Innovative Projects Realized

Moderniser l’évaluation foncière commerciale : modélisation des attributs, régressions quantiles spatio-temporelles et approche de nowcasting

Le projet cherche à moderniser la façon dont on évalue la valeur des immeubles commerciaux au Québec. Aujourd’hui, ces évaluations sont souvent basées sur des méthodes anciennes ou adaptées du marché résidentiel, ce qui ne reflète pas bien la réalité plus complexe du secteur commercial. L’équipe de recherche développera donc de nouveaux outils d’analyse capables de traiter automatiquement de grandes quantités de données — comme la localisation, le taux d’occupation ou la qualité des bâtiments — pour obtenir des estimations plus justes et plus réactives aux conditions du marché.

Ce projet est réalisé en partenariat avec CMSD, une entreprise québécoise spécialisée en évaluation immobilière. En combinant leur vaste base de données et des méthodes statistiques avancées, l’objectif est d’améliorer la précision, la rapidité et la fiabilité des évaluations. Ces innovations permettront à CMSD et à ses clients de mieux comprendre les dynamiques du marché et de soutenir des décisions plus éclairées en matière d’investissement et de planification urbaine.

Community Engagement Project

This project goes beyond routine marketing activities by establishing a data-driven, strategic approach to community building and social media platform engagement. The initiative will provide us with the insights, tools, and strategies needed to transform Tribe’s marketing media into an active one. By increasing engagement, we will be better positioned to deliver on Tribe’s mission of supporting racialized entrepreneurs, creating more opportunities for meaningful connections and founder-to-founder collaborations, knowledge sharing, and collaborative growth within the community.

Evaluation of Registered Nurse Prescribing in Ontario

This project, led by Women’s College Hospital’s Institute for Health System Solutions and Virtual Care (WIHV), will evaluate the implementation of Registered Nurse (RN) prescribing in Ontario. Through focus groups with prescribing and non-prescribing RNs, the project will identify barriers and enablers to adoption, as well as organizational and system-level factors influencing uptake. Findings will generate practice-based evidence to inform regulatory policy, workforce planning, and strategies to support equitable implementation of RN prescribing. This project will provide timely insights to guide policy and practice, contributing to improved access, equity, and sustainability within the health system.

Effet du type d’agent compensateur de retrait sur le comportement des mortiers à base de sable de recyclage

L’enjeu de cette recherche est double : approfondir la compréhension scientifique des mortiers intégrant des sables recyclés et adjuvants compensateurs de retrait et identifier les conditions (type et dosage d’adjuvants) permettant d’obtenir des performances comparables aux mortiers traditionnels. Le manque actuel de connaissances, combiné à la sévérité des normes en vigueur, freine encore la commercialisation de tels matériaux. Une des retombées attendues de ce projet serait de contribuer à l’intégration progressive des sables recyclés dans les référentiels normatifs, au Canada, en Europe et ailleurs dans le monde.

Canadian-based investment product solution for personalized direct indexing of client-specific requirements.

One of the fastest growing product categories in the investment industry in the United States is personalized direct indexing. This is a meaningful innovation that allows client specific requirements to be included in the delivery of investment solutions, such as considering concentrated risk positions held by the client, environmental social and governance related preferences, and the tax situation of the investor. To date, there are only two providers of this capability for Canadian enterprises, and both are US based firms. This project will help PICTON Investments bring to market the first Canadian based solution, and further innovate and expand on the capabilities available to Canadians by implementing Canada-specific considerations that have not been considered by the US based solution providers.

PICTON Investments is positioned to be the first to market, ahead of Canadian firms. They were the first to buy access to technology from a US vendor that is a key part of the workflow. They have a unique combination of talent in place to execute on the project because their Second Engine Division enables them to tap into a group of technologists with the skills needed to deliver this capability at scale. They are also the market leader in Canada for expertise in the specific investment discipline required to create this innovative solution.

They have sourced talent from University of Toronto’s Master of Mathematical Finance program who has strong quantitative expertise and the ability to create scalable and efficient workflows. The intern will create a robust backtest of the proposed solution by working in close partnership with an associate from PICTON’s portfolio construction team, together they’ll create a proposal tool allowing investment professionals to create simulations of the solution specific to an individual’s unique circumstances, and optimize the design of the solution in partnership with senior PICTON stakeholders.

Multi-model platform set-up and development of multi-model framework, integrating new and existing Wealth Management asset allocation models

Picton Mahoney Asset Management (PICTON Investments) was founded in 2004 to provide unique investment solutions to institutional, retail and high net worth investors in Canada and around the world. They are 100% employee-owned and manage approximately $15.3 billion in sub-advisory, pension plan and hedge fund assets on behalf of their clients. The Multi-Strategy Team at PICTON Investments uses various models to determine asset allocation preferences across time during different macroeconomic regimes. The primary objective of this projects is to learn about and set up their existing allocation models for use in a multi-model platform. The updated platform will use Python and integrate into their SQL database. This will allow for models to automatically update and have a report generated to alert users of a new update and the required asset allocation change. The second objective is to develop a Multi-Model framework that can take and integrate the signals from the input models in step one and blend them into one final allocation recommendation. This will also include developing a weighting scheme for the models that will use a conviction signal to adjust each model’s contributing weight over time. Additional levers will be explored to shift each model’s weights, as well as a secondary set that only apply to individual asset classes. The third objective is to evaluate by back testing the new multi-model framework and compare its performance to their existing model. This includes applying their standardized statistical analysis that looks at such performance variables as Sharpe ratio, drawdown and hit rate and well as our existing stress testing platform. The new framework will also be used to create allocation recommendations for 40/30/30 and the Wealth management end uses.

