Innovative Projects Realized

Relative Attitude and Orbit Estimation of Passive Spacecraft

This study focuses on parameter estimation for passive and non-operational satellites. With the increase in space technologies, space-related activities have expanded and the number of satellites has grown accordingly; as a result, non-operational satellites and potentially hazardous space debris have emerged. Information about these space objects is mostly obtained through measurements conducted from ground-based platforms. This project aims to address this problem by using observations made from a working satellite in space. By studying how inactive satellites move and reflect light, the project seeks to improve our ability to monitor space objects, reduce collision risks, and support safer and more sustainable space operations for future exploration. The project will help the participating institutions build stronger expertise in space technologies, gain hands-on experience with space-based observation methods, and create a solid foundation for future collaborative space missions.

Framing Resilience through a multidisciplinary approach: reference, agency and metaphor in sustainability discourse

This project investigates how the concept of resilience is used and understood in Canadian sustainability discourse, especially in policy documents and public reports written in English and French. Resilience is a key term in climate and sustainability discussions, but its meaning often difficult to grasp; resilience can be used as a property of communities, governments, systems, or individuals and often remaining vague and abstract. Such indeterminacy can lead to confusion or miscommunication, especially when responsibility for resilience is unclear. This project will analyze how resilience is introduced, maintained, and framed by focusing on (i) reference, (ii) metaphoric and other non-literal imagery associated to resilience and (iii) explicit and implicit indicators of agency and responsibility. A bilingual corpus of Canadian and UN documents will be built and annotated using specialized tools to track how resilience is expressed and who is portrayed as responsible. The project will benefit participating institutions by roviding new insights into how sustainability messages are shaped through language, helping improve communication strategies and policy development. Ultimately, the findings will help clarify how resilience is framed in public discourse and support more inclusive and accountable approaches to sustainability and climate governance.

Advancing Responsible Clinical AI through Combined Metrics of Performance, Equity, and Sustainability in Canadian Health Systems

My project will develop and pilot a practical framework to evaluate one clinical AI tool across three areas at the same time: how well it works, whether it works equally well for different patient groups, and what its environmental impact is. I will use the University of Melbourne’s Validitron simulation facility to test the tool in realistic clinical workflows without affecting real patient care, then use the findings to create a draft one-page scorecard that helps health systems decide whether an AI tool is ready to adopt, how it should be monitored, and when it may need to be updated or retired. The University of Toronto benefits by strengthening its capacity to lead responsible, equity-centered AI adoption in cardiovascular digital health, while the University of Melbourne benefits by adding a new evaluation layer to its digital health testing work that includes equity and environmental impact alongside performance and workflow fit. Through the ICTDH partnership connecting Toronto, Manchester, and Melbourne, the project also creates shared methods and templates that can support future joint research and faster translation of digital health innovations across institutions.

Decision-grade, reproducible evidence for transportation policy and community health

Transportation policy is already a health policy because the air we breathe along roads is shaped by what we drive and how we move. In 2026, Canada and the US’s renewed push for electric vehicles (EVs), which can also become an immediate public health opportunity.

Yet health benefits are not automatic, because “zero tailpipe” from EVs does not mean “zero exposure.” As EV uptake rises, pollution composition shifts toward non-tailpipe sources such as tire and brake wear and resuspended road dust, and net impacts become sensitive to regional context, including fleet turnover, electricity generation, industrial background emissions, and ozone chemistry. The same EV target can therefore deliver strong early gains in some places while plateauing or producing localized harms in others, often in communities already overburdened.

At Harvard-UofT, I will build a transferable, neighborhood-scale model that links explicit policy levers to exposures and premature mortality burdens under uncertainty, and I will compare strategies that emphasize new sales against those that prioritize retiring the oldest, highest-emitting vehicles first. The outputs will help agencies identify which policies become dominant levers, and where safeguards are needed so health gains are shared.

