Projets novateurs réalisés

Development and Characterization of Zr-Doped Multifunctional High-Entropy Alloy Coatings

This project aims to develop multifunctional high-entropy alloy (HEA) coatings that protect industrial components from multiple types of damage at the same time, including erosion, corrosion, and hydrogen attack. Using laser-directed energy deposition (L-DED), the research will study how adding zirconium to AlCoCr2FeMo0.5Ni –Zrx changes the coating’s microstructure and improves its performance for real industrial applications, such as aerospace, energy, marine, and hydrogen transport systems. The goal is to create long-lasting, reliable coatings that enhance the durability of critical components in harsh environments. Both participating institutions will benefit from shared expertise, access to advanced manufacturing and microscopy facilities, and opportunities for student training and collaborative research, strengthening their capacity to design and evaluate next-generation multifunctional protective coatings.

Studying collaborative interactions with physical props in extended reality

People in VR can see each other and see the same virtual objects, but the feeling of touch is usually weak or missing. It is not realistic to give everyone a full set of physical replicas for every virtual item they use. Instead, this project will explore how one or two simple physical objects can be reused in clever ways so they feel like many different virtual tools or parts. We will create a shared virtual space where two people, each in their own room with a VR headset, collaborate on tasks such as moving, aligning, or inspecting virtual objects. Both will hold only a small number of physical objects, but in VR, they will experience them as several different items that can be passed or shared. We will test various techniques to make this illusion feel natural and convincing, and then conduct a study to assess how well people can cooperate and when the illusion fails. The final goal is to offer clear, practical recommendations to help future VR tools make remote collaboration feel more physical, shared, and affordable.

Automated Structural Analysis and Change Detection for No-Code Automation Workflows

This project focuses on developing and evaluating automated methods for analyzing complex no-code automation workflows used in enterprise environments. Platforms such as Workfront Fusion and Make.com allow organizations to build powerful automations quickly, but as these workflows grow in size and importance they often become difficult to understand, maintain, and safely modify. Changes are frequently made without clear visibility into downstream impacts, which increases operational risk and makes long-term governance challenging. The project will investigate techniques for parsing automation configurations, identifying structural relationships within workflows, and detecting meaningful differences between versions over time. In addition, the project will explore ways to generate clear, human-readable summaries and visual explanations that help technical and non-technical stakeholders understand how automations function.

The expected benefit to the partner organization is improved clarity and control over their automation systems. By making the structure and behavior of complex workflows easier to understand, the organization can reduce the risk of errors when changes are introduced, support more informed technical decision-making, and improve collaboration between developers, administrators, and business stakeholders. These capabilities will also help the organization scale its use of automation more responsibly by supporting better documentation, auditing, and long-term maintainability. Overall, the project aims to transform opaque automation configurations into understandable, manageable systems that can evolve safely as organizational needs change.

Détection, prévision et interprétabilité des sécheresses extrêmes à l’aide de l’intelligence artificielle

Les sécheresses extrêmes constituent un enjeu majeur dans un contexte de réchauffement climatique, affectant l’agriculture, les ressources en eau, la production énergétique et les écosystèmes. Leur caractère multi-échelle, dépendant des interactions entre précipitations, évapotranspiration, humidité du sol et stockage souterrain, rend leur détection et leur prévision complexes. Les indices traditionnels (SPI, SPEI, SSI) apportent une évaluation utile, mais ils peinent à représenter la dynamique complète du système hydrologique, en particulier dans les régions à disponibilité limitée de données.

Les avancées en télédétection améliorent la surveillance, mais nécessitent des méthodes capables d’intégrer des données hétérogènes. L’intelligence artificielle (IA) offre ici un potentiel important. Des modèles tels que Random Forest ou LSTM ont montré de bonnes performances de prévision, mais leur manque d’interprétabilité limite leur utilisation opérationnelle.

L’intégration d’IA explicable, notamment via des approches comme SHAP, permet de lier les prédictions aux processus physiques. Couplée aux projections climatiques CMIP6 (scénarios SSP), elle offre la possibilité d’anticiper l’évolution future des sécheresses, en identifiant les facteurs critiques, tels que l’augmentation de l’évapotranspiration. L’objectif est de développer un cadre intégré pour améliorer la gestion et la résilience des territoires face aux sécheresses.

