Projets novateurs réalisés

L2M – WayLuma

This project will develop an easy-to-use system that helps students automatically fill out university application forms by organizing their information and entering it into different school portals. The goal is to save students time, reduce mistakes, and make the application process less stressful. For North Forge, this project provides a chance to support a new technology that can help thousands of students and families, while also giving them early access to an innovative tool being built through the Lab2Market Validate program. This partnership also allows North Forge to strengthen its role in supporting youth-focused and education-focused startups in Manitoba.

L2M – Market Validation and Commercialization Assessment of Sustainable Activated Carbon Produced Using a Novel Production Method

This project focuses on validating the market potential and commercialization pathway for a sustainable activated carbon product produced using novel pyrolysis of Brewer’s Spent Grain (brewery waste). The work will assess customer segments, value propositions, production feasibility, cost structure, and pilot-scale readiness for Maple Carbon Technologies Inc. The internship will generate evidence-based insights to support market entry, identify early adopters, evaluate competitive positioning, and define the commercialization strategy for launching bio-based activated carbon within environmental, water treatment, and sustainability sectors.

Solving the Distributionally Robust Split Delivery Vehicle Routing Problem via exact methods

This Mitacs Globalink project, part of the PhD thesis titled “Robust Optimization for Logistic Problems,” aims to develop new optimization techniques for solving the distributionally robust split delivery vehicle routing problem under uncertainty. In split delivery routing, customer demands can be served in multiple partial shipments rather than a single delivery, an approach that can reduce total delivery costs by up to 50%, though it significantly increases computational complexity to find the optimal solution. This project will study the problem through the distributionally robust optimization framework, which allows the incorporation of limited probabilistic information without requiring perfect knowledge of the underlying distribution of parameters, a common limitation in real-world applications. As this variant has not yet been explored in the literature, it holds strong potential for improving the efficiency and resilience of modern logistics and delivery systems. Building on the applicant’s previous successful implementations of branch-and-cut algorithms for deterministic and robust variants of the problem, this project seeks to develop an exact method for the new distributionally robust framework. The collaboration between experts from Université Laval and the University of Groningen combines expertise in transportation optimization and stochastic and distributionally robust programming, a promising outcomes that will advance both domains.

IA générative et intelligence éducative : vers des évaluations intégratives multimodales

Les systèmes tutoriels intelligents sont de plus en plus utilisés pour offrir un apprentissage personnalisé dans les environnements numériques. Ils permettent d’adapter les activités aux besoins de chaque apprenant, de suivre sa progression en temps réel et de fournir un accompagnement individualisé. Ces technologies génèrent une grande quantité de données sur la façon dont les étudiants apprennent, par exemple leur niveau d’engagement, leurs stratégies d’étude ou leurs difficultés.

Cependant, les méthodes actuelles d’évaluation utilisent encore des mesures ponctuelles, comme des tests ou des notes finales, qui reflètent mal la progression globale des apprenants et leurs compétences complexes, telles que la pensée critique ou l’autonomie. Bien que l’intelligence artificielle générative puisse analyser des réponses écrites et offrir du feedback personnalisé, elle n’est pas encore pleinement intégrée dans des méthodes d’évaluation complètes et cohérentes.

Ce projet vise à développer une nouvelle approche d’évaluation intégrative, assistée par l’IA générative, capable de combiner différentes sources d’information sur l’apprentissage. En analysant à la fois les comportements d’étude, les productions écrites et les résultats, le système pourra produire des diagnostics clairs et personnalisés. Cette approche aidera étudiants et enseignants à mieux comprendre les progrès, identifier les difficultés et soutenir un apprentissage plus efficace et équitable.

L2M – Auto Byuing Companion

This project aims to develop AutoBuyingCompanion (ABC), a digital advisory tool that helps Canadians make informed car-buying decisions. By processing user requirements, car specifications, and cost of use, as well as environmentally responsible usage, this project will provide recommendations on the most suitable models. By incorporating data analytics with insights from artificial intelligence, this project will enhance car-buying transparency, as well as simplify the comparison process for users. Indeed, the benefiting partner will be able to enhance its position in the industry with the scalable, friendly, and helpful car-buying system.

L2M-Blue Beauty Innovation: Sustainable Seaweed Bioactives for Cosmetics and Skincare

Title “Blue Beauty Innovation: Sustainable Seaweed Bioactives for Cosmetics and Skincare”
This project explores the potential of using natural compounds extracted from sugar kelp (Saccharina latissima), a seaweed that grows abundantly in Atlantic Canada, as safe and effective ingredients for skincare and cosmetic products. Around the world, consumers and companies are looking for cleaner, more environmentally friendly alternatives to synthetic chemicals traditionally used in beauty formulations. Sugar kelp contains valuable bioactive compounds including proteins, lipids, polysaccharides, minerals, and powerful antioxidants such as polyphenols, phlorotannins, and fucoxanthin that may offer benefits including skin protection, hydration, and anti-aging effects. However, it is still unclear whether these seaweed-derived ingredients meet the actual needs of cosmetic formulators, and whether there is a strong market demand for them. To address this, the project focuses on gathering market insights. Through interviews with businesses, researchers, clean-beauty brands, ingredient suppliers, potential customers, and other industry professionals, the project will seek to understand what cosmetic companies require from new ingredients, what challenges they face with current synthetic options, and whether sustainably sourced kelp bioactives could serve as a suitable solution. This includes learning about performance expectations, regulatory needs, pricing considerations, and supply-chain concerns. The results of this work will guide future product development by identifying where sugar kelp ingredients could realistically fit into the beauty market and what evidence would be needed to move forward. For the partner organization, the project offers meaningful benefits: it provides a clearer picture of customer needs, commercial opportunities, and potential early adopters in the cosmetics sector. It also outlines the next strategic steps required to bring an ocean-friendly, Canadian-sourced cosmetic ingredient closer to market readiness. Ultimately, the project supports both sustainable innovation and economic growth within Canada’s emerging blue-beauty and marine biotechnology industries.

