifs utilisant des triangles, des carrés ou des hexagones sont bien connus, les chercheurs s’attaquent aujourd’hui à des figures plus complexes et moins étudiées : les polygones irréguliers, en particulier ceux qui ne sont pas convexes.

Ce projet de recherche s’intéresse à une question encore largement ouverte : peut-on paver le plan avec des heptagones non convexes (des polygones à sept côtés qui « rentrent vers l’intérieur ») ? Et si oui, comment les construire, les représenter, les classer ? Les heptagones non convexes n’ont pas encore été systématiquement étudiés. Ils posent des défis à la fois géométriques, combinatoires et esthétiques, car leurs angles et leurs formes rendent difficile l’assemblage sans chevauchements ni trous.

Customization of the wheelchair skills training program for people with neuromuscular disease

This project evaluates an evidence-based wheelchair skills training program tailored for individuals with ARSACS and DM1, conditions currently underrepresented in mobility training resources. In partnership with Muscular Dystrophy Canada (MDC) the study aims to create customized materials—such as posters and a handbook—to support wheelchair users. MDC’s involvement ensures findings are effectively translated into clinical practice and policy, addressing training needs and improving access to innovative therapies. By documenting the impact of the training on mobility, the project will generate relevant tips and materials for MDC clients. Enhanced wheelchair use can significantly improve physical health and psychosocial well-being, enabling greater participation in social and vocational activities and promoting independence for those with neuromuscular diseases.