Projets novateurs réalisés

L2M – VirtualSLP

VirtualSLP is an AI-powered platform transforming care for patients with neurologic speech disorders. Conditions such as ALS often cause bulbar impairments—loss of speech, swallowing, and facial expression—that devastate quality of life. Yet clinicians, particularly speech-language pathologists (SLPs), face overwhelming barriers: caseloads as high as 600 patients per year, only 15–20 minutes per visit, and no standardized, easy-to-use tools. As a result, care is inconsistent, delayed, and less effective than it could be.

The need for better solutions is clear. Our team, led by Dr. Yana Yunusova, has decades of experience at the intersection of research and clinical practice. Communities of Practice uniting 200+ SLPs in Canada and the U.S. further highlight the urgency and scale of the problem.

VirtualSLP addresses this gap with a web-based platform that integrates remote multimodal assessments and AI-driven insights. Patients complete validated assessments online, while the platform extracts 200+ acoustic and movement features to detect early signs of bulbar disease and classify severity. Upcoming modules will predict disease progression within a six-month window and deliver GenAI-powered, evidence-based recommendations, enabling personalized, efficient, and scalable care.

Backed by $3M in research funding, VirtualSLP is clinically validated and built with direct input from both patients and providers. With a $400M obtainable market and 70% of SLPs projected to adopt telehealth by 2030, it is positioned to become a game-changing solution that improves patient outcomes, streamlines clinician workflows, and scales across healthcare systems.

VirtualSLP: where AI meets patient-first neurological care.

L2M – Visum Neurotechnologies

The project aims to develop a non-invasive, dual-modality tool combining eye-tracking (ET) and functional near-infrared spectroscopy (fNIRS) to measure cognitive capacity and enable early detection of cognitive impairments in older adults. Using short-term memory tasks, it captures objective biomarkers (e.g., pupil size and prefrontal neural activity) to outperform the conventional tests in sensitivity and cultural neutrality.

L2M Novel Hybrid Scanless Confocal Fluorescence Microscopy System

Our project introduces a novel hybrid fluorescence microscopy system that merges the strengths of light-sheet and confocal microscopy while eliminating their key limitations. The system is fully scanless, removing the need for any motorized scanning devices—resulting in faster, simpler, and more reliable imaging. It also integrates a signal intensifier to detect weak fluorescence and a microfluidic channel for automated, high-throughput sample handling.

From an academic perspective, this technology enables rapid, high-resolution, and low-phototoxic imaging, ideal for studying cancer cells, microorganisms, and developing tissues. The combination of scanless confocality and light-sheet illumination allows continuous, non-invasive observation of cell behavior, division, and drug response in real time, even in samples with weak fluorescence signals.

From a business perspective, the platform offers a compact, affordable, and user-friendly alternative to existing expensive and complex microscopes. By combining speed, resolution, and automation in one system, it reduces costs, training time, and maintenance—providing strong return on investment for research and clinical users. With the global fluorescence imaging market expected to grow from USD 11 B to 20 B by 2030, this hybrid scanless microscopy system stands as both a scientific innovation and a high-potential commercial product for research, diagnostics, and drug discovery.

L2M Highly Efficient DC-DC Converters for Small Offshore Oil Rigs

Offshore oil rigs play a critical role in Canada’s energy industry, but they rely heavily on diesel generators that are expensive, polluting, and difficult to maintain in remote marine environments. This project explores the development of highly efficient DC–DC converters—advanced electronic devices that improve the way electricity is managed on offshore platforms. These converters make it possible to integrate renewable energy sources, such as solar and wind, with existing systems, helping to reduce fuel costs and greenhouse gas emissions.
The goal of this project is to design, test, and evaluate a converter prototype optimized for small offshore oil rigs. By focusing on efficiency, compact size, and durability in harsh marine conditions, the project will provide a practical solution to improve energy reliability and sustainability. The outcomes will benefit Canada by supporting cleaner energy use in offshore operations, reducing environmental impact, and advancing technologies that can also be applied to other remote and marine industries.

L2M: AI-assisted marine ecosystem monitoring

This project aims to turn raw underwater video into decision-ready science. Our software automatically detects, classifies, and counts marine species, then delivers results in a simple web dashboard with linked video clips, maps, and exportable reports. Instead of annotating from scratch, ecologists just review and correct AI suggestions—cutting review time by more than half. Built for real-world conditions, it flags unknown species, handles uncertain IDs with genus- or family-level counts, and works across different cameras, habitats, and seasons. Backed by peer-reviewed methods and expert-annotated Atlantic datasets, and led by my PhD research in partnership with Fisheries and Oceans Canada, this solution combines scientific rigor with deployable AI. Whether regulators, operators, or researchers, our users can ask: “How many cod were at Site 12 in June?” and get traceable, audit-ready answers in minutes instead of months.

L2M – Contactless InfraRed Sleep System

For this proposed project we aim to collaborate with the University of Toronto, Mitacs, and the partnered organization to further develop, refine, and bring to market our novel Contactless Infrared Sleep System (CIRSS). Our novel invention uses advanced specialized infrared imaging to measure key psychophysiological signals including heart rate, heart rate variability, stress, breathing rate, emotion, and more. To date, sleep, sleep health, and sleep pathology remain a growing area of research and public health with over 30% of adults reporting sleep problems. Our innovation addresses significant challenges current clinical monitoring system pose (e.g., accessibility, disruptive to natural sleep, often limited to clinical settings). Through this internship, I aim to improve our current prototype models based on stakeholder and expert feedback, apply for patents, commence large scale studies, and establish a startup company centred around our novel invention. Through this internship, we aim to establish a collaborative relationship with our partnered organization(s) to advance this novel, ground breaking, evidence based invention which has immense commercial and public potential.