ESROP Laser Plume Formation MPSD 2026

This research project explores a fundamental anomaly in fluid mechanics: the potential for simple liquids to exhibit solid-like elasticity under extreme conditions. Conventionally, Newtonian fluids are understood to lack elasticity because they dissipate deformation energy almost instantly through viscous flow. However, research demonstrated that liquid glycerol exhibits “rubber-like” elasticity when subjected to ultrafast, high-strain-rate laser pulses in a vacuum. The primary objective of this project is to determine if this phenomenon is unique to glycerol or if it is a fundamental property of other Newtonian fluids, particularly water. The intern will refine the experimental hardware and software to ensure high-fidelity data, modify the vacuum and optical systems to maintain stable conditions for water experiments, and measure elastic properties at strain rates and correlate macroscopic observations with theoretical models. Understanding the exact dynamics of fluid deformation under laser impact will support innovations in fields, such as biotechnology and material processing, and help improve current technologies. Furthermore, this collaboration between MPSD and the University of Toronto benefits the host institute by helping them refine their complex experimental hardware and software, while the University of Toronto gains a student trained in world-class laser science who can share this new expertise with peers.

BEES App transition

Labatt is transitioning customers from the current BEES ordering platform to the upgraded Canadian BEES App, which introduces new features and incentives, including improved navigation, personalized product suggestions, in-app promotions, BEES rewards, push notifications, and streamlined ordering workflows. While the enhanced app provides significant mutual benefits for both Labatt and its retail partners, customer adoption has varied across the province — particularly among retailers who are not as technologically confident and continue to place phone orders.
This project will support Labatt in improving BEES App adoption and maximizing usage of the platform’s sales-driving features. The intern will complete a strategic analysis of app usage patterns, identify barriers to adoption across different customer segments, and develop targeted education and engagement strategies to increase onboarding and recurring app use. This will include building customer-facing learning resources (e.g., walkthrough materials, short feature guides, tip sheets), recommending communication touchpoints to reinforce app usage, and proposing a results-driven outreach plan to transition phone-order customers to digital ordering.
The outcome of this project will be a measurable increase in BEES App adoption and feature utilization, enabling more efficient ordering for customers and greater ability for Labatt to leverage personalized promotions, push notifications, and loyalty incentives to drive sales

Understanding how trace element contamination affects sedimentary DNA signals for biodiversity reconstruction in lakes

This project aims to improve how scientists use sedimentary DNA (sedDNA) to study past biodiversity in lakes affected by metal contamination. Sedimentary DNA preserved in lake sediments offers a powerful way to reconstruct long-term ecological changes, but high concentrations of trace elements can interfere with DNA preservation and detection, making results difficult to interpret. Through a critical review of existing research and the analysis of sedimentary DNA data from metal-contaminated lakes in Quebec, this project will clarify how metal contamination influences sedimentary DNA signals and their ecological interpretation. The collaboration between the home institution and the host laboratory will strengthen expertise in sedimentary DNA applications, support the doctoral research of the intern, and contribute to the development of more reliable methods for studying and monitoring freshwater ecosystems affected by pollution.

Layer-by-layer Nanofiltration Membranes for the Recovery of Rare Earth Elements

This project aims to develop and evaluate layer-by-layer nanofiltration (LbL-NF) membranes for the recovery of rare earth elements (REEs) from acidic solutions of secondary waste. The research will involve fabricating charged membrane layers on commercially available ultrafiltration supports and testing their ability to concentrate solutions of REEs while maintaining high permeability under acidic conditions. Building on membrane designs previously demonstrated by the host group for scandium recovery, the project will investigate whether similar membrane compositions can be extended to other rare earth elements. The collaboration will benefit the home institution by providing new insights into membrane technology for critical materials separation, while the host institution will expand their study of LbL membranes toward a variety of other REEs.

Conversion des résidus agricoles en nanocatalyseurs pour la valorisation du biogaz en syngaz riche en H2

The proposed project represents a strategic expansion within a larger, multi-phase research initiative aimed at advancing biogas utilization into renewable energy solutions. Currently, the first phase, funded by MITACS, is actively developing advanced purification technology for biogas using Metal-Organic Framework (MOF) filtration materials. The second phase, which includes the current project, builds upon this foundation by focusing specifically on practical applications of the purified biogas. This phase encompasses two complementary research directions: one is the proposed catalytic approach to upgrading biogas into H2-rich fuels, while the other involves developing an innovative, high-efficiency biogas-to-electricity system based on Solid Oxide Fuel Cell (SOFC) technology, funded by the Alliance-NSERC program. The present project is expected to enhance further the overall scope and impact of this larger integrated effort, demonstrating a targeted yet crucial additional step toward realizing comprehensive and sustainable biogas-to-energy solutions.

