Projets novateurs réalisés

TRLUP – ML Insights and Metrics

More than 90% of pharmaceuticals fail to impact fail to pass regulatory approval due to toxicity or lack of effectiveness. 50% of those failures have the potential to impact patient lives and existing therapeutics can be repurposed to expand therapeutic options if they can be delivered in smaller dosages to where they are needed most. The project aims to accelerate the development of nanoparticles that are modified to deliver therapeutics to where they are needed most (targeted therapeutic delivery).

The technology uses AI to extract and index the qualitative and quantitative data about how nanoparticles are made and how they interact or impact cells and in animal models once administered. The data presented in an analytics platform and search engine, allows users to perform head-to-head comparisons of their formulations with peer-reviewed literature. This allows users to have confidence in their work and gain insights on how to design or make nanoparticles suitable for their therapeutic of interest and application.

If successful, this solution will generate enough data to train AI models that can simplify the R&D process by predicting how to make nanoparticles for a specific purpose; to load and deliver a therapeutic to where it is needed most by accounting for safety and how it will interact in the body. This will provide Canada the tools to accelerate the creation of safer and more effective solutions for patients – providing more therapeutic options for tailored therapies for patients.

Bridging Symbolic and Audio Spaces for Interactive Music Generation

This project explores a new workflow for creating adaptive music for video games and other interactive media. Today, most interactive music relies on pre-recorded audio segments, which limits flexibility and requires composers to manually prepare a large number of variations and transitions. Our research takes a different approach by working with MIDI, a digital data format that represents musical notes and instruments rather than audio recordings. This makes it easier to control, transform, and generate music using artificial intelligence and facilitates musical interpretation.. The project will develop a complete pipeline: first converting audio into MIDI using transcription models, then using a transformer-based AI model to generate smooth transitions or variations between musical segments, and finally synthesizing the result back into expressive, high-quality audio. By doing so, we aim to give developers and composers more tools to create rich, seamless, and responsive soundtracks without requiring advanced musical knowledge. For the partner organization, this research will strengthen their software by adding cutting-edge generative capabilities, making it more attractive to game studios, interactive artists, and educators who want affordable and flexible solutions for adaptive music.

TRLUP-Commercialization Strategy for a Secure Enterprise Management Platform (SEMP) for SMEs and Institutions In Nova Scotia

I am developing and commercializing a cybersecurity and enterprise management platform through Shortlist Enterprises Canada, a Nova Scotia–based technology company that integrates information security, data management, and business process automation into one scalable solution. The platform is designed to help small and medium-sized organizations operate more securely and efficiently while enabling compliance with ISO 27001 and Canadian privacy standards.
This project represents a continuation of my academic and professional journey in cybersecurity and digital forensics. I hold a Bachelor’s degree in Computer Security and Forensics from Kabarak University, Kenya. I am currently in Year 2, Semester 1 of my Diploma in IT Systems Management and Security at Nova Scotia Community College (NSCC), and in Year 1, Semester 2 of my Master of Science in Cybersecurity and Digital Forensics at the Open University of Kenya. These programs have equipped me with a blend of theoretical and applied expertise that I am leveraging to build this innovative, Canadian-born technology.
Through this initiative, I aim to strengthen Canada’s innovation ecosystem by developing a homegrown cybersecurity product that can compete globally. The project will empower local talent in Cape Breton, create knowledge-based jobs, and demonstrate that world-class digital solutions can be built in Atlantic Canada and exported worldwide.

TRLUP – Lickety Splint

RAR Innovations is a woman-owned, Newfoundland-based start-up focused on improving emergency-care technology through the development of the Lickety Splint (LS), a one-piece, wrap-around compression immobilization device that combines the function of a splint and a bandage. Unlike conventional splints such as the SAM Splint, SpeedSplint, or improvised wooden planks, the Lickety Splint can be applied single-handedly within an average of 17.26 seconds during the critical “Platinum Ten Minutes” following trauma, when rapid compressive support can prevent death from blood loss (Standing Committee on National Defence, 2014, p. 14).

