Innovative Projects Realized

Methods for Recording Soundscapes Using New Technologies

This project aims to collect high-quality sound recordings of diverse environments for subsequent analysis and annotation to support the training of artificial intelligence models. The project will equip the intern with the skills needed to complete their PhD at their home institution following the international experience. Both institutions will benefit from further relationship-building, as the hearing science faculty at Macquarie is world-renowned for its expertise in audiology and sound scene classification, thereby opening opportunities for future Western students to access this connection. Access to Macquarie’s facilities and staff will ensure high-quality data collection that benefits both schools.

Anti-colonial Urban Sustainability at a Settler-led Community Land Trust

This community-based research project, undertaken through the U of T Climate Hub Catalyst Internship, partners with the Toronto Chinatown Land Trust (TCLT)-a community-controlled effort to build an inclusive, culturally competent, and ever-evolving Chinatown in Toronto. TCLT acquires, develops, and stewards land in perpetuity for community needs and benefit. Operating with the understanding that Chinatown exists on stolen Indigenous land, through its Hak Hei program, TCLT prioritizes relationship-building and accountability with Indigenous communities and seeks to align its work and governance with the Land Back movement while actively avoiding imperialist and colonial cultural dynamics.
The project will strengthen TCLT’s capacity to integrate anti-colonial and sustainability action principles into its organizational practices through the creation of a land-based education and membership program. This initiative positions urban sustainability as inseparable from social justice and decolonization, demonstrating how community land trusts can model equitable, low-carbon, and relational forms of urban sustainability.

Teaching Sustainability Through Science: A Comparative Study of Pedagogical Approaches in China and Canada

This proposed project investigates the differences in pedagogical methods in teaching sustainability across Canada and China. This research aims to use the UN’s sustainable development goals as an outline to approach sustainability issues. Research methods will be conducted through initiating casual conversations with students and educators, following the educators guide with the Chinese curriculum and practicing the activity/lecture to really gain a perspective on the work. Research articles and peer-reviewed works will supplement this work with further insight. The objective of this project aims to benefit both countries by developing a stronger cultural bridge through the Reciprocal Learning Program where Canadian students and Chinese students visit each other’s countries to immerse themselves in the educational and cultural contexts. There they are able to witness first-hand knowledge of the language, cultural mannerisms and educational differences. Additionally it benefits the educational system of both institutions by absorbing unique teaching methods, resources and technological tools that are utilised in educating the citizens. This project serves to increase global competency in each respective country while gaining and appreciation for the cultural distinctions that make that country special.

L2M – Commercialization of Cold Plasma Technology for Sustainable Disinfection in Agri-Food Applications

Disinfection is essential across households, industries, agriculture, and healthcare. But, choosing the right chemical disinfectant raises concerns, as residues, volatile compounds, and microbial resistance often force us to switch chemicals or increase dosages. What if we could disinfect without adding chemicals? Our project explores this possibility by altering the chemistry of air and water using cold plasma technology. We’ve developed a system that activates air and water into reactive agents capable of disinfecting surfaces and environments, safely and sustainably.This innovation offers a chemical-free alternative for effective sanitation, reducing environmental impact and microbial resistance. Our aim is to bring this breakthrough to market, transforming how we think about cleanliness and safety.

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.