M.I. Understanding Social Platform Strategy Optimization

M.I. Understanding is a community-centered educational organization that supports families, caregivers, educators, and children as they navigate important social and developmental challenges. Through online learning modules, original puppeted educational videos, and curated guidance to external supports, the organization builds understanding around childhood mental health, PRIDE education, and an upcoming expansion into Fetal Alcohol Spectrum Disorder (FASD). Delivered in partnership with schools, libraries, and community agencies, its programs foster early, supportive conversations and provide families across Canada with accessible tools for emotional resilience, skill building, and navigating complex developmental needs. Because this mission relies on cultivating an informed and connected community, establishing a strong, intentional, and evidence-based online presence has become essential for the organization’s long-term impact and sustainability.

This project will support M.I. Understanding by developing a comprehensive and optimized social media strategy designed to expand reach, strengthen engagement, and make its educational content more accessible to diverse audiences including families, educators, and potential partners. Over four months, the project will accomplish four core objectives: 1) assessing current platform performance; 2) defining overarching social media goals aligned with the organization’s mission; 3) creating platform-specific strategies tailored to each audience; and 4) launching, testing, and refining a prototype content plan. Guided by the Toronto Translational Thinking Framework (TTF), the project will follow an evidence-based, iterative approach that moves from observation and data collection to analysis, contextualization, solution design, prototyping, and refinement.

The project will enhance the M.I. Understanding’s capacity to deliver mental health education, foster community support, and promote early skill development by translating its existing resources into compelling, accessible social media content. A stronger digital strategy will help the organization reach new families, engage existing users more meaningfully, and broaden its impact across Canadian communities. Ultimately, this work will provide M.I. Understanding with a framework that supports its mission to foster understanding.

Advanced characterization of adhesively bonded CFRP-to-concrete systems

In the context of strengthening existing structures, the use of fibre-reinforced polymer materials has been continuously increasing as an alternative to traditional materials, due to their superior durability (absence of corrosion), lightweight, low maintenance cost, and rapid installation. Adhesive bonding techniques for strengthening existing structures, such as near-surface mounted (NSM) and externally bonded reinforcement (EBR) are usually preferable. Despite the increasing knowledge, the long-term performance of these strengthening techniques under hygrothermal ageing conditions is still not clearly understood, limiting their use. This work aims to give new insights for reliable predictions of the long-term performance of these strengthening techniques, supported by advanced and innovative numerical modelling, properly calibrated with a refined experimental program that will be developed in this work. These outcomes will provide new knowledge on the most influencing parameters, serving as a basis for the development of simplified design recommendations.

L2M – NeviSight

NeviSight is an AI-powered platform that transforms retinal imaging into faster, more accurate, and more accessible eye disease detection. Designed as a decision-support tool, it aims to integrate seamlessly with existing fundus cameras and clinical workflows, providing clinicians with real-time lesion segmentation and risk insights. Our completed prototype combines a proprietary hybrid AI model with a user-friendly UI/UX, validated in partnership with ocular oncologists from the Alberta Ocular Brachytherapy Program. By advancing toward Health Canada and FDA approval, securing strategic partnerships, and refining deployment in clinical settings, NeviSight is positioned to reduce preventable blindness and fatal cancer through digital health innovation.

Phosphorescent 3D COFs via Tin and Silicon Precursors: Synthesis, Structure-Property Relationships, and Photophysical Investigation

Ce projet vise à développer de nouveaux matériaux lumineux appelés 3D-COFs capables d’émettre une phosphorescence à température ambiante. En intégrant des atomes lourds comme le silicium ou l’étain dans leur structure, nous cherchons à comprendre comment renforcer cette lumière durable. Ces matériaux pourraient servir à créer des capteurs plus sensibles et de nouveaux dispositifs optoélectroniques.