This innovation addresses a significant gap identified in both field studies and the House of Commons Standing Committee on National Defence (2014): excessive application time and complexity in limb immobilization contribute to preventable deaths from exsanguination. Current devices often require multiple components and at least two trained responders, which slows intervention in high-stress or remote environments.

The Lickety Splint is designed to immobilize limb injuries such as fractures, sprains, and compound wounds. It uses elastic compression bandages, birchwood-dowel supports, and Velcro closures to provide adjustable pressure, stability, and protection against contamination while accommodating swelling. Its radiolucent and reusable construction ensures compatibility with medical imaging and durability for training or field deployment. Compact enough to fit into a standard first-aid kit or M9 medical bag, it supports both civilian and military emergency readiness.

Primary users include paramedics, first-aid instructors, military and sports medics, and remote-site responders, though its intuitive design allows it to be used safely by untrained individuals in emergencies. For professional responders, the LS reduces time-to-care, and for everyday users, it offers an accessible, pre-assembled immobilization option that prevents improper splinting or delayed stabilization.

TRLUP- Equlantic Aquatic Monitoring Inc,

Equlantic is a water quality monitoring company developing advanced technology to support countless sectors that rely on sound water quality data. Our modular sensor suite collects high-resolution, real-time data across key chemical, physical, and biological parameters in aquatic environments.

By closing critical data gaps, Equlantic enables companies to accurately understand the aquatic environments they are working in. Our patented technology is designed for field deployment in dynamic aquatic systems, offering scalable, cost-effective tools for researchers, developers, and regulators alike.

Recognized globally through the Mission Innovation SMART-CDR challenge, Equlantic is building the monitoring infrastructure necessary to unlock the oceans full potential — with transparency, accountability, and scientific integrity at the core.

Investigating Glymphatic Dysfunction in ALS: A Longitudinal Study Using the DTI-ALPS Index

Doctors often use biomarkers which are measurable signs in the body to better understand diseases, track their progress, and test whether treatments are working. Right now, there is no reliable biomarker for amyotrophic lateral sclerosis (ALS), a disease that weakens muscles over time and makes it difficult to move, speak, eat, and breathe. Sadly, ALS patients have limited survival time (3-5 years) after symptoms begin, and there is currently no cure. Our project looks at the brain’s cleaning system (glymphatic system) which acts to filter and remove toxic substances. An impairment in this system might contribute to the damage seen in ALS. We will use special brain scans (MR scans) to measure how well this cleaning system is working and test if these measures can serve as a biomarker for ALS. This research could help doctors track ALS more accurately, provide more personalized care, and guide new treatments for this fatal condition. Our work will further the mission of the Alberta ALS Research Network in improving the quality of life of ALS patients through earlier detection

TRLUP-QuantumSecure Wireless Hub

Developing QuantumSecure Wireless Hub: quantum-safe wireless security integrating Quantum Key Distribution with wireless infrastructure to protect against current cyber threats and future quantum computing attacks. Addressing the $36B quantum cybersecurity market opportunity

TRLUP–New AML Compliance Platform

CanAML Intelligence is an AI-powered compliance platform being developed to modernize Anti-Money Laundering (AML) operations for Canadian financial institutions. The project focuses on building explainable, automated risk detection and FINTRAC-compliant reporting workflows.

TRLUP – Adaptive Radar Health Monitoring with RIS Support and Machine Learning

This project will develop and test an adaptive radar system that can monitor a person’s vital signs and detect falls without the need for wearable devices or cameras. The system uses advanced radar hardware and intelligent software to track heart rate, breathing, and movement safely and privately, even in real home or care environments. The goal is to create a reliable and affordable technology that helps prevent injuries and supports independent living for seniors. The project will benefit DMZ Ventures by advancing a new Canadian-made innovation with strong potential for commercialization in the growing health and safety technology market.

