Projets novateurs réalisés

Vapor Cell Development for Atom-Based Radio Frequency Sensing

Vapor cell technology is the backbone of a wide range of atomic sensors. This project aims to improve the functionality of vapor cells for Rydberg atom radio frequency detection. The main goal of the work is to test vapor cells whose interior walls are coated with materials that passivate the surfaces so as to eliminate the bonding of the sensor atoms to the surfaces, which eliminates the electric field effects that can decrease the sensitivity of the sensor. The interns will make measurements of a collection of vapor cells using optical spectroscopy and x-ray photoelectron spectrsoscopy to optimize the coatings for radio frequency sensing. The statistics of the measurements will be used to characterize the variation of the vapor cell properties and thereby quantify the reproducibility of the fabrication process. The results will be used to improve the fabrication process with the goal of reducing the variation of the vapor cell properties.

Subspace Hierarchies for Musculoskeletal Control

Musculoskeletal systems are responsible for the rich and diverse ranges of motion and behaviors exhibited by all sorts of animals, including humans. Recreating these motions is key for applications in medical simulation, robotics, and virtual reality. Unfortunately, modeling even simple control tasks such as grasping or locomotion with such musculoskeletal systems is extremely difficult. The key problem is that faithfully modeling the dynamics of the musculoskeletal anatomy requires extremely high resolution detailed deformable geometry. This not only makes simulating such systems slow, but it also makes the optimal control task ill-determined. This in turn makes the numerical method used to solve any optimal control problem at hand struggle to converge reliably to a viable solution.
To solve this problem, we draw inspiration from the model reduction community, which simplifies the dynamics of a system by only modeling its most dominant modes. By only modeling a small number of degrees of freedom, and then gradually increasing that number, we aim to construct a reliable optimal control solver for arbitrarily complex musculoskeletal control problems.

Ergonomic Design of an Automotive Material Sequencing Centre

The Ford Motor Company is bringing 800 jobs into the Oakville Assembly Plant. These jobs will be concerned with
sequencing parts for the new Material Sequencing Centre. To ensure that workers remain healthy, and their
productivity and quality output is up to Ford’s high standards, Ford (through this fellowship) wants to establish clear
ergonomic guidelines for this type of work. The post-doctoral fellow will conduct surveys in the plant, as well as
review existing ergonomic guidelines within Ford. The main focus of this Elevate award is to develop new guidelines as
necessary for the work in the MSC, and to base these standards off of current research, or research to be conducted
during the duration of this award. The priority is to include strong theory and research to make these guidelines, as
they will be used across all Ford platforms around the world.

Development of a Climb Model for Aerodynamic Shape Optimization of Blended-Wing-Body Regional Aircraft

In the interest of sustainability, novel aircraft configurations such as the blended-wing-body (BWB) have garnered a great deal of attention for their potential to dramatically improve aircraft fuel efficiency. The BWB concept has been studied extensively using aerodynamic shape optimization to predict its performance benefits and study the key design features of the aircraft. In many of these studies, fuel burn is used as the objective function and is computed using a combination of empirical relationships and a well-known equation for cruise. While this approach is reasonable for long-range aircraft, a more accurate climb model is needed for regional-class aircraft, where a considerable portion of the nominal mission is spent climbing to cruising altitude. This project will address this issue by studying the impact of various climb models on the optimal design and block fuel burn of a regional-class BWB, optimized using a framework developed at the University of Toronto. Existing models will be tested, and novel approaches for computing climb fuel burn will be developed using methods available at Stanford University. The primary result will be the selection of a climb model for use in future studies that will inform industry decisions regarding the next generation of commercial aircraft.

Design and Fabrication of a Pre-Calibrated Mount/Jig and a Fixed Calibration Fixture for WAAM Thermal Camera

Wire Arc Additive Manufacturing (WAAM) is a promising technology for fabricating large metal components, but consistent thermal monitoring is challenging due to camera positioning variability. This project addresses that issue by developing two key systems: a pre-calibrated mounting jig and a fixed calibration fixture.

The partner organization, which operates a WAAM system, requires precise camera alignment to capture thermal data for process optimization and defect detection. The proposed pre-calibrated jig offers an operator-friendly, adjustable mount with linear scales, quick-release mechanisms, and marked focal distances, enabling rapid repositioning while maintaining accuracy. The fixed calibration fixture will attach to the robot arm, ensuring consistent distance and orientation between the thermal camera and wire tip for each setup. Both solutions involve CAD modeling, technical drawing, and fabrication via additive manufacturing, ensuring precision and integration with the existing setup.

The anticipated benefits of this project include improved process repeatability, reduced calibration time, and enhanced data reliability during WAAM monitoring. For the partner organization, this streamlines AM workflows, enhances thermal control, and supports more robust and scalable WAAM solutions. Socially, the project fosters workforce upskilling in digital fabrication, expansion of professional network, and aligning with broader advanced manufacturing goals.

Demand Forecasting Model

Flashana Technologies Inc. develops software products in retail supply chain, specifically predictive analytics for inventory management.

Current challenges include:
(1) Data harmonization, where manual field mapping consumes a large portion of onboarding time.
(2) Algorithmic competitiveness, while competitors deploy classical forecasting, Flashana targets developing AI/ML integrates model that combines customer history with external factors to contribute effective forecasting approaches.
(3) Scenario-based analysis, considering new developments in business analytics, users need rapid AI-guided evaluation of supplier failures or new policies without corrupting production data.

This research project addresses these gaps by building an AI-based model that benchmarks state-of-the-art AI/ML models and time-series demand forecasting methods, and a data-driven simulation environment where users perform what-if scenario analysis and obtain the predicted supply-chain impact.

