Innovative Projects Realized

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

30508 Completed Projects

2882
AB
5105
BC
825
MB
681
NL
860
SK
9051
ON
9491
QC
97
PE
586
NB
1141
NS

Projects by Category

Assay development for point-of-care COVID-19 antibody detection

A key requirement for returning to societal and economic normality is the evaluation and surveillanceof SARS-CoV-2 infection and immunity. The proposed technologies described herein, enable a safe and effectivediagnostic test that can quickly provide an easily interpreted result for SARS-CoV-2 immunity, all without complicated equipment, expensive reagents, or high-level biosafety containment facilities. Such a system would allow for rapid testing in decentralized labs or at point-of-care providing quick data information on antibody responses produced by infection or vaccination. This data will be invaluable for assessing the most efficacious SARS-CoV-2 vaccine(s) for use in population-wide vaccination booster campaigns against currently circulating strains, and will also guide future public health mitigation strategies to reduce the spread of viral transmission.

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Faculty Supervisor:

Dustin Little;Marc Adler

Student:

Partner:

OxiLight

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

Toronto Metropolitan University

Program:

Accelerate

Optimizing Wireless Data Communication for Implantable Medical Devices

The research project aims to develop a portable, wireless interface for implantable medical devices that monitor brain activity in patients with conditions such as epilepsy and motor disorders. This new software will allow for continuous, unrestricted measurement of brain activity, enabling doctors to collect high-quality data in patients’ natural environments. The interface will wirelessly transmit the recorded data to external storage locations when in proximity to a base station, while a proprietary time series compression algorithm will optimize data management and storage. This innovative solution has the potential to improve current neuromodulation treatments and enhance our understanding of neurological conditions.

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Faculty Supervisor:

Xilin Liu

Student:

Partner:

NerveX Neurotechnologies, Inc.

Discipline:

Engineering

Sector:

Technology; Health and Related Sciences & Technology; Advanced Manufacturing

University:

University of Toronto

Program:

Accelerate

Building a privacy-preserving federated recommender system for mobile devices

Lerna AI helps app developers to better understand their users and optimize their campaigns. The company’s mobile library optimizes the timing of user engagement, for example by identifying when the right moment is to send an up-selling notification. This is achieved by seamlessly deploying privacy-preserving federated learning on the mobile phone in order to learn from enriched first-party data that never leaves the device.
The project aims at enhancing Lerna AI’s underlying ML algorithms by introducing a recommender system and content-based optimization. This project will primarily assist Lerna AI in improving its predictive capability, i.e. more accurately identify what time and which content are more likely to better engage each individual user. By extension, the project is expected to augment the campaign conversion rates and return on investment for Lerna AI’s own clients.

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Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

Lerna

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Université de Montréal

Program:

Accelerate

Kanonhkwa’tsheranákere, Where the Medicines Are: Creating an Indigenous biocultural atlas and ethnobotanical field guide grounded in decolonial methodologies

This landmark project was built by Dr. Jessica Dolan with leadership at the Indigenous organization Plenty
Canada, and the Conservation Through Reconciliation Partnership team at University of Guelph. Working closely
in collaboration with Haudenosaunee and Anishinaabeg educators and culture-bearers, the team is combining
plant biology surveys in the Greenbelt region of Southern Ontario, with historical and contemporary linguistic
and cultural research on the ethnobotany of native plants, for food, medicine, craft and utility. The team is
creating a digital atlas of ethnobotanical surveys of the Greenbelt area, and writing a field guide to
Haudenosaunee and Anishinaabeg in Southern Ontario. The project includes significant Kanienke’ha and
Anishinaabemowin resources, to support linguistic revitalization in tandem with place-based learning. The goal
is to create rigorous work that is publicly accessible as tools for learning among youth and adults, alike, that will
aid in equitable collaborative relationships in environmental conservation and cultural revitalization.

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Faculty Supervisor:

Robin Roth

Student:

Partner:

Plenty Canada

Discipline:

Sociology

Sector:

Agriculture; Arts, entertainment and recreation; Education; Health and Related Sciences & Technology; Other services (except public administration); Professional, scientific and technical services

University:

University of Guelph

Program:

Accelerate

AI Based Script Builder for Web Payments

To conduct research and provide a feasible solution to create an AI-based script builder to automate the company’s pay-by-web transaction process. The pay-by-web transaction process includes many steps. These are steps such as extracting data from different sources and monitoring email inboxes as well as verifying payment information. The process also involves identifying the correct supplier websites and submission of the complete payment transaction. The project aims to provide an AI script that automates this process to make it more streamlined and efficient. If the project is successful, it will provide great business value to the company and its customers.

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Faculty Supervisor:

Mariano Consens

Student:

Partner:

Iteration Matrix LP

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Assessing Global Adoption of the Central Banking Digital Currency

Central Banking Digital Currency (CBDC) is a digitalized version of cash. It seeks to preserve all the properties and characteristics of the monetary unit, the most famous of which are: a store of value, a medium of exchange, and a unit of account.
Using machine learning techniques, the student will answer questions about how quickly it is possible to implement CBDC, whether it will be successful and predict the transition from a state of disinterest to the active use of this type of currency.
Identification of factors that can help in the implementation of the project, as well as the introduction and acceptance of Central Banking Digital Currency, in Canada, will be carried out. Drawing up recommendations that can be improved and considered at the state level, the project itself can contribute to the accelerated implementation of technology, as well as direct interaction with the state apparatus.