L2M-A Triboelectric Nanogenerator-Based Strategy to Minimize Battery Dependence in Ocean Sensing

We are developing a self-powered ocean-energy system that reduces reliance on conventional batteries by using triboelectric nanogenerators (TENGs) to harvest energy from ocean waves, currents, and wind. Unlike many existing technologies that struggle with low-frequency and unpredictable motion, TENGs are lightweight, low-cost, and highly efficient at capturing irregular, low-frequency energy. With nearly 70 % of Earth’s surface covered by oceans, this vast and underused resource can power marine sensors, weather buoys, and environmental monitoring equipment where frequent battery replacement is costly and logistically difficult.

Self-Efficacy for Creative Innovators in Canada

The research project involves studying how incubator programs help artists become successful entrepreneurs in the music industry. Incubators are places where artists get training, support, and resources to build their careers. The interns will collect and analyze data to understand how these programs impact artists’ growth, creativity, and business skills. By examining these effects, the interns will learn about the music industry and gain hands-on experience in research. The expected benefit for the partner organization is to better understand what makes these incubator programs effective. This can lead to improved support for artists and help shape future programs to ensure they meet artists’ needs. Ultimately, the research aims to boost artists’ success and foster a more vibrant and inclusive music industry.

L2M – AI-Driven Cybersecurity for Ocean Critical Infrastructure

This project will develop an AI-powered cybersecurity system to help protect important ocean infrastructure, starting with offshore energy systems such as oil and gas, wind and tidal farms. These facilities are becoming a key part of Canada’s clean energy future but are also vulnerable to cyberattacks that could cause costly shutdowns or disruptions. The project will create tools that can detect unusual activity, test different attack scenarios in a safe digital environment, and suggest quick responses to keep systems running securely. For the partner organization, this work will provide a clear business model, a prototype system, and direct input from industry stakeholders, helping them move closer to offering a unique product that improves the safety, reliability, and trust in Canada’s offshore energy and ocean infrastructure.

L2M – Clean Hydrogen and Ocean Alkalinity Enhancement through Bipolar Membrane Electrodialysis

This project aims to advance a clean technology that uses an electrodialysis process to remove acidity from seawater while producing green hydrogen. Using seawater and renewable electricity, the system removes excess acidity, encouraging ocean carbon uptake. The result is de-acidified seawater capable of absorbing more carbon dioxide, while simultaneously producing green hydrogen as a valuable co-product. This approach addresses ocean acidification, accelerates the natural carbon cycle, and generates a carbon-free fuel. Our modular, cost-effective system can integrate with existing ocean infrastructure for scalable climate and energy impact. It has potential applications in hydrogen-intensive industries such as coastal refineries, desalination plants, green steel, and shipping. With Canada’s vast coastline, renewable energy potential, and leadership in clean technology, this project aligns with national priorities by combining climate restoration with clean energy production, supporting Canada’s net-zero ambitions.

L2M-COASTAIR

Coastal and marine pollution is draining ecosystems and budgets across Canada and beyond, yet we still check shorelines with clipboards or wait for low-resolution satellite passes. The result: blind spots, slow responses, and costly cleanups. COASTAIR flips that script. Our AI-enabled aerial platform pairs agile drones with RGB and hyperspectral cameras to spot, classify, and measure pollution, i.e. floating plastics, oil sheens, and even harmful algal blooms, in real time. Each flight streams geo-tagged imagery to an onboard/edge model that flags anomalies, while a cloud dashboard turns detections into clear maps, trends, and alerts the inspection team immediately. Built as a simple subscription, COASTAIR gives coastal municipalities, port authorities, and environmental agencies continuous, decision-ready intelligence at a fraction of traditional costs. Schedule automated routes, track hotspots over time, and export reports for compliance. For partners, the payoff is direct: faster, safer monitoring and earlier interventions that prevent small spills from becoming headlines. Furthermore, a validated rollout plan for pilots and partnerships, including training, regulatory guidance, and shared metrics for success. In short, COASTAIR transforms the coastal monitoring of the coastal region. One can protect water, wildlife, and communities with speed and confidence.

Design and Implementation of a Generalized, Secure, and Scalable API

DiamondAI and McMaster University are working together to create a secure and scalable software interface, called an API, that connects DiamondAI’s career guidance platform with the online systems used by schools and colleges. DiamondAI helps students and newcomers understand their strengths and interests through personalized assessments powered by artificial intelligence. Right now, most schools use platforms such as Moodle, Brightspace, or Google Classroom. Because DiamondAI operates separately, students need to leave their school systems to use it, which reduces engagement and raises privacy concerns. This project will build a solution that allows schools to offer DiamondAI’s assessments directly inside their existing systems. It will support safe data sharing, single sign-on, and compliance with Canadian privacy rules. Ultimately, the project demonstrates how applied AI and software engineering can combine to strengthen Canada’s digital innovation ecosystem, bridge the gap between education and employment, and create meaningful social and economic impact. The new integration will make DiamondAI easier for schools to adopt, improve student access, and protect personal data. For DiamondAI, this project will create a strong foundation for scaling its services across Canada and help build partnerships with educational institutions, advancing both innovation and social impact.