L2M-Reinventing Drug Discovery and Cell Therapy Discovery, Validation and Safety Testing

To improve how diseases are treated, our team has developed a single, versatile platform that supports rapid drug discovery, therapeutic safety testing, and evaluation of cell-based therapies. In the longer term, the same technology will enable the bioengineering of transplantable human organs. Our initial work targets the kidney, pancreas, and lung—organs central to major chronic diseases. Current testing systems for drugs and cell therapies are inefficient and often fail to predict human outcomes, leading to high attrition in clinical trials. Our platform generates living three-dimensional organs that reproduce the complexity of animal models while allowing direct observation and measurement in real time. This provides faster, more reliable results, reduces research costs, and accelerates the translation of new treatments from the laboratory to patients.

L2M – FishNet Map: A Satellite-Based Fishing Hotspot Mapping Service

The FishNet Map project aims to create a practical, user-friendly mapping service that helps identify fishing hotspots in near real-time using satellite observations and environmental data. By integrating multi-source satellite observation with real-time weather and sea-state forecasts, the platform pinpoints optimal fishing zones, predicts hotspot shifts, and flags risks such as storms or restricted areas. The project will focus on building an early demonstration version of this service for a pilot region in the North Atlantic, with the long-term goal of scaling it across Canadian waters and beyond. This project addresses a major challenge in the fishing industry: inefficient and time-consuming searches for fish that increase fuel costs, greenhouse gas emissions, and safety risks for crews. By providing data-driven guidance, FishNet Map will help reduce time spent at sea, lower operating costs, and improve decision-making for fishers and regulators alike. The project also supports sustainability efforts by reducing unnecessary fuel use and promoting responsible fishing practices. For the partner organization, this initiative demonstrates how cutting-edge satellite and ocean data can be translated into actionable tools. It will help validate the commercial potential of such a service, establish initial customer connections, and set the foundation for a scalable business model.

L2M – Flow

The goal of this project is to validate and improve the scientific, clinical, and user-centered value of Flow’s AI-powered features in preparation for commercialization. The focus will be on developing a structured evaluation framework, conducting hands-on testing, and iterating based on clinician feedback. This complements the customer discovery and business model testing activities of the Lab2Market Validate program

L2M – Shearwave Labs

This project explores new ways to make medical imaging faster, more accessible, and more efficient in emergency and critical care settings. It aims to address the broader challenge of improving how clinicians can quickly obtain vital diagnostic information when every minute matters. The work combines insights from advanced imaging, computational modeling, and healthcare innovation to develop and assess novel approaches that could enhance patient care and system efficiency. At this stage, the focus is on early research, design exploration, and understanding the needs of healthcare providers and patients. The long-term goal is to contribute to more timely, affordable, and widely available diagnostic technologies that strengthen emergency response and improve outcomes across healthcare environments.

Etude sur la construction d’un stade de Baseball à Shawinigan

L’objectif de ce stage est de réaliser une étude concernant les conditions d’accueil et de construction d’un stade de baseball dans la ville de Shawinigan. Ce stade de baseball devrait servir autant à l’équipe locale de niveau provincial (Les Cascades) que la communauté locale.

Applied AI for EdTech: Automated Reporting and Analytics in VidaNovaVLE

Fenix Alma Solutions Inc. is a Canadian EdTech company that develops VidaNovaVLE™, a purpose-built, partner-driven Virtual Learning Environment designed specifically for medical and health sciences education. The platform supports curriculum management, assessment, clinical scheduling, competency-based medical education, and accreditation reporting for leading institutions across North America. As the number of institutional partners grows, the volume and complexity of data being generated within VidaNovaVLE™ has increased significantly. Partner schools rely on dashboards and analytics to make informed decisions about curriculum delivery, learner progression, faculty performance, and accreditation compliance. Currently, much of the data aggregation and dashboarding work requires manual configuration, custom report building, and ad hoc queries. This approach is time-consuming, limits scalability, and places a heavy operational burden on both the Fenix Alma team and partner institutions. The innovation challenge is to introduce AI-driven automation to make data visualization, reporting, and dashboard generation more intelligent, efficient, and scalable. Specifically, the company seeks to leverage machine learning and natural language processing to (1) automate the generation of meaningful dashboards tailored to institutional roles and objectives, and (2) enable dynamic, conversational querying of data to support evidence-based decision-making at all levels of a medical school.This project goes beyond day-to-day operations by laying the foundation for a new AI-powered analytics layer within VidaNovaVLE™, representing a significant product evolution. Rather than manually producing static reports, the envisioned solution will proactively surface insights, reduce administrative overhead, and enable partner institutions to focus more on educational impact.