Projets novateurs réalisés

Real-Estate Industry Transformation Through Innovative AI Platform Development

Royal Trillium Homes Inc. is a startup focused on transforming the real estate industry through creating an innovative platform and data-driven solutions. Through extensive market research, incorporating insights from PricewaterhouseCoopers (PwC), Deloitte, and direct feedback from clients, agents, and brokerages, critical inefficiencies exist. Currently the real estate industry relies on outdated processes, leading to mismatched client-agent pairings, limited agent visibility, and inefficient brokerage recruitment. Clients depend on personal referrals and static listings, while agents struggled to stand out in a crowded market. Brokerages faced hiring challenges due to traditional recruitment methods lacking real-time performance insights. This project will bring Information Technology expertise that currently does exist within Royal Trillium Homes, to develop an Artificial Intelligence (AI)-powered platform designed to eliminate inefficiencies in client-agent matchmaking, enhance agent visibility and streamline brokerage recruitment. Addressing these inefficiencies requires expertise of an intern with knowledge in AI, data science, user experience design, and real estate market dynamics, along with collaboration across industry and technology sectors. The fragmented nature of real estate transactions and the slow adoption of digital solutions further compounded these challenges, highlighting the need for a more transparent, efficient, and scalable industry framework.

Integrating AI with ITSM for Smarter Support and Workflow Automation

This project aims to develop and apply AI technology within an IT Service Management (ITSM) system to improve IT support and efficiency. By training Freshservice’s Freddy AI using real data, we will enhance the accuracy of ticket handling, streamline repetitive tasks, and automate routine IT workflows. This will significantly reduce the workload of IT staff, allowing them to focus on important strategic tasks. Co-op students involved in the project will gain hands-on experience in AI implementation and automation, directly linking their academic knowledge to practical industry challenges. Ultimately, this initiative will result in faster IT response times, better user satisfaction, and improved efficiency for the partner organization.

L2M_Marketing Strategy for VT-Patch, a medical smart patch as RPM system.

The VT-PATCH project addresses a crucial gap in pediatric healthcare by introducing a user-friendly medical patch
designed for short-term, continuous monitoring of infants and young children. Unlike the current complex system of
multiple devices and wires, the VT-PATCH offers an all-in-one solution to measure vital signs like heart rate,
respiratory rate, blood oxygen saturation, and body temperature. This innovative approach not only improves the
quality of care for sick children but also enhances operational efficiency in healthcare facilities. By facilitating earlier
patient release, the project increases bed availability, reduces nursing requirements, saves costs, minimizes energy
consumption, and optimizes time management. The VT-PATCH stands out in the market as a tailored solution for
pediatric patients, ensuring effective remote monitoring from an early age and contributing to the advancement of
pediatric healthcare in Canada.

European Responses to China’s Support for Russia in its War against Ukraine: A Study of the Czech Republic, Poland, and the Baltic States

This research project is situated within the broader scientific context of international relations, security studies, and European foreign policy. In recent years, the geopolitical landscape has been significantly shaped by China’s increasing global influence and its complex relationships with both Russia and the West, and it changed even more drastically when the Russia has started its full-scale invasion to Ukraine on February 24, 2022. The study would fill a gap in the literature on Europe-China relations in the context of the ongoing war in Ukraine. While most existing analyses focus on the EU’s collective stance or major Western European powers like Germany and France, this project highlights the often-overlooked perspectives of the Central-Eastern Europe (CEE) countries that are among the most vocal critics of Russian aggression. Relatively little or no scholarship has focused on how specific CEE countries, particularly the Czech Republic, Poland, and the Baltic states, perceive and react to China’s indirect support for Russia. By examining potential variations in the countries’ responses—particularly in light of their differing levels of support for Ukraine—the research aims to identify patterns and explanations behind these differences.

An investigation into the failure trends of an AI tool for the detection of tumour margins

Breast cancer is a global health challenge, affecting millions of women annually. In 2020, it was the most common cancer in 109 countries, including Canada. Breast-conserving surgery (BCS), or lumpectomy, is the standard of care for early-stage breast cancer. The goal of BCS is to remove malignant tissue while preserving healthy tissue but achieving tumor-free margins remains a significant challenge. Permanent histopathology, the gold standard for margin assessment, typically takes 2–5 days, leading to reoperations in over 20% of cases due to positive margins.

