Innovative Projects Realized

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

29670 Completed Projects

2811
AB
4990
BC
801
MB
663
NL
825
SK
8841
ON
9197
QC
95
PE
568
NB
1088
NS

Projects by Category

Digital Illustration & Children’s Story Creation Strategy

Simbi is working on a process innovation project to significantly improve the process by which our end users are able to use our product. In other words, the intern will be working on innovative methods to get more children’s stories into the hands of our young readers. Simbi faces challenges in effective and timely artistic development for the development of children’s stories. The intern will be a key player in creating strategy to improve the creative process from brainstorming to stories read in children’s hands.

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Faculty Supervisor:

Diyan Achjadi

Student:

Partner:

Simbi

Discipline:

Sociology

Sector:

Education; Information and cultural industries

University:

Emily Carr University of Art + Design

Program:

Business Strategy Internship

Development of operational control system for multi-robot coordination in a fleet based environment

Operating a fleet of autonomous robots introduces challenges such as reduced safety for humans working around the robots, reliability of intern communication of the robots, coordination behaviour of the robots, etc. At Fourien we have developed autonomous robots which work very well as stand alone units but there is a need of development of a fleet of the robots so that multiple robots can be deployed to perform complex tasks of material handling on a large factory floor. Through this project, we are targeting to perform research and development to fund solutions for coordinated task management among multiple robots when they work in a team.

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Faculty Supervisor:

Li Cheng

Student:

Partner:

Fourien

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Alberta

Program:

Accelerate

The Automation of System AssemblyRecognition

Traditional 3D reverse-engineering and prototyping involves 3D modeling and manual parts recognition. With the advent of 3D scanning technologies, it becomes possible to automate this workflow. This research internship aims at creating a novel mechanism to automate the process of assembly recognition, as part of a new workflow of 3D interactive prototyping. The research will be focused on technologies and algorithms suitable for volume graphics model. Research topic includes 3D shape matching, alignment and recognition. Prototypes will be built to verify and evaluate the research results.

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Faculty Supervisor:

Karan Singh

Student:

Partner:

NGRAIN

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Development of Inherently Antimicrobial Fabrics

Pleasant aromas fill our homes and enrich our lives, whether it’s by helping us relax after a long day’s work or evoking some of our fondest memories. This project will test scented fabrics prepared by Aroma Textiles Lab for their ability to inhibit the growth of the bacteria that are commonly found on our skin and on sweat-soaked fabrics. The same essential oils that give these fabrics their pleasant aroma also have significant antimicrobial activity, and so if successful this work will identify fabrics that are not only pleasing to the nose, but are free from microbes and thus stay fresher longer.

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Faculty Supervisor:

Brandon Findlay

Student:

Partner:

Aroma Textiles Lab

Discipline:

Life Sciences

Sector:

Pharmaceuticals; Life Sciences (not health)

University:

Concordia University

Program:

Accelerate

Valorisation de sous-produits de la luzerne dans le secteur des cosméceutiques

Les produits cosmétiques et de santé naturels représentent le segment dont la croissance est la plus rapide au cours des
dernières années dans le marché des produits de soin. Ces produits ont des applications variées dont l’efficacité repose sur le
choix des ingrédients actifs. La luzerne produite au Québec par le partenaire, propose un ingrédient particulièrement
intéressant pour l’industrie par sa composition chimique, mais également leurs cultures locales. Les études sur l’activité
biologique des chloroplaste de luzerne ont démontré un potentiel antioxydant important et d’autres activités biologiques
restent à découvrir. Dans le cadre de ce projet, les partenaires souhaitent donc valoriser la luzerne pour le secteur des
cosmétiques.

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Faculty Supervisor:

Lionel Ripoll;André Pichette;Jean Legault

Student:

Partner:

Innovation Virentia Inc.

Discipline:

Life Sciences

Sector:

Manufacturing

University:

Université du Québec à Chicoutimi

Program:

Accelerate

Market 360 AI: AI-enabled Discovery of Customer and Market Insights

Organizations across various industries struggle to deliver personalized offerings and expanded access for target populations. Developing sub-population profiles will help identify priority sub-populations with the greatest potential to benefit from personalized support and intervention, and inform the development, targeting and delivery of personalized offerings. The objective of this research project is to enhance Deloitte’s Market 360 AI tool across 3 dimensions. Firstly, the project aims to improve the tool’s time-series clustering methodology with the goal of delivering meaningful and interpretable results, as well as allowing for the ingestion of high-dimensional data. Secondly, the project will study different application of this tool across different markets, including but not limited to the drug discovery industry. Finally, the project aims to develop a methodology that can accurately determine the latest market size for higher-level geographic areas.