L2M-An AI-powered interactive educational robot that engages learners at the young age through natural conversation, personalized instruction, and hands-on activities, adapting its teaching style to each child’s needs.

This project focuses on developing an innovative, smart, interactive toy robot for children aged 3 to 8. The robot combines emotional intelligence, educational content, and safety monitoring to provide a healthier alternative to mobile device usage. It serves as a companion that can recognize a child’s mood, offer adaptive learning activities, and detect environmental hazards such as fire, smoke, or toxic gases.
By integrating AI-based emotion recognition, interactive games, and parent–child communication features, this project aims to address the lack of multifunctional, development-oriented smart toys in the market. The outcome will include a validated product concept, a market analysis, and a commercialization strategy tailored to the Canadian smart toy industry. This work will support child well-being, enhance family safety, and contribute to Canada’s growing innovation ecosystem.

L2M – CO2 Loading Automated Tester

This project will explore the market needs for a new automated CO2 loading tester, a device designed to make carbon capture systems easier, safer, and more cost-effective to operate. The intern will interview potential users (e.g., engineering firms, research labs, and early adopters) to understand the challenges they face and the features they need most. The project will also evaluate different business models and identify the customer group most likely to benefit from the technology first. The outcome will be a clear commercialization roadmap that reduces uncertainty, supports informed product development, and helps bring this innovation to market more effectively.

Synthesis and Isolation of terminal carboxyl and pyridyl metal-organic cages

The objective of this project is to synthesize and isolate new heteroleptic metal-organic cages (MOCs) to function as reaction vessels for a series of different applications. The MOCs will consist of two triarylborane ligands and two triarylamine ligands for a ligand ratio of 1:1. Two separate cages will be explored with one consisting of ligands with carboxyl terminals, and the other with pyridyl terminals. These cages will provide different sizes and geometries based on the ligand to metal ratios and functionality of the varying ligand terminal groups. The MOCs will be used to explore charge-transfer fluorescence with potential in chemical sensing as well as host-guest chemistry with potential for FLP activity. Many systems involving charge-transfer and FLPs have been explored, but none of which have been developed into cage-like structures.

L2M – UV-Laser Based High-Resolution Hybrid Additive Manufacturing by Precise Material Dispensing

Our project is about a novel hybrid 3D printing system that combines the best parts of two popular printing methods: Fused Deposition Modeling (FDM) and Stereolithography (SLA). FDM printers can make large parts but with low precision and limited surface quality, while SLA printers create very accurate parts but waste a lot of resin and can only print small objects. This gap shows a clear need for a method that can produce large, high-quality parts without high material costs. We have already built and tested a proof-of-concept system that addresses this problem. It uses a custom nozzle and syringe pump to deposit small amounts of resin, which is cured by a laser in real time, reducing waste and enabling large, high-resolution printing at lower cost. The proposed project will now focus on a market assessment to understand who would benefit most from this technology and where it can provide the greatest impact. The partner organization will gain early insight into a new Canadian innovation, its possible applications, and opportunities to support future commercialization.

Predictive Processing of Disfluent Speech in Bilinguals

Language is one of the most fundamental cognitive processes, yet we know relatively little about how bilinguals learn, understand, and use language. This is a critical knowledge gap, as around 20% of Canadians grow up in bilingual homes. Furthermore, what we do know about bilingual language acquisition has focused on fluent language. Speech disfluencies (e.g., uh and um) can aid language processing in adults, as experienced listeners harness an association between disfluency and hard-to-produce words to make predictions for what a speaker will say next. Here, we investigate how age and exposure to a second language impact disfluent language processing. We emphasize the role of prediction making, hypothesizing that older children with more exposure to a target language will use speech disfluencies to anticipate what a speaker will say next. We also make a methodological contribution by testing and refining methodological tools, focusing on how eye-tracking can be used to measure prediction making. This project unites two bilingual language acquisition labs at Concordia University and the University of Wisconsin. Through this project, the two labs will be able to investigate bilingualism across a diverse set of participants and develop analytical tools that will support the ongoing work of both labs.

Enhancing Health Practitioners’ Engagement in Digital Behaviour Change Programs: The EVO+ Colombia Case

The project aims to design and test a strategy that helps healthcare workers introduce and follow up on the use of the EVO+ Colombia app, a digital program that supports people in building healthier habits related to eating and physical activity. By understanding what makes it easier or harder for health workers to recommend and monitor the app, the team will create tools and approaches that fit their daily routines. Participating institutions—both in Canada and Colombia—will benefit from stronger collaboration, new knowledge about how to support behaviour change through digital tools, and practical interventions that can improve patient care and the successful adoption of health programs within their services.