Perimeter Medical Imaging AI, a Toronto-based medical device company founded in 2013, has developed an OCT device with an embedded AI decision support module. This tool provides real-time feedback on tumor margins during surgery, potentially reducing reoperation rates. However, the AI module’s performance is not uniform across all patient subgroups and device conditions. A retrospective efficacy study of the AI tool has been published in a peer-reviewed journal, but further investigation is needed to understand its limitations and improve its reliability.

This project will focus on identifying trends in the AI tool’s failure modes, including false positives and false negatives, across different demographics, disease types, and device variability. By addressing these limitations with changes to the annotation or training data composition, we aim to enhance the tool’s accuracy and usability, ultimately improving patient outcomes.

Réingénierie d’un processus de classification de la qualité de placages de bois

Ce projet de recherche consiste à classifier automatiquement du bois plaqué selon 16 catégories à l’aide d’apprentissage automatique. Il sera réalisé en collaboration avec une entreprise, R.Perron, qui collaborera activement pour ce projet en fournissant des données réelles de production ainsi que l’avis d’expert dans ce domaine. Actuellement, cette classification est faite visuellement par un opérateur. Étant donné le grand nombre de catégories (16), l’entreprise souhaite automatiser ce processus, car celui-ci est laborieux et difficile pour l’opérateur. De plus, des erreurs de classification peuvent causer un retour des produits de l’entreprise par ses clients, ce qui emmène des pertes. Aucune donnée n’est actuellement disponible, donc l’objectif de ce projet sera à la fois d’entraîner des modèles de vision pour effectuer cette classification ainsi que d’effectuer la collecte des données, le tout dans un contexte réel de production. Ce projet a donc une nature exploratoire, car nous serons emmenés à tester différentes caméras ainsi que différents positionnements de celle-ci.

Hazelnut alley cropping for the Pacific Northwest

Commercial hazelnuts are a woody perennial crop that requires significant upfront investment. They are typically grown in a monocropped orchard, with bare soil or a closely-cropped grassy orchard floor. Hazelnuts are usually harvested from the ground, meaning that the presence of intercrops is a hindrance for harvest. This results in lower in-field diversity, wasted production space, and unrealized income potential. The use of alley space for production has the potential to increase food and nutrient yields as well as economic returns from the system, but management strategies for doing so without interfering with nut harvest have yet to be established for the Pacific Northwest.

We will establish a hazelnut alley-crop system with the goal of developing strategies for allowing both nut and intercrop harvest. We plan to evaluate the feasibility of intercropping with hazelnuts under three different canopies – single stem, multi-stem, and hedging forms. Strategies may include using harvest technologies such as straddle-harvesters and ATV-pulled harvesters that work in narrow spaces. We will also experiment with different intercrops to develop best practices with the goals of with the goals of allowing efficient nut harvest, maximizing food production from the intercrop, and maintaining soil health and fertility under organic management.

Development of Paper-Based Microfluidic Electrochemical Biosensor for Early Detection of Acute Myocardial Infarction

The project’s goal is to investigate the use of a 2D reduced graphene oxide (rGO) based microfluidic channel driven electrochemical biosensor to detect AMI biomarkers. The device could be able to identify heart attacks before they happen. Therefore, it may be beneficial to reduce the enormous number of deaths brought on by this illness worldwide. Experts from the Universities of Waterloo in Canada and Jadavpur University in Kolkata will supervise this study. The researcher intends to work with top scientists and make use of cutting-edge resources by enrolling in the Mitacs Globalink program in order to gain a deeper understanding of this process. The results may result in important breakthroughs in medical technology and research. Shared resources, expertise, and the possibility of ground-breaking medical advancements are anticipated to be advantageous to both educational institutions.