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Faculty Supervisor:

Murat Erdogdu

Student:

Partner:

Deloitte Canada

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Multimodal Geolocation and Traffic Management in Healthcare Centers

Throughout the COVID-19 pandemic, there has been an emerging need of using new advances in information and communication technologies (ICT) to improve our healthcare system to manage a massive influx of patients. Indoor geolocation techniques and optimized traffic management algorithms are substantial for serving a large number of patients in healthcare centers while respecting procedures and protocols implemented during the pandemic. In this project, we will develop a framework which combines the signal from multiple communications techniques to precisely locate the position of users in a center. We will also apply multiple artificial intelligence (AI) and machine learning (ML) techniques to improve the geolocation and filter interferences. Then, we will assess the risk of COVID-19 infection of people living in a building or a healthcare facility. The framework will be implemented on Nano Data Center (NDC) edge computing platform of Humanitas’s to perform real-time assessment. The outcome of this project will contribute significantly to reopening our society and economy in post-pandemic era.

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Faculty Supervisor:

Kim Khoa Nguyen

Student:

Partner:

Humanitas Solutions

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology; Information and cultural industries; Professional, scientific and technical services

University:

École de technologie supérieure

Program:

Accelerate

Ubiquity: Intelligent Supply Chain Management

The intern will first determine which one of the two modules in the Ubiquity product is more likely to be improved using the machine learning and deep learning methods, and then implement the proposed method or research on other methods. To improve the sensitivity of the pricing and promotion module towards the small price changes, the intern will use deep neural network to improve the existing forecasting model. For the assortment planning module, the intern will evaluate and choose various machine learning algorithms to learn the interaction between items’ inventory and forecast the demand of all assortments in the future. The benefits of both modules including the improvement of the performance of the existing demand forecasting models in the Ubiquity product.

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Faculty Supervisor:

Murat Erdogdu

Student:

Partner:

Deloitte Canada

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Classical and Quantum Metaheuristic Optimization Tools to Improve the Constrained Vehicle Routing Problem Solutions

The constrained vehicle routing problem is a typical optimization problem with many real-life applications, such as last-mile route planning for delivery services. The goal is to find the optimal routes for a set of vehicles to deliver all the packages, such that the time and cost of delivery is minimized, sometimes with additional constraints such as a loading capacity for each vehicle. In recent years, quantum computing has started to show great potential in providing a speedup to optimization solutions. This project aims to understand the current state-of-the-art classical solutions to the vehicle routing problem and explore the application of quantum computing in this field, with the hope of developing a quantum hybrid algorithm that a customer will eventually use to improve their logistics planning.

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Faculty Supervisor:

Christopher Beck

Student:

Partner:

ForeQast Technologies Limited

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Forecasting Patient Flow Pressures

At St. Michael’s Hospital (SMH), having insight into patient flow throughout the hospital is essential to resource planning and operational efficiency. When patients are admitted, discharged, or transferred in the hospital, several actions need to be taken to ensure patients receive timely care and resources do not become backlogged. Improving patient flow reduces wait times, improves operational efficiency, and ultimately improves care. However, due to reasons like the epidemic outbreak, traditional methods of controlling the flow of patients are no longer effective. In this project, Unity Health Toronto is seeking to find better way to control the patient flow, efficient by utilizing state-of-the-art machine learning models.

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Faculty Supervisor:

Igor Jurisica

Student:

Partner:

Unity Health Toronto

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology

University:

University of Toronto

Program:

Accelerate

Fabrication of Deformable Micro Mirrors

Histology studies the microscopic anatomy of biological tissues, which is critical to clinical disease management and fundamental to biological understanding. However, current approaches that rely on bright-field microscopy require extensive issue preparation prior to imaging and may thus take days. Photoacoustic remote sensing, or PARS, can dramatically reduce the histology bio-imaging time from days to minutes. PARS microscopy must be capable of 3D imaging or optical sectioning, which enables visualizing multiple layers of disease tissue without the need for lengthy physical sectioning. This in turn calls for auto-focusing or adaptive optics, to focus on different depths of the tissue (i.e. optical sectioning). In this project, we will fabricate resonant deformable micro-mirrors that can shift the focus by ~10cm as needed for high throughput PARS microscopy, with mirror actuation voltage down to 20V. Besides auto-focusing, the mirror can also correct aberrations of the optical system. Moreover, the resonant micro-mirror can avoid the high electrode count (on the order of hundreds), complex drive electronics and control software, as needed by the conventional DC/static actuation of deformable micro-mirrors.

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Faculty Supervisor:

Bo Cui

Student:

Partner:

illumiSonics Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

University of Waterloo

Program:

Accelerate

Modeling Application Performance under Multi-Instance (Multi-Stream) Execution Scenarios

Multi-stream execution is a technique in GPUs that allows multiple operations/kernels from the same program to effectively use GPUs without explicitly stating the affinity of threads to the cores. Several recent optimizations in Machine Learning (ML) algorithms leverage multi-stream execution. While performance modeling of ML applications is well studied under single-stream execution, performance models of novel ML applications under multi-stream execution is lacking. There is a pressing need to develop performance models for multi-stream execution – that would be the primary area of exploration under this co-op/internship. Specifically, we are expecting to study the state of the art (SOTA) in performance modeling for multi-stream execution and develop first principles performance models, conduct validation with silicon performance, and integrate the performance models in an internal simulator developed at AMD.

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Faculty Supervisor:

Gennady Pekhimenko

Student:

Partner:

AMD Canada

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

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