Projet de recherche Mitacs Globalink – Université des Antilles 2026

Mon projet de thèse vise à étudier les relations entre mort.e.s et vivant.e.s tels que représentés dans la littérature caribéenne de langue française. Les bibliothèques et les archives de l’Université des Antilles contiennent des sources précieuses pour mon travail, comme des thèses inédites, des publications – essais et revues – non disponibles au Canada, et les archives personnelles des écrivain.e.s qui sont au centre de mon questionnement. Ainsi, ce projet Globalink me permettra de parfaire ma thèse et en faire un ouvrage ambitieux et novateur.

Data-driven recovery of at-risk bumblebee species: pinpointing optimal release and recovery sites across Ontario

This project will help protect at-risk bumble bee species in Ontario by identifying the best places to release captive-reared queens back into the wild. Bumble bees are essential pollinators, but many species are declining due to habitat loss and environmental change. While Wildlife Preservation Canada (WPC) has successfully reared several native species, choosing safe and suitable release sites remains a major challenge.

To address this, we will analyze eight years of queen bumble bee survey data collected across Ontario by WPC, combined with environmental information such as land cover, soil conditions, elevation, and vegetation. Using modern geospatial tools and habitat-suitability modelling, we will create detailed maps that show where conditions are most favourable for bumble bee survival and nest establishment. These maps will be tailored to individual species, highlighting the distinct environmental features that support each species’ needs early in the season when nests are founded.

The results will directly support conservation planning by helping practitioners focus resources on high-quality habitats, improving the success of reintroduction efforts, and contributing to the long-term recovery of native pollinators in Canada.

Obligations communautaires en contexte de ferme collective

Le modèle d’affaires actuel des fermes collectives au Québec ne leur permet pas de dégager des marges de profit suffisantes pour rivaliser avec les grands joueurs de l’agro-industrie, ce qui mène la plupart du temps à des difficultés importantes en matière de rentabilité et de prévisibilité du financement pour ce type d’organisation. Ce projet vise donc à améliorer durablement l’autonomie financière des fermes collectives de trois manières différentes : (1) en identifiant les contraintes et les opportunités propres au modèle d’affaires des fermes collectives, (2) en transférant des connaissances/compétences financières pertinentes et adaptées à la réalité des fermes collectives et (3) en évaluant le potentiel d’un outil comme les obligations communautaires au sein du modèle d’affaires des fermes collectives. En plus d’être un outil de financement peu connu dans le secteur agricole, les obligations communautaires ont le potentiel d’engendrer des retombées encore plus significatives lorsqu’appliquées au modèle de fermes collectives. Le projet se déroulera en partenariat avec la ferme collective Les jardins sur le fleuve, mais toute la documentation produite a pour but d’être diffusée sur des plateformes publiques et accessibles et de rendre le modèle accessible aux autres fermes collectives.

Étude des altérations de la structure et du fonctionnement cérébral consécutives à un traumatisme crânien léger chez le jeune enfant.

Le traumatisme crânio-cérébral léger (TCCL) constitue un enjeu majeur de santé publique : les enfants de 0–6 ans sont particulièrement vulnérables, alors que la majorité des travaux se concentre sur les plus âgés, suggérant que les TCCL induisent des altérations cérébrales persistantes au-delà de 3 mois post-blessure. La thèse de V.Chouquet étudie ces altérations cérébrales persistantes, tant structurelles que fonctionnelles, induites par un TCCL précoce, leur association avec la persistance de symptômes post-commotionnels et l’apparition de difficultés cognitives et socio-émotionnelles. Les enfants sont évalués 3 mois post-blessure grâce à un protocole spécialisé combinant différentes techniques d’IRM. Dans ce contexte, les défis majeurs sont la connaissance limitée des conséquences cérébrales d’un TCCL précoce et le manque d’outils optimisés pour le traitement d’images du jeune enfant.
Ce projet Globalink vise à développer une chaîne de prétraitement optimisé dédiés à la neuroimagerie du jeune enfant et de développer des méthodes d’analyse QSM pour détecter des altérations persistantes. Les données canadiennes et françaises permettront de positionner le projet comme un acteur de pointe dans le développement de méthodes avancées en imagerie pédiatrique. Cette collaboration internationale offrira à l’étudiante l’opportunité de développer des compétences avancées en neuroimagerie et en analyse computationnelle.