Development of new solid lubricant coatings for different industrial applications

This project focuses on the development, testing, and validation of next-generation solid lubricant coatings for cutting tools designed for hard-to-cut materials commonly used in aerospace and automotive industries. Traditional machining processes often rely on oil- and water-based lubricants, leading to excessive waste, environmental contamination, and increased operational costs. By introducing advanced solid lubricant coatings, this project aims to enhance machining efficiency while reducing industrial waste and water consumption.
The research is centered on improving tool performance in demanding machining applications. The coatings are engineered to minimize friction, reduce heat generation, and extend tool life, ultimately optimizing productivity. Hard-to-cut materials, such as heat-resistant alloys and composites, pose significant challenges in conventional machining. These coatings provide a dry, eco-friendly solution that improves cutting efficiency and also decreases reliance on liquid lubricants.
By addressing critical challenges in machining technology, this initiative supports a transition toward greener, more sustainable manufacturing practices. The outcomes have the potential to benefit industries seeking high-performance solutions that align with environmental sustainability goals while maintaining production efficiency.

Implementing a Computer Vision-based Collision Avoidance System for a Collaborative Surgical Assistant Robot

This project aims to enhance the safety, precision, and efficiency of robotic surgical assistants — collaborative robots designed to work alongside surgeons — by developing a computer vision-based system that prevents collisions in real time. Using cameras and advanced image processing techniques, the system will monitor the operating room, detecting people and other obstacles. It will then use an adaptive decision-making to adjust the movements of the robot based on sensory information. By reducing risks for both patients and medical staff, this technology will make robotic assistance more reliable, reducing the physical strain on surgeons. In the long term, this will allow surgical teams to operate more efficiently, reducing the number of personnel needed per procedure, and thus improving accessibility to advanced surgeries in underserved regions. Participating institutions benefit by advancing their research in artificial intelligence, robotics, and healthcare technology while fostering interdisciplinary collaboration. This project will also contribute to the development of new safety policies for collaborative robots, bridging the gap between computer vision research and surgical robot safety, and paving the way for more intelligent, adaptive, and safer robotic assistants.

Enhancing light-matter interactions with intercalated transition metal dichalcogenides

This research project pioneers the development of novel molybdenum disulfide (MoS2)/copper hybrid materials through electrochemical intercalation and exfoliation techniques. By transforming readily available powdered molybdenite—a byproduct of Canadian mining operations—into atomically thin layers with enhanced properties, the work creates a sustainable pathway for producing high-performance two-dimensional materials without organic additives that typically compromise conductivity. The integration of copper introduces unique plasmonic resonances that dramatically enhance light-matter interactions in the near-infrared spectrum, opening the possibility for advanced photodetectors with exceptional responsivity. This collaboration benefits the German host institution by advancing cutting-edge nanomaterial fabrication methods and quantum material design, while providing the Canadian home institution with an innovative approach to add value to domestic natural resources. The project’s focus on scalable production methods bridges fundamental quantum materials science with practical industrial applications, potentially advancing fields ranging from quantum sensing to night-vision technologies, while strengthening both countries’ positions in sustainable advanced materials development.

L2M – Commercial Validation and MVP Development of a Pediatric Biofeedback Tool for Grip and Pinch Strength Assessment

Pediatric therapists struggle to obtain accurate grip strength measurements because current tools were originally designed for adults. These tools require excessive hand strength and fail to engage children, leading to unreliable data. As a result, therapists often resort to subjective estimates, making it difficult to track progress, tailor treatment plans, or justify therapy to insurance providers. Our solution is a child-friendly grip strength tool with sensory feedback (lights and sounds) to make assessments engaging and ensure precise, reliable data.

Through the L2M Internship, we will focus on three key areas: IP & regulatory groundwork, business strategy, and product development. First, we will file a provisional patent and prepare regulatory pre-submissions to navigate compliance for our Class II medical device. Second, we will refine our go-to-market strategy by assessing distribution channels, reimbursement pathways, and commercialization partners. Third, we will test and iterate our prototype with pediatric therapists and children, gathering feedback to refine our design and ensure usability. Our goal is to finalize our Minimum Viable Product (MVP) by Q3 2025.

By the end of the internship, we will be positioned for early 2026 fundraising to support batch manufacturing and market launch, assuming we meet regulatory and IP milestones. This project will lay the foundation for bringing an innovative, evidence-based grip strength assessment tool to market, ensuring pediatric therapists have the data they need to make informed treatment decisions while making therapy more enjoyable for children.