Innovative Projects Realized

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

31133 Completed Projects

2940
AB
5159
BC
837
MB
685
NL
882
SK
9292
ON
9695
QC
97
PE
601
NB
1161
NS

Projects by Category

Pondération dynamique de modèles prédictifs à court terme de la charge sur le réseau électrique du Québec

Le projet vise a developper des strategies permettant de combiner plusieurs modeles d’intelligence artificielle (IA) etudies au
sein de l’ecosysteme d’intelligence artificielle de l’equipe de prevision de la demande de !’unite prevision des apports et de la demande d’Hydro-Quebec. Ces modeles combines permettront d’obtenir de meilleures predictions a court terme de la charge
sur le reseau electrique du Quebec. Un premier outil sera developpe et valide au sein de plusieurs experiences IA temps reel, en vue de permettre de qualifier en condition reelle d’exploitation les modeles IA actuels et futurs qui iront en production, ainsi que les strategies d’entrainement et de ponderation dynamique, en plus d’orienter en parallele les axes de modernisation de la production.

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Faculty Supervisor:

Fabian Bastin

Student:

Partner:

Hydro-Quebec

Discipline:

Engineering

Sector:

Energy and Utilities; Technology; Green/Alternative Energy

University:

Université de Montréal

Program:

Accelerate

Increasing Grid Resilience using Game-theoretic Demand Side Management

Demand Side Management is a scheme that manages production, consumption and storage of energy of an aggregation of households in a neighborhood. The automated algorithms communicate between households to ensure that grid constraints are respected and households use energy optimally to maximize the use of green energy and save money. A promising tool for these control algorithms is game theory which gives mathematical guarantees for fairness and equity between households such that all participants in this scheme are treated equally while respecting their individual preferences. Game-theoretic control algorithms in the area of energy management are novel and have not been applied to real-world settings. One major hindrance of the implementation in the real world is that currently there are no safety and stability guarantees for these types of algorithm. In this project we want to develop such mathematical guarantees for a specific game-theoretic controller which is ideally suited for the Demand Side Management application.

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Faculty Supervisor:

Dominic Liao-McPherson

Student:

Partner:

ETH Zurich

Discipline:

Engineering

Sector:

Education

University:

The University of British Columbia

Program:

Globalink Research Award

Controllable and editable character performance using Implicit Neural Representation approaches

Nowadays, many of the movie characters whose performances move us on screen are at least in part digital. From superhero stunts to de-aged beloved actors and actresses, visual effects artists have to create digital characters and painstakingly reproduce performances to convince audiences. New Deep Learning (DL) technologies are emerging to help alleviate the processes. For instance, Deep Fakes have been quite successful at swapping facial performances. Other promising approaches are emerging under the large umbrella of Implicit Neural Representation (INRs) such as Neural Radiance Fields (NeRFs). We wish to explore novel ways to automate parts of the workflows involved in creating so-called Digital Doubles using NeRF-like algorithms.

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Faculty Supervisor:

David Lindell

Student:

Partner:

DNEG

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Toronto

Program:

Accelerate

Exploration of RL-based agents in the context of space robotic systems

This research will explore machine learning methods in order to devise a control scheme for robotic manipulators(Candarm3) in the context of space exploration. The objective is to develop an early prototype for an autonomous learning agent which can carry out standard control tasks without any operator supervision.
The primary machine learning methods that will be studied will revolve around deep-reinforcement learning methods, in which an agent iteratively improves its performance in a given task. This is done through simulating training exercises, where the agent is rewarded for performing well. The agent modifies its behaviour in order to maximize its expected reward in future training exercises.

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Faculty Supervisor:

Chi-Guhn Lee

Student:

Partner:

MacDonald, Dettwiler and Associates Inc (Brampton, ON)

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Développement d’outils d’évaluation, de suivi et de mesures de la maturité et de la transformation numérique au sein des PME

Videns accompagne actuellement plusieurs PME dans le secteur de l’assurance dans leur initiative de transformation numérique. Nos services d’accompagnement visent à soutenir les PME dans leurs démarches vers une transformation numérique répondant à leurs besoins et alignée à leurs objectifs stratégiques.
L’accompagnement de Videns est divisé en 4 volets : l’analyse de la situation actuelle, l’évaluation du potentiel de transformation numérique, la planification et la création d’une feuille de route détaillée, et l’accompagnement pour la mise en oeuvre des solutions identifiées. Les 4 volets ont lieu sur une durée de 4 mois et ont pour but d’uniformiser les pratiques en matière d’accompagnement des PME. L’objectif final est de contribuer à la création et à la mise en place d’outils d’évaluation et de mesure standards pour aider les PME dans leur transformation numérique.

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Faculty Supervisor:

Ryad Titah

Student:

Partner:

Videns Analytics

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

HEC Montréal

Program:

Accelerate

Mary England – Sparking the Interest of Canada’s Youth in Sustainability within Advanced Manufacturing and Entrepreneurship.

The intern will take part in a multifaceted project surrounding sustainability in industry and clean advancements in advanced manufacturing techniques within a multitude of different sectors. The intern will perform an in-depth study on Canada’s status regarding advanced manufacturing and utilize social media platforms to relay novel information to promote interest, engagement and an entrepreneurial spirit in these topics among youth. In turn, the partner organization’s (Cansbridge Fellowship) network will gain increased interest and expand, allowing for furthering of the mission to bridge Canada’s innovation knowledge, manufacturing, capital and capability to the global stage with business on an international level. The academic supervisor (Dr. Joseph McDermid) will provide guidance and expertise on advanced manufacturing and future advancements in industry. The partner organization will provide ongoing support and mentorship through the capable network of successful entrepreneurs and innovators. The intern will emphasize outreach and recruitment within content delivery for the partner organization (Cansbridge Fellowship) to promote the program and foster innovation within Canada’s youth while incorporating excellence in knowledge, leadership, mentorship and entrepreneurship for the Cansbridge Fellowship program.