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Faculty Supervisor:

Mark Crowley

Student:

Partner:

Kyiv School of Economics

Discipline:

Engineering

Sector:

Education

University:

University of Waterloo

Program:

Globalink Research Award

Automating Insider Threat monitoring and detection

Advanced persistent threat (APT) groups, as well as those sponsored by a nation-state, often aim to gain undetected access to a network and then remain silently persistent, establish a backdoor, and steal data, as opposed to causing damage. APT groups use different tactics, techniques, and procedures (TTPs) at various stages of cyberattacks. The continuous and ongoing threats and attacks imposed by APT groups create a need for continual and collaborative assessments of defensive measures. So APTs drive the need for purple teaming. Purple teaming is a collaborative approach to cybersecurity that brings together Red and Blue teams to test and improve an organization’s security posture. By emulating adversaries’ tactics, the Red team makes the blue team better at defense. To create an optimized and continuous security workflow, the purple teaming processes must be automated. In the Red team phase, there are several tools that can be used to emulate attacks and its possible to interact with all parts of the tools through the core REST API to make the process automatic.

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Faculty Supervisor:

Ali Dehghantanha

Student:

Partner:

GlassHouse Systems

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Guelph

Program:

Accelerate

Développement du biocarbone pour des applications à haute température – QC-655

Le changement climatique représente l’un des plus grands défis auxquels nous devons faire face. Ce phénomène est causé par le réchauffement de la planète en conséquence des concentrations élevés des gaz à effet de serre (GES) dans l’atmosphère liée aux activités humaines telles que l’utilisation des combustibles fossiles dans le secteur industriel.
Les acteurs métalliques sont de plus en plus engagé à réduire leurs émissions de GES. Dans ce contexte, Elkem, un des fournisseurs mondiaux de métaux, souhaite remplacer le charbon fossile par du biocarbone, un produit obtenu par la pyrolyse de la biomasse forestière. Pour introduire le biocarbone dans le marché métallurgique comme alternative fiable et économique au charbon fossile, ses propriétés structurelles et mécaniques doivent être explorées et étudiées en profondeur. En effet, ce projet vise à étudier et optimiser les propriétés physico-chimiques du biocarbone afin d’améliorer sa réactivité dans les fournaises de ferrosilicium.

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Faculty Supervisor:

Richard Martel

Student:

Partner:

Elkem Métal Canada

Discipline:

Physics

Sector:

Manufacturing

University:

Université de Montréal

Program:

Accelerate

Robotic-based methodology for synthetic seizure dataset generation for machine learning-driven medical devices.

This research project aims to improve epilepsy treatment by developing a robotic-based method for testing wearable seizure detection devices. The project will create a robotic system that can simulate seizures, providing realistic data to help refine and test machine learning algorithms for detecting seizures more accurately. The goal is to address the limitations of current wearable devices and enhance the effectiveness of seizure detection and neurostimulation. By improving these devices, the project intends to make epilepsy treatment more accessible and enhance the quality of life for millions of people affected by this neurological condition.

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Faculty Supervisor:

Xilin Liu

Student:

Partner:

NerveX Neurotechnologies, Inc.

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Modelling of Asset Health and Risk Matrix for Rotating Assets in Sawmill

Weyerhaeuser Drayton Valley Lumber Mill has employed continuous monitoring of vibration for all the critical rotating assets in the mill to detect failures in early stage and prevent failures. Continuous monitoring is achieved through sensors mounted on the equipment and each sensor collects real-time vibration data of the equipment. Real-time vibration data give an understanding of the condition/health of the rotating asset. If the asset’s vibration goes above a certain limit (typically the limits are called as “warning” or “alarm”), then maintenance actions are initiated to prevent the failure. To reduce the overall vibration and prevent failures, Weyerhaeuser wants to better use the asset’s vibration information to represent asset health as very healthy, healthy, approaching warning, warning, and alarm. In this project, interns will work on developing an Asset Health Model to classify the asset condition/health using vibration information and build a risk matrix to prioritize maintenance activities. This will help maintenance team at Weyerhaeuser to take the appropriate actions to prevent failures, run the mill reliably, and achieve planned production.

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Faculty Supervisor:

Zhigang (Will) Tian

Student:

Partner:

Weyerhaeuser

Discipline:

Engineering

Sector:

Agriculture; Manufacturing

University:

University of Alberta

Program:

Accelerate

Using multi-modal data and self-supervised approaches for machine learning in healthcare

This research project aims to address the growing interest in predicting clinical outcomes using machine learning
(ML) approaches applied to Electronic Medical Record (EMR) data. The primary objective of this study is to
develop representations of both EMR and text data found in medical notes using current state-of-the-art ML
techniques. In particular, this research proposes to leverage self-supervised learning techniques to learn dynamic
representations. By doing so, the research aims to