Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

31132 Completed Projects

2940
AB
5159
BC
837
MB
685
NL
882
SK
9291
ON
9695
QC
97
PE
601
NB
1161
NS

Projects by Category

Business Intern working within cross-functional teams to develop and commercialize AI-powered solutions in the Public Services sector (1)

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

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Faculty Supervisor:

Bertrand Malsch

Student:

Partner:

AltaML

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Queen's University

Program:

Business Strategy Internship

Access to Spirometry in Nova Scotia

test it is difficult to get a correct diagnosis of lung disease, and most importantly to get started
on the right treatments. In this study we will 1) describe the quality of breathing tests done in
the community; and 2) compare review and interpretation of breathing tests by a doctor to an
artificial intelligence algorithm available in the testing software. Nova Scotia Health, has been
conducting a pilot study in the community and will work with the intern to provide the data, review
preliminary results, and inform the interpretation and dissemination of the findings to key
stakeholders. Through this partnership the results of the pilot will be made available much faster and
will help to inform the next stage of the project. The evidence generated from this study will
inform Nova Scotia Health, and the Department of Health and Wellness whether community testing
can be done in a way that is comparable to the hospital setting, and whether innovative
algorithms embedded in commercially available software can be used safely to inform
diagnosis of respiratory conditions.

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Faculty Supervisor:

Sanja Stanojevic

Student:

Partner:

Nova Scotia Health

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Professional, scientific and technical services; Public administration

University:

Dalhousie University

Program:

Accelerate

Promutuel : Validation de la qualité des informations de souscription en assurance de dommages

Pour émettre une police d’assurance auto ou habitation, les agents d’assurance de Promutuel doivent récolter des informations sur les clients et les biens à assurer. La qualité de ces informations est primordiale pour déterminer l’admissibilité des risques, pour les tarifer adéquatement et pour s’assurer de répondre de manière adéquate aux besoins des assurés.
Pour ce faire, les agents de Promutuel doivent respecter les procédures établies, poser les bonnes questions aux clients et, dans certains cas plus complexes, référer les risques à des analystes de niveau supérieur. L’assureur valide la qualité du travail effectué par ses agents tous les deux ans. Promutuel souhaiterait créer un système d’intelligence artificielle pour suivre en continu l’évolution des indicateurs de qualité. Ce système permettrait de réagir plus rapidement aux changements et d’optimiser les processus actuels en ciblant les questions, polices, agents et mutuelles à valider en priorité.

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Faculty Supervisor:

Christian Gagné;Thierry Duchesne

Student:

Partner:

Promutuel Assurance

Discipline:

Computer science

Sector:

Finance and Insurance

University:

Université Laval

Program:

Accelerate

CMQuébec : Segmentation sémantique d’infrastructures sur images aériennes

La Communauté Métropolitaine de Québec (CMQuébec) a pour mission de coordonner le développement de la région
métropolitaine en matière d’aménagement du territoire, d’environnement, de transport et mobilité, d’économie, et de gestion
des risques naturels. La CMQuébec fournit également des services de production de données géospatiales de haute qualité
générées par l’intelligence artificielle (segmentation, classification, etc.) à ses cinq municipalités membres : la Ville de Québec,
la Ville de Lévis, la MRC de La Jacques-Cartier, la MRC de La Côte-de-Beaupré, et l’Île d’Orléans.
Afin d’aider ses municipalités membres à bonifier leurs inventaires d’actifs, et pour assurer la cohérence des données
géospatiales et des analyses à l’échelle métropolitaine, la CMQuébec souhaite ajouter à son portefeuille un modèle de
segmentation sémantique supplémentaire basé sur des photographies aériennes. Ce modèle permettra de segmenter
plusieurs infrastructures urbaines telles que les trottoirs, routes, pistes cyclables, et stationnements. Les données segmentées
pourront être partagées et rendues accessibles publiquement sur le site web de DonnéesQuébec. Finalement, un inventaire
précis et à jour de ces actifs est essentiel pour faciliter la planification urbaine, la maintenance des infrastructures, et pour
alimenter les analyses et indicateurs métropolitains.

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Faculty Supervisor:

Christian Gagné;Christian Larouche

Student:

Partner:

Communauté Métropolitaine de Québec

Discipline:

Computer science

Sector:

Information and Communications Technology; Artificial Intelligence

University:

Université Laval

Program:

Accelerate

Technical Business interns working within cross-functional teams to develop and commercialize AI-powered product solutions (1)

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

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Faculty Supervisor:

Norah McRae

Student:

Partner:

AltaML

Discipline:

Business

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Waterloo

Program:

Business Strategy Internship

Creative Funding Opportunities for Cultural Space Projects: Lessons for Toronto

This research project investigates public and private investment ideas and strategies for local culture and heritage projects, with an emphasis on lessons for Toronto, Canada. The study will identify emerging trends and developments in the areas of urban planning, heritage preservation, cultural investment and local governance. The research will be based on current thinking from academic literature, public policy, professional expertise, and first-hand accounts of those working in the philanthropic and charitable giving sectors. The intern will be responsible for producing the final report: a 20-25 page white paper consisting of an executive summary, description of methodology, key findings, application of research, and policy implications sections. Ultimately, the research will inform ERA’s existing planning and policy work on culture and heritage infrastructure in Toronto. ERA is currently working with a local parks’ group, (FOAG), to identify innovative public and private investment opportunities for culture, heritage, and community initiatives. The final report will provide a roadmap for local stakeholders (e.g., community and interest groups) and city officials (e.g., city staff and planners) to access best practices for financing and delivering local culture and heritage projects.