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Faculty Supervisor:

Joseph McDermid

Student:

Partner:

Cansbridge Fellowship

Discipline:

Engineering

Sector:

Education; Other services (except public administration)

University:

McMaster University

Program:

Business Strategy Internship

Blunose AR Reloaded;Upgraded App with Advanced AR capabilities and more

Speed Eco has developed an interactive educational selfguided local history app for tourism and self guided tours. As the bluenose is of significance to the local history and of interest to tourists and locals, speedEco is developing the appropriate software and tools to allow for virtual reality, a 3D rendering of the boat and a more interactive tour experience.

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Faculty Supervisor:

Trishla Shah

Student:

Partner:

PiRat Ghost History Hunt

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Nova Scotia Community College

Program:

Business Strategy Internship

Alison Xia – Artificial Intelligence in Modern Hospitality

Artificial Intelligence in Modern Hospitality will analyze the effects and business applications of AI in various aspects of the hospitality industry, including but not limited to food & beverage, travel & tourism, lodging, and recreation. Specifically, it will analyze how AI fits into various consumer profiles, allowing for greater flexibility, efficiency, and profitability for companies operating in the aforementioned segments. The rise in disruptive technology has enabled AI neural networks to adapt and gain information on servicing human clients and has become a critical aspect of the services industry. This analysis will include a breakdown of current and emerging technologies, with a core focus on how hospitality disruptors use and plan to use AI in everyday operations. More specifically, the intern will analyze their application in catering to customer needs and operational workflows, focusing on how geopolitical forces drive innovation in artificial intelligence and beyond. As the Cansbridge Fellowship seeks individuals looking to disrupt and reimagine how technology is used in everyday life, this analysis is essential in reporting how artificial intelligence has and can lead to disruption in the hospitality industry.

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Faculty Supervisor:

Sandy Staples

Student:

Partner:

Cansbridge Fellowship

Discipline:

Business

Sector:

Education; Other services (except public administration)

University:

Queen's University

Program:

Business Strategy Internship

Building integrative machine learning framework for precision oncology

Traditional cancer treatments have followed a “one size fits all” approach, which limits efficacy and often results in significant side effects.
This research project aims to develop an approach to predict the impact of cancer missense mutations on the drug-protein interactions of cancer treatments. The approach will use the patient’s own genomic profile and will help to tailor cancer treatments for the patient. This will reduce side effects and costs to the patient by selecting optimal treatment options for individual patients. Using binding affinity as a measure of drug efficacy this research will follow techniques similar to prior works, with the use of graph representation learning for the drugs and targets. This project will also explore several ideas to allow for better results by considering the uniqueness of this problem, such as including information from both wild-type and the mutated protein.
The significance of this work to the partner organization (Princess Margaret Cancer Center) is improved patient care and potential improvements in efficiency/costs with the use of such an automated system.

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Faculty Supervisor:

Arvind Gupta

Student:

Partner:

University Health Network

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology

University:

University of Toronto

Program:

Accelerate

Développement et validation des méthodes de contrôle non-destructif de pièces métalliques fabriquées par fusion laser sur lit de poudre

Le but de cette recherche est d’établir un protocole de contrôle pour détecter efficacement les défauts présents dans des pièces métalliques produites par une méthode appelée “fabrication additive”. Le contrôle de qualité des pièces produites est une étape cruciale avant leurs mises en service. Différentes techniques de contrôle sont disponibles ayant chacune des avantages et des limitations en termes de précision, de capacité de détection et de coûts associés. Des défauts représentatifs des problèmes qui peuvent survenir lors de la fabrication seront introduits intentionnellement et d’une façon contrôlée dans des pièces pour évaluer les limites de différentes techniques de contrôle de qualité. Un intérêt particulier sera porter à la méthode tomographie par rayons-X, qui permet de voir l’intérieur des pièces sans les endommager se basant sur le même principe que les radiographies médicales. Ce projet permettra au partenaire industriel d’optimiser la qualité et les coûts du contrôle des pièces produites.

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Faculty Supervisor:

Vladimir Brailovski

Student:

Partner:

Pratt & Whitney Canada;Pratt & Whitney (US)

Discipline:

Engineering

Sector:

Manufacturing

University:

École de technologie supérieure

Program:

Elevate

Text-to-Image Diffusion Models for Product Image Generation

Ecomtent focuses on developing vertical-specific generative AI models for e-commerce brands, offering a self-service tool to allow customers to generate an unlimited number of high-quality images in any scenario. To this end, we leverage a textto- image model which will be trained to recontextualize any image via a simple text prompt. In particular, we seek to explore additional data-type, beyond just text-prompts and images, that the model can be trained on in order to improve the fidelity of its output. The successful completion of this project will enable Ecomtent to offer their customers a model whose output is realistically recontextualized, yet also very faithful to the original image’s details. This will provide Ecomtent a competitive edge in the content-generation industry.

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Faculty Supervisor:

Kirill Serkh;Sushant Sachdeva

Student:

Partner:

Ecomtent

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Design and develop a computer vision system to detect anomalies in the bus stop using the SCiNe device of BusPas

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

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Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

BusPas Inc.

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Université de Montréal

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.