LA STRATÉGIE DE DIVERSIFICATION DES PARTENAIRES COMMERCIAUX DU QUÉBEC ET DU CANADA EN ASIE

Face aux bouleversements actuels de l’économie mondiale et aux tensions géopolitiques, le Canada et le Québec ont fait de la diversification de leurs partenaires commerciaux une priorité stratégique. L’Asie du Sud-Est, région en forte croissance et au cœur des échanges mondiaux, occupe désormais une place centrale dans cette orientation. Si les stratégies canadiennes vers l’Indo-Pacifique sont bien connues au Canada, on sait en revanche peu comment elles sont perçues par les partenaires asiatiques eux-mêmes.
Ces recherches visent à combler cette lacune en analysant, sur le terrain, la manière dont les initiatives commerciales du Canada et du Québec sont reçues, interprétées et intégrées par les institutions et les acteurs de l’Asie du Sud-Est. Plutôt que d’évaluer ces stratégies uniquement du point de vue canadien, le projet adopte une perspective décentrée, en s’intéressant au regard porté sur le Canada depuis l’Asie.
Mené principalement à Hanoï, le projet permettra de mieux comprendre la visibilité réelle du Canada, les domaines où il est perçu comme un partenaire pertinent, ainsi que les limites de sa présence institutionnelle. Il contribue ainsi à renforcer la compréhension des enjeux de la diversification commerciale canadienne et à améliorer l’impact des stratégies du Canada dans une région clé de l’économie mondiale.

Quantitative Lung Ultrasound Spectroscopy for the Characterization of Lung Injury in a Preclinical Porcine Model

Quantitative LUS (Q-LUS) is a safe, real-time imaging tool that exploits the frequency dependency of imaging artifacts to extract biomarkers that characterize the lung disease. However, the direct relationship between Q-LUS biomarkers and the severity of lung injury remains insufficiently characterized. Without this grounding, Q-LUS metrics remain difficult to interpret and cannot be linked to damage severity or disease evolution.

This project investigates the correlation between Q-LUS biomarkers and the severity of the lung injury. Ultrasound data from preclinical porcine models with healthy lung and induced lung injury will be acquired. Data will be processed to extract Q-LUS biomarkers to statistically associate with different levels of injured severity.

University of Toronto will benefit of advance expertise in Q-LUS, including study design, ultrasound platform, and data analysis. A unique lung ultrasound dataset on controlled large animal models will be generated, positioning Canada as an active and important contributor to the development of Q-LUS.

The university of Trento will benefits of preclinical porcine models presenting an induced, controlled lung injury to acquire data. The outcome of this project will fill a knowledge gap on the conducted research line. Furthermore, this work represent an important component of the intern’s doctoral thesis.

Prédiction de la durée de vie des matrices d’extrusion d’aluminium par modélisation mécanique et apprentissage automatique

Ce projet propose une approche intégrée, combinant modélisation mécanique et apprentissage automatique, afin d’estimer quantitativement la durée de vie en fatigue et de prédire la durée de vie restante (RUL) des matrices. La démarche repose d’abord sur le développement d’un modèle numérique par éléments finis (FEM) de la matrice, intégrant des chargements représentatifs des conditions d’exploitation, afin de calculer les champs de contraintes et de déformations et d’identifier les zones critiques d’amorçage. Un plan d’expériences (DoE) est ensuite défini pour faire varier de manière structurée la géométrie, la position et la taille des défauts, permettant de générer, via des calculs paramétriques automatisés, une base de données traçable regroupant les paramètres de simulation, les indicateurs mécaniques extraits et les estimations de durée de vie obtenues à partir de modèles de fatigue et de propagation de fissures.