Projets novateurs réalisés

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.

Development of room-temperature exciton-polariton quantum simulator

This MITACS project between the University of Waterloo (UW, host supervisor: Prof. Na Young Kim) and Yonsei University (YU, home supervisors: Prof. Jong-Souk Yeo and Prof. Chae-Yeun Park) aims to develop a room-temperature exciton-polariton quantum simulator. The room-temperature system can contribute to solving a complex many-body problem, since it does not require cryogenic cooling that limits the scalability of quantum information processors. The exciton-polariton quantum simulator is a solid-state platform that can be fabricated using conventional deposition/lithography techniques. Since the exciton-polariton is a bosonic quasiparticle that also interacts strongly with matter, both bosonic and fermionic dynamics can be emulated. The room-temperature operation can be demonstrated by utilizing transition metal dichalcogenides that have exciton binding energies larger than 25.9 meV. We plan to theoretically understand and design the simulator to describe the Bose-Hubbard Hamiltonian, fabricate and characterize a unit exciton-polariton system, and correlate the experimental results with the defined Hamiltonian. This project will be successfully conducted leveraging the UW’s outstanding expertise in quantum simulation and YU’s extensive experience in nanomaterials engineering. The collaboration between the UW and YU, which was officially initiated by signing a Memorandum of Understanding (MOU), will be further strengthened and expanded thanks to the MITACS project.

L2M – A2O

Direct lineage reprogramming (DLR) is the newest advance in the field of cellular reprogramming. It is the forced conversion of one mature cell type to another, without the need for a pluripotent intermediate, directly at the site of injury or disease. We have developed DLR technology to convert astrocytes into new oligodendrocyte lineage cells (iOLCs) for the treatment of central nervous system (CNS) disease and injury. To ensure the success of our technology in clinical trials and increase the likelihood of commercial viability we will source input from stakeholders and key opinion leaders in four main areas. First, we will validate the market fit and clinical need for our technology in our proposed beachhead indication, neuromyelitis optica, through clinicians and patients. Second, we will obtain insight into possible delivery methods (AAV, RNA vaccines, LNPs) for our technology. This will be assessed with groups specializing in these methods to determine optimal efficacy and safety parameters but also with clinicians and patients to identify administration preferences. Additionally, we will meet with regulatory bodies and intellectual property experts to understand the regulatory landscape, clinical trials space and licensing agreements. Finally, we will connect with business strategists and pharmaceutical companies (end buyer) to come up with a market plan and licensing model that supports our vision of a platform technology. As a result of this project we will be able to make evidence-based decisions that de-risk our technology, accelerate our path to clinical trials, and position us for successful partnership negotiations.

Towards Reliable Vibration Design of Mass-Timber and Timber-Concrete Floors: Data, Models, and Practical Guidance

The proposed project studies the vibration behaviour of innovative mass timber floor systems, focusing on cross-laminated timber (CLT) floors supported by CLT beams and timber-concrete composite (TCC) floors with notched connections. Using laboratory testing and numerical modelling, the research aims to enhance the understanding of how these floors respond to dynamic loads, including the effects of non-structural components such as concrete toppings, which can add mass and damping. The project will generate validated experimental data and finite element models to develop design guidelines that improve vibration serviceability, occupant comfort, and structural performance. Collaboration between Canadian and Italian institutions strengthens the research by combining expertise and access to advanced facilities. The project aligns with Canadian CSA O86 standards and European Eurocode 5 design provisions, helping harmonize approaches to timber floor vibration across North America and Europe. This collaboration supports the advancement of mass timber design standards, expanding market acceptance of innovative floor systems and enhancing the capacity of participating institutions to address vibration challenges in sustainable construction. Through joint supervision, knowledge exchange, and shared resources, the project aims to establish international partnerships and contribute to safer, more efficient timber buildings.

TRLUP– Zester

Many Canadian companies and researchers struggle to find affordable, secure, and easy-to-use data labelling tools. Most existing platforms are foreign-owned, which can cause privacy issues and make it harder for small teams to work with sensitive information. It also prevents us from building local expertise and jobs in AI data management. Zester aims to solve this by developing a secure and scalable data labelling platform built entirely in Canada. The first version will focus on agricultural data because Saskatchewan has a strong base in agri-tech and applied research. The Minimum Viable Product (MVP) will support both manual and AI-assisted labelling, and it will be built in a way that can later support a no-code automation feature so users can design their own workflows easily. For Saskatchewan Polytechnic, this project creates a bridge between classroom knowledge and real-world software development challenges. For North Forge, it shows how a locally built data tool can grow into a larger startup opportunity. By the end of the internship, the project will deliver a functional MVP, a detailed technical and business report, and a go-to-market strategy. This project helps Canada strengthen its AI infrastructure while keeping data safe and jobs local.