L2M ParkinSense

This project aims to validate and strategically position an innovative digital health solution designed to monitor tremors in individuals with Parkinson’s disease. The system integrates a wearable sensor and a mobile application to provide continuous, objective measurements of tremor severity and patterns — data that are traditionally collected subjectively and infrequently during clinical visits. Over a four-month period, the project will pursue two complementary goals: technical validation and market validation. First, I will conduct a pilot study with people living with Parkinson’s disease to assess the device’s usability, comfort, and measurement accuracy compared to standard clinical assessments. Iterative improvements will be made based on user feedback to ensure clinical relevance and patient adherence. In parallel, I will conduct structured interviews and focus groups with neurologists, movement disorder specialists, and other healthcare professionals to understand clinical workflows, unmet needs, and integration points for the technology. Interviews with patients and caregivers will further elucidate symptom management challenges, willingness to adopt digital tools, and desired features. Insights from these activities will inform a comprehensive competitive landscape analysis and business strategy, including market size estimation, value proposition refinement, and a commercialization roadmap. By combining user-centered design, stakeholder engagement, and strategic market analysis, this project will not only validate the technical performance of the tremor-tracking system but also identify pathways for its adoption in clinical and home settings. The ultimate objective is to bridge the gap between innovative sensor technology and real-world clinical utility, enabling earlier intervention, more personalized care, and improved quality of life for people living with Parkinson’s disease. This project will lay the groundwork for future large-scale clinical trials and commercialization efforts, positioning the technology for meaningful impact in neurology care.

L2M – Designing and validating an AI-based, user-centered tool to assess discourse skills in individuals with aphasia

Up to 40% of stroke survivors can experience aphasia, a language disorder that affects a person’s ability to speak, understand, read, or write. However, one of the most effective ways to accurately assess aphasia, discourse analysis, remains underutilized. Discourse analysis is usually time-consuming and performed manually, leading to varying results between clinicians. It can also be difficult to integrate into busy clinical settings, leaving many stroke survivors in need of this assessment method without access. Practical assessments like discourse analysis are important because they help clinicians choose the most appropriate therapy options and track patient recovery more efficiently.

The purpose of this project is to develop an automated discourse analysis tool powered by Artificial Intelligence that is designed to assess linguistic difficulties in individuals with aphasia. This tool will be used to transcribe aphasic speech, extract its linguistic features, and deliver results in minutes, not hours. Designed by clinicians for clinicians, this tool can be easily used in busy clinical settings, providing clinicians with quick, accurate, and reliable assessments that can support diagnosis, treatment planning, and outcomes. By using this tool, clinicians may save time and reduce variability in their assessments, allowing them to focus on patient care. During this internship, we will conduct the first phase of the project, which includes engaging with potential users like speech-language pathologists, to gain insights into how to design and further develop this tool for the next phases of the project.

In terms of benefits at the health systems level, this tool may reduce wait times, optimize the use of resources, and improve the overall quality of rehabilitation services for patients. By making discourse analysis fast, consistent, and accessible, this tool could support its wider clinical use for individuals with aphasia.

MoCaP–ScoRe: Markerless Motion Capture for Precision Scoliosis Rehabilitation

Adolescent idiopathic scoliosis (AIS) is a common spinal condition that affects posture, movement, and quality of life. Deficiencies in proprioception, the body’s sense of position and movement, may play a role by disrupting balance and postural control, contributing to spinal misalignment.

Scoliosis is typically assessed with static images such as X-rays, which cannot show how the spine moves during daily activities. The most accurate technology for studying movement, marker-based motion capture, is costly, complex not allowing for routine clinical care.

This project will develop a safe, non-invasive, markerless system that uses machine learning (ML) trained on surface scans and X-ray images. The result will be an easy-to-use app that allows clinicians to assess posture, spinal movement, and postural control in real time.

In partnership with Curvy Spine, a specialty scoliosis clinic, the project will deliver a tool to track progress, personalize treatments, and measure the effects of exercises and bracing. This practical and scalable system will reduce reliance on repeated X-rays, improve clinical assessments and personalized intervention. Ultimately, it brings cutting-edge science into everyday practice to improve outcomes for young people with scoliosis.