L2M – ActiMent

Dementia affects over 55 million people globally, with a new case diagnosed every three seconds 1, contributing to annual healthcare costs exceeding $1.3 trillion, including a projected $16.6 billion in Canada by 2031 2. Up to 40% of dementia cases are linked to modifiable lifestyle factors such as sleep and physical activity 3. However, health-conscious individuals lack accessible, scalable tools to proactively manage cognitive health, often relying on costly, invasive clinical assessments like imaging or blood tests that detect issues only after cognitive decline is evident.
The innovation challenge is to develop a practical, data-driven tool that empowers individuals to reduce dementia risk through daily lifestyle adjustments. ActiMent, an Apple Watch app, addresses this challenge by helping user adjust their routines and align with activity patterns associated with better cognitive health. Built on research from over 50,000 participants, ActiMent reveals how specific daily patterns connect to cognitive performance. Unlike fitness trackers or brain games, ActiMent offers actionable, research-driven insights that empower health-conscious users to take meaningful, proactive steps to support long-term brain health. This project aligns with the Lab2Market Validate Health program’s mission to translate health-related research into impactful solutions, moving beyond academic research by validating market potential and developing a commercialization strategy.
Expertise required includes data science and AI to analyze large-scale actigraphy datasets, software development for creating a user-friendly Apple Watch app, and cognitive health research to translate findings into actionable insights. Entrepreneurial skills are also critical to validate market fit and develop a scalable business model through customer discovery and industry mentorship provided by the University of Toronto’s Lab2Market Validate Health cohort.

Reversing immune evasion through modulation of exosome release

Ovarian cancer is the deadliest type of cancer affecting the female reproductive system. While surgery and chemotherapy are commonly used to treat it, many patients experience a recurrence of the disease that no longer responds to treatment. Immunotherapy—a method that helps the body’s immune system fight cancer—has worked well in other cancers but not in ovarian cancer. One reason is that ovarian tumors create a surrounding environment that weakens the immune system’s ability to attack. Our project is focusing on stopping cancer cells from releasing tiny particles, exosomes, which cancer cells use to communicate with one another to weaken the immune environment around the tumor. So, by blocking the release of exosomes, we hope to improve the body’s immune response to ovarian cancer. We are also combining this new approach with another approach our laboratory has previously studied, which involves turning off the function of protein called BRG1 that affects how genes are controlled in cancer cells. By combining both treatments—blocking BRG1 and stopping exosome release—we aim to make the immune system stronger and more effective at fighting ovarian cancer.

Adaly.AI Generative AI & Data Science Proposal (WLU)

This project focuses on researching and developing an advanced coordination mechanism between our API layers and LLM infrastructure. The goal is to efficiently access and reason across both structured and unstructured data sources to improve customer’s decision-making processes. Success will be measured by the speed in which that data source can be identified, sourced, and integrated alongside the response quality that’s ultimately generated from the implementation.

Demonstrate the potential carbon emission reductions of increasing middle housing supply in Calgary

The Canadian Urban Institute (CUI) is a national convener of city builders — policymakers, urban professionals, civic leaders, community advocates, and academics — who collaborate to advance inclusive, sustainable communities across Canada.
Through this project, CUI seeks to address how Calgary can meet its net-zero emissions goals by 2050 through residential densification. The project models future growth scenarios and evaluates land use policies and climate programs to enable more middle housing in established neighbourhoods.
CUI anticipates significant benefits: improved public and professional understanding of how built form influences GHG emissions by housing type and location, and enhanced decision-making through new analytical tools. These tools support evidence-based planning and climate action by illustrating the emissions impact of various development patterns.
The research findings will inform industry practices and municipal policy, encouraging innovative, cost-effective middle housing strategies. This contributes to increased housing choice, equitable access to well-served neighbourhoods, and reduced sprawl and ecological harm.
Long-term, the project supports a shift toward more compact urban form, with co-benefits for local retail, services, and transportation behaviours, reducing emissions in both the building and mobility sectors — helping Calgary become a more climate-resilient, livable city.

Trade in Services as Transboundary Data Regulation

This research falls at the intersection of the “data, economy and society” and the “digitalization, security and democracy” core research areas. It aims to tackle the consequences of cross-border data flows on democracy and individual’s security, while underlining the economic potential of this data. The research also exists in continuation of CIGI’s two essay series on Data Governance (Centre for International Governance Innovation, 2018, 2024).
Trade in services regulation, despite its intrinsic focus on regulating data flows (Marchetti & Mavroidis, 2011), has failed to keep up with the evolution of the data landscape (Leblond, 2024). Even the CUSMA, which was lauded for expanding the scope of trade issues in emerging legislative areas (Gagné & Rioux, 2022), lays the responsibility for data governance with domestic governments (Chapter 19 – Digital Trade, 2020). This lack of regulatory action has also created a measurement gap, with little data available on the volume and value of data flows (Office of Policy and Strategic Planning, 2016). Considering the looming CUSMA review and the chaotic trade environment, this research will attempt to map the “trade in data” landscape to inform trade policy, providing a pathway for governments, notably Canada’s, to protect their citizen’s privacy across borders.

Generative AI in Corporate: Do Male and Female Leaders Respond Differently?

This project will study how male and female executives differ in the adoption of artificial intelligence (AI) technologies. I use the launch of ChatGPT as the big technological event to understand how executives communicate and discuss the adoption to generative AI. I then analyze how these differences affect how investors react in the stock market. By using data from company earnings calls and stock prices, the project will provide new knowledge about the role of gender in corporate decision-making during the changing era of technology. The research will strengthen connections between the University of Oxford and the University of Calgary, combining expertise in gender and corporate governance with innovation and technology adoption. The project will help both institutions build new collaborations and contribute to important discussions about leadership and technology in today’s economy with a new angle.