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Faculty Supervisor:

Deborah Leslie

Student:

Partner:

ERA Architects Inc

Discipline:

Sociology

Sector:

Construction and infrastructure; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

XEOS Imagerie : Cartographie par IA des ponceaux et des fossés en 3D

XEOS Imagerie a développé une grande expertise en cartographie par intelligence artificielle notamment à partir
de nuages de points lidar en 3 dimensions. Ce projet de recherche vise à cartographier automatiquement par
intelligence artificielle les fossés et les ponceaux à partir de nuages de points lidar aériens. Toutes les méthodes
existantes utilisent un modèle numérique de terrain. XEOS Imagerie a pour objectif de mettre en ?uvre une
méthodologie innovante combinant la segmentation sémantique et la segmentation d?instances afin d?identifier
les ponceaux et les fossés directement dans les nuages de points LiDAR, sans recourir à la rasterisation.

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Faculty Supervisor:

Christian Gagné;Éric Guilbert

Student:

Partner:

XEOS Imagerie

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université Laval

Program:

Accelerate

Improved Error Bounds for Trotter-based Hamiltonian Simulation

“THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW”

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Faculty Supervisor:

Nathan Wiebe

Student:

Partner:

Xanadu

Discipline:

Physics

Sector:

Information and cultural industries; Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Linear driver PAM4 optical transceivers

Axonal Networks and Concordia University are working together to develop low-power, high-throughput optical transceivers using linear drive techniques. This technique avoids extensive in-transceiver digital signal processing and clock/data recovery in favour of modest equalization, relying on the linearity of the channel and the equalization capabilities of the host chips at the ends of the optical links. The supported intern will develop an extensive link model and oversee the develop of a transceiver prototype.

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Faculty Supervisor:

Glenn Cowan

Student:

Partner:

Axonal Networks

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

Concordia University

Program:

Accelerate

Defining the sphere of influence: the distribution of non-vent megafauna around active hydrothermal vents

Interest in mining deposits formed at deep-sea hydrothermal vents for minerals that are required for the transition to clean energy technologies has increased. However, animal communities at hydrothermal vents and the surrounding deep-sea have not been well characterized. Many of the animals at hydrothermal vent sites, such as deep-water corals and sponges, are indicators of Vulnerable Marine Ecosystems and require management interventions for their protection. The proposed research aims to better characterize biological communities on inactive deposits surrounding active hydrothermal vents and to determine factors that may be regulating their occurrence. The results of this research will be disseminated by Oceans North as materials relevant to marine management and policy makers, contributing to a comprehensive management strategy that does not currently exist for hydrothermal vent ecosystems.

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Faculty Supervisor:

Anna Metaxas

Student:

Partner:

Oceans North

Discipline:

Life Sciences

Sector:

Other services (except public administration); Professional, scientific and technical services

University:

Dalhousie University

Program:

Accelerate

Control of a Self-Organizing Network of Quadrotor Vehicles Interacting with Real-Time Acoustic Signals

Networks are pervasive in our world. From migrating birds flying in formation, to social groups, wireless networks, world-wide web, the examples are endless. Migrating birds, such as the Canada goose, are a good example of a network between agents where information is communicated based on real-time musical (acoustic) signals. Inspired by this example, the research problem being addressed in this proposal is the real-time autonomous response of a network of quadrotors to acoustic inputs in a choreographic performance. This is an open and challenging problem because the control algorithm must react in real-time to musical inputs that are unknown a-priori. This work will be done in collaboration with Pleiades Robotics. Pleiades is a Canadian company that has recently announced plans to release a new design for a quadrotor robot called Spiri.

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Faculty Supervisor:

Luis Rodrigues

Student:

Partner:

Pleiades Robotics;Echoer Canada Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

Concordia University

Program:

Accelerate

Answer Quality Assessment for Retrieval-Enhanced Generation via Conformal Relevance and Factuality

This project focuses on improving the reliability of AI systems that generate answers using external databases, known as Retrieval Augmented Generation (RAG) systems. While these systems help reduce inaccuracies, it’s still hard for users to know how trustworthy or relevant the answers are. To address this, the intern will explore a new method that evaluates the quality of RAG-generated answers by providing clear confidence scores and conformal prediction set without needing predefined answers for comparison. The approach will use a technique called conformal prediction to ensure reliability across various aspects like factual accuracy and relevance. The research will involve reviewing existing methods, identifying gaps, developing new theoretical approaches, and testing them on real-world datasets. For the partner organization, this project offers a scalable framework to evaluate and improve the trustworthiness of AI-generated content. The outcome can help enhance user trust in AI systems, especially in critical applications like customer support, financial, and healthcare, where reliable information is essential.

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Faculty Supervisor:

Ga Wu

Student:

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

Finance and Insurance; Professional, scientific and technical services

University:

Dalhousie University

Program:

Accelerate

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Mitacs — Return on Innovation

Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.