Projets novateurs réalisés

L2M – GNSS Reflectometry Techniques for Monitoring Local Sea State

This project explores the use of GNSS reflectometry as a simple way to observe local sea conditions from the shoreline. The work focuses on understanding how signals from navigation satellites can be used to estimate changes in sea level and surface state without placing equipment in the water. During the internship we will study the behaviour of reflected GNSS signals near the coast, outline the conditions under which they carry useful information, and identify the technical requirements for future development. The expected benefit for the partner organization is a clear understanding of how this sensing approach can support safer and more reliable coastal operations.

Dopant-Defect Coupling and Recombination Efficiency in Mg- and Si-Doped GaN Nanowires

Semiconductor materials are central to modern technology, from clean energy generation and quantum computing to high-speed communication. Among them, gallium nitride (GaN) nanowires have attracted strong interest for their excellent electrical conductivity, optical performance, and thermal stability. These qualities make them ideal for energy-efficient devices such as solar cells, LEDs, and photoelectrochemical systems. However, their full potential is still limited by the complex behaviour of dopants—impurities added to control conductivity—and the structural defects that often follow.

Algorithmic Bias in Canada Internship with TELUS

Artificial Intelligence (AI) is increasingly used in Canada to help make decisions and to deliver public services, but research shows that AI systems can unintentionally reinforce social inequalities, leading to unfair or discriminatory outcomes for minority groups. Biases can appear when AI systems are trained on incomplete or skewed datasets, when development teams lack diverse perspectives, or when AI tools attempt to predict sensitive personal characteristics, for example. This project will help identify where such biases appear and how they many negatively affect minority groups. Student interns will work with TELUS, one of Canada’s leading telecommunications company, assisting TELUS’ Data and Trust Office in their work towards ensuring that the company uses data and AI responsibly. Interns will assist with researching the risks of algorithmic bias, supporting the company’s annual Responsible AI report, and helping translate academic knowledge into practice tools and recommendations for industry. The goal is to better understand how AI systems may lead to unfair outcomes and to support the development of safeguards that can make these technologies more trustworthy. This work strengthens TELUS’ responsible AI efforts while also providing students valuable practical experience applying their scholarly knowledge to industry challenges.

Move IT YVR Inc. – Operational Workflow Innovation & Performance Intelligence Enhancement

MOVE IT. YVR is a premium relocation company specializing in high-touch, white-glove moving services for luxury residential clients, developers, and corporate partners. As the organization continues to grow rapidly, its core operational challenge is the need to transition from a traditional, labour-intensive service workflow to a more data-enabled, systematized, and scalable operations model. The company currently manages complex scheduling, dispatch coordination, crew performance oversight, and elevated client-experience expectations; however, these processes rely heavily on manual decision-making, individual supervisor judgment, and legacy workflows that limit efficiency, predictability, and long-term scalability. The innovation challenge lies in designing a more integrated operational system that leverages process-improvement methodologies, digital tracking tools, KPI-driven workforce oversight, and a standardized service-quality model that aligns with luxury-tier expectations. This project enables MOVE IT. YVR to go beyond day-to-day operations by exploring, mapping, and redesigning core processes, such as dispatch optimization, workforce accountability, client-experience workflows, and performance analytics, into a modernized operational blueprint that supports growth, reduces inefficiencies, and elevates service quality. Solving this challenge requires expertise in operations analysis, data interpretation, workflow systems, logistics coordination, and service-design thinking. Through the intern’s applied research and operational audit, MOVE IT. YVR will gain new tools, insights, and implementation strategies that support long-term innovation, rather than short-term operational output.

AI-driven Spatiotemporal Analysis of Human–Nature Interaction and Cultural Ecosystem Services at the Global Scale

This project aims to use artificial intelligence to study how people interact with nature in cities and natural areas around the world. By analyzing millions of social media photos and captions, the research will identify different types of human activities, emotions, and cultural values connected to nature. The project combines computer vision, natural language processing, and geographic analysis to understand global patterns of environmental engagement. It will benefit the University of British Columbia and the Massachusetts Institute of Technology by strengthening their collaboration, sharing advanced research methods, and producing a global dataset and visualization tools that can guide sustainable landscape design, environmental planning, and public policy.

Supporting Review of Topic-Based Code Changes

La revue de code est essentielle pour assurer la qualité logicielle, et une revue efficace nécessite de comprendre à la fois la modification individuelle et le contexte plus large dans lequel elle s’inscrit. Dans de nombreux projets, y compris ceux utilisant Gerrit, les développeurs regroupent les modifications liées en un « topic » qui représente une fonctionnalité complète répartie sur plusieurs pull requests.

Lorsque ces modifications liées sont examinées de manière isolée, les outils d’IA produisent souvent des commentaires incorrects ou répétitifs, car ils ne peuvent pas voir comment les différentes parties s’articulent. Par exemple, une fonctionnalité de mode sombre peut être répartie sur plusieurs changements concernant les couleurs, les composants d’interface et la logique, et elle ne prend tout son sens que lorsqu’elle est vue comme un topic.

Ce projet vise à développer des modèles d’IA pour la revue de code qui soient « topic-aware », capables d’analyser des modifications liées comme un ensemble cohérent. Nous créerons TopicReviewBench, un jeu de données composé de topics réels, et nous évaluerons plusieurs grands modèles de langage, tels que GPT-4, Claude et Llama, en testant différentes manières de leur présenter le contexte inter-modifications.

Procédés de microfabrication pour la polarisation de guides d’ondes dans les isolateurs

AEPONYX Inc. a développé une technologie innovante de guides d’ondes dans les isolateurs, révolutionnant l’intégration des isolateurs aux circuits photoniques et quantiques haute performance. L’intérêt du marché pour cette technologie est manifeste, mais la capacité de production demeure limitée en raison du caractère expérimental du procédé actuel. Le projet général vise à concevoir un procédé manufacturable et l’infrastructure associée afin de répondre aux besoins du marché.
La stratégie repose sur la technologie de reconstitution de tranches par moulage (FOWLP), qui permettra une fabrication industrielle robuste et à grande échelle. Cette approche consiste à regrouper des dispositifs discrets sous forme de tranches pour leur appliquer des étapes de microfabrication et d’intégration dans des équipements automatisés de fonderie. En combinant les guides d’ondes isolants et la reconstitution FOWLP, nous pourrons adresser des marchés à fort volume avec une technologie entièrement « made in Canada ».

L2M – TruckBud Commercialization Strategy

The project will focus on applying structured market research methods to better understand the commercial potential of my venture idea. I will interview potential customers, analyze market insights, test assumptions, and evaluate different business model options. This work will help me clarify the problem I am solving, identify real customer needs, and determine whether the opportunity is strong enough to move forward. The expected benefit to Lab2Market is that this research directly supports the goals of the program by helping a participant develop evidence-based insights, improve the quality of their commercialization work, and increase the chances of building a viable, scalable venture.

CLIQMedia – AI-Enabled Digital Marketing Insights Framework for Predictive Campaign Innovation

CLIQMedia is a Vancouver-based digital marketing agency specializing in web design, development, branding, and advertising for clients in construction, real estate, design, and dental industries. As the company continues to scale, it faces an innovation challenge around how to generate deeper, data-driven insights that meaningfully improve campaign outcomes in industries that are rapidly digitizing but historically slow to adopt advanced marketing technologies. Currently, the agency relies on manual research, traditional competitive analysis, and platform-native analytics, which provide only surface-level insights and limit the ability to anticipate market trends or design highly targeted campaigns. The improvement priority is to build a more sophisticated, predictive, and automated system for gathering, analyzing, and applying digital marketing intelligence—something that goes far beyond day-to-day campaign execution. This project proposes to explore next-generation marketing technologies, including AI-enabled trend analysis, competitor intelligence mapping, and automated insights dashboards, to help CLIQMedia redesign how strategic decisions are made for clients. Solving this problem requires expertise in marketing strategy, digital research, analytics interpretation, and the ability to translate complex findings into implementable recommendations. The intern will support the exploration and design of a new insights framework that modernizes CLIQMedia’s processes, enhances client ROI, and creates a scalable model for future digital marketing innovation across the agency.

L2M Voltage Meter

The Voltage meter is a first of its kind device created with the intention of bridging the gap between the symptomatology we understand from our frail patients’ (those who score 0-1 on the Frailty Index) experience and a numerical way to quantify rehabilitative progress or decline in the clinical environment. We know that there is a relationship between changes in voltage and an increased level of frailty as we age, but until now we have not been able to create a device to monitor this change in health status. From my first hand experience as a Preliminary Neurosurgeon, I have seen this gap in both my own clinical practice and the research settings around me. The goal of the voltage meter is to quantify numerically the patients’ specific nerve amplitude to better understand their clinical status, rehabilitation developments or clinical decline. The hope is to implement this into both care settings and rehabilitative environments as an ‘easy to use tool’ to assess the patient’s level of frailty, functional capacity and specific voltage changes over time.

L2M – WindChat Intelligence: Conversational AI for Wind & Hydrogen Site Screening

The wind energy industry currently relies on a fragmented workflow involving multiple disconnected and expensive software tools for site assessment, energy calculations, and reporting. This inefficiency limits the number of sites developers can evaluate and creates a high barrier to entry for smaller developers and community projects.

This project, titled WindChat, focuses on validating a conversational AI tool designed to streamline preliminary screening for wind farms and green hydrogen projects. WindChat aims to allow users to describe a potential site in natural language and receive transparent, auditable estimates of wind energy and hydrogen production, along with simple techno-economic indicators.

Through the Lab2Market Validate program, the intern will bridge the gap between academic research in wind resource assessment and commercial viability. The primary objective is to conduct rigorous customer discovery to determine whether wind professionals trust an AI interface for decision-making and under what conditions. Key activities include building a professional demonstration environment using real Canadian wind site scenarios, developing a technical documentation package to ensure transparency and trust in the tool’s calculations, and testing various pricing models with potential customers.

The project involves conducting customer interviews to gather evidence on willingness-to-pay and feature priorities. By the end of the internship, the intern will deliver a validated Business Model Canvas, a functional demo platform, and a go-to-market roadmap. This initiative supports Canada’s transition to a net-zero economy by potentially lowering the cost and complexity of deploying renewable energy infrastructure, particularly for smaller and emerging wind and hydrogen developers.

Graph Neural Networks for Wind Power Modelling

This project aims to develop new machine learning models that can help wind energy companies design wind farms more efficiently and at lower cost. Today, planning a wind farm requires running large, high-resolution computer simulations to understand how wind flows around turbines and how much power a proposed layout can produce. These simulations are accurate but extremely slow and financially expensive, especially when companies need to test a large number of different layouts or new locations to optimize the power production and transmission. Our research will create advanced prediction models that learn directly from existing wind flow simulations and real atmospheric data. By using graph-based neural networks and advanced generative methods, our models can adapt to different turbine layouts and geographic domains, while also estimating uncertainty in the predicted wind and power output. This makes them more flexible and scalable than current tools. For Veer Renewables, the partner organization, this project will produce a new commercial product that can deliver fast, early-stage assessments for new wind farm layouts and new geographic domains, reducing reliance on costly physical simulations.