Projets novateurs réalisés

L2M – Path Forward Neurotech

Path Forward Neurotech is bringing cutting-edge brain training technology from York University’s research labs to the business world. The company has developed special exercises that combine thinking and movement using augmented reality (AR) technology, which has been shown to significantly improve brain function and quality of life in just 8 weeks. This project will help the company figure out the best way to sell this brain training program to busy executives and corporations who want to boost their decision-making abilities and mental performance. Over 4 months, the team will interview over 100 potential customers, test different pricing options (ranging from $5,000 for individuals to $50,000 for companies), and develop a clear business strategy. Additionally, the team will explore the regulatory requirements needed to eventually offer this technology as a clinical service for patients with aging-related cognitive decline, concussions, and other neurological conditions. The goal is to create a successful business that helps Canadian executives and organizations improve their productivity while turning 37 years of university research into a real-world solution that benefits society, with a pathway to future clinical applications.

(L2M) Enabling Real-Time Pathogen Detection at the Edge: A Low-Power SoC for Mobile DNA Sequencing

This project will develop a small, energy-efficient hardware device that can quickly analyze DNA to detect harmful viruses or bacteria in real time. Unlike current systems that rely on internet access and cloud computing, this device will do all the work on its own, making it ideal for use in remote or low-resource areas such as rural clinics, farms, or during outbreaks. The partner organization will benefit by advancing its goal of creating portable, low-cost diagnostic tools that can bring cutting-edge health technologies directly to the point of care.

Development of rapid and accurate genomic techniques for ballast water UV treatment – Year two

The United States Coast Guard (USCG) recently introduced stringent regulations for the treatment of ballast water. Ultra-violet (UV) light is a useful technology in a ballast water treatment system (BWTS), for inactivating species which could be invasive and harmful to humans and the environment. UV damages DNA and prevents replication, but the vital stain methods mandated in the USCG protocol do not detect UV damage. Alternative culture-based measures of cellular replication capacity are yet to be approved, time consuming, and have limitations (some species mayn’t grow). Rapid and accurate assays to identify cells that are UV damaged beyond recovery are critical for the acceptance of UVbased BWTSs. Hence practical methods based on genomics/transcriptomics will be developed for rapid, high throughput, on-site assessments of DNA damage related to key functional genes in micro-eukaryotes. TROJAN Technologies will use this technology to validate UV sterilization for ballast water treatment at the global scale.

L2M – Enhancing 3D Bioprinting with Advanced Nanofillers for Regenerative Medicine

This project focuses on developing advanced bioinks enhanced with tiny filler materials to improve 3D bioprinting for tissue engineering. By creating stronger, more printable, and biologically compatible materials, the project aims to help the partner organization advance regenerative medicine technologies. This will support the development of new personalized medical treatments and open up commercial opportunities in the growing field of bioprinting.

L2M -Bringing Compact RF Drone Detection Technology to Market

This project aims to develop a business strategy to bring a new drone detection technology to market. The system uses a compact, reconfigurable antenna to detect and locate drones by listening to the radio signals they emit. Unlike current systems that are expensive or complex, this solution is designed to be affordable, portable, and easy to use. During the internship, we will identify the best markets to enter, explore how the product can be improved to meet real customer needs, and create a roadmap for commercialization. The expected benefit to the partner organization is a clear plan to move this innovation from the lab into the hands of users who need reliable and low-cost drone detection — such as airports, security agencies, or infrastructure operators.

L2M – Development of a user-driven, evidence-informed, AI-powered post-pregnancy support application

The first year after pregnancy is a critical but often overlooked by healthcare systems globally. Many people face physical, emotional, and social changes, yet there is very little formal support. In Canada, most people receive only one six-week follow-up appointment, and support varies depending on who their care provider was. As a result, people with lived experiences of pregnancy turn to online sources, family, or friends for advice. This fragmentation in care leads to missed warning signs, preventable complications, and increased strain on emergency services. To help address this gap, this project will support the development of Hey Aunty!, a novel digital tool that uses artificial intelligence (AI) to provide personalized, culturally sensitive, evidence-based support to people during the first year after pregnancy. It is designed to work alongside medical professionals and not replace them. As a first step, this internship will focus on designing and testing the feasibility of the app’s core feature: an AI-powered conversational companion for tech-savvy, English-speaking individuals who have experienced a healthy, full-term birth. To achieve this, the intern will conduct interviews with end-users and experts. These will also help curate and train a domain-specific AI model that underpins the conversational companion.

L2M – AllerEase: Personalized Allergy-Safe Grocery Tool

AllerEase is a personalized grocery recommendation tool that helps allergy-sensitive users shop safer and easier. It generates user-specific shopping lists based on allergy profiles and live product data. This project aims to refine matching logic and validate usability through real-world testing.

L2M – Characterizing the pathology of airway stenosis in canine patients: a first step to advancing airway stenting.

This project involves histopathological analysis of canine airways with stenosis, a type of obstructive disease that impedes airflow. This research is aimed to characterize the disease and understand the cellular mechanisms that drive the disease progression, and therefore inform the future directions of treatment innovation.

L2M – Load Forecasting Improvement in Smart Grids

This project focuses on improving short-term electricity demand forecasting, which is essential for ensuring the reliable and cost-effective operation of the power grid. Many Canadian utilities are now using demand-side management strategies such as peak shaving, reducing electricity consumption during peak hours, to lower costs and reduce strain on the grid. However, these strategies can unintentionally distort electricity consumption data, making it more difficult to accurately forecast future demand using traditional models.

The project proposes a novel, software-based forecasting solution that detects and adjusts for the effects of peak shaving to address this challenge. The approach uses advanced machine learning techniques to incorporate key indicators, such as the timing and duration of peak shaving events, into the forecasting process. This results in more accurate and robust predictions of electricity demand, even when smart grid interventions have altered the data.

The improved forecasting model will help utilities like Saint John Energy plan more efficiently, reduce their reliance on fossil-fueled backup systems, and better integrate renewable energy into the grid. These outcomes support Canada’s broader goals for environmental sustainability, grid modernization, and energy affordability.

The project contributes to building smarter and more resilient electricity systems across Canada in the long term by equipping utilities with advanced tools to manage clean energy transitions effectively.

Development of rapid and accurate genomic techniques for ballast water UV treatment

The United States Coast Guard (USCG) recently introduced stringent regulations for the treatment of ballast water. Ultra-violet (UV) light is a useful technology in a ballast water treatment system (BWTS), for inactivating species which could be invasive and harmful to humans and the environment. UV damages DNA and prevents replication, but the vital stain methods mandated in the USCG protocol do not detect UV damage. Alternative culture-based measures of cellular replication capacity are yet to be approved, time consuming, and have limitations (some species mayn’t grow). Rapid and accurate assays to identify cells that are UV damaged beyond recovery are critical for the acceptance of UVbased BWTSs. Hence practical methods based on genomics/transcriptomics will be developed for rapid, high throughput, on-site assessments of DNA damage related to key functional genes in micro-eukaryotes. TROJAN Technologies will use this technology to validate UV sterilization for ballast water treatment at the global scale.

L2M – Next-Generation Multisource Thermo-Photovoltaic Receivers for Wireless Power Transmission Networks

We’re developing the first high-efficiency, ultra-long-range, multi-source thermophotovoltaic (TPV)-based wireless power transmission (WPT) receiver, designed to convert laser beams into electricity without relying on fragile PV surfaces. It uses a spectrally selective absorber to convert laser into tailored thermal emission, matched to low-bandgap GaSb cells. Unlike traditional laser WPT systems, it performs effectively in space, underwater, harsh environments, and ultra-long distances, where cables, batteries, or PV-based WPT fail or lead to high losses, costs, and oversized infrastructure. With low beam divergence, low atmospheric attenuation, spectral and laser flexibility, uniform high-power density tolerance, thermal buffering, and photon recycling, our system de-livers unmatched efficiency, scalability, and mission adaptability. From drones and satellites to deep-space and subsea infrastructure, this technology redefines untethered power delivery across Earth, orbit, and beyond.

L2M – LeanPrompt: Accelerating Generative AI with Smarter Resource Utilization

We aim to develop and implement a cost-efficient framework for communicating with proprietary generative AI platforms such as ChatGPT. In fact, these provider companies expose their models through an interface that can be accessed via API. However, calling their API will incur a cost considerably based on our request. Basically, smaller models are cheaper than larger models. However, the smaller models are less capable and may not generate accurate responses. Hence, we aim to reduce the cost while maintaining the accuracy and latency of our product. We will implement this approach with three mechanisms. First, we compress input as the larger queries will charge us more (It is calculated per number of words). Secondly, we use a routing mechanism to use smaller models for simpler tasks and larger models for more complex tasks. Lastly, we will use a caching mechanism to leverage the previously answered data and avoid invoking the models every time. All these approaches would reduce our cost, and also they can preserve the performance of our model with smart techniques. At the end, the partner organization can invest its budget more broadly across different objectives. Also, they can engage more user on their platforms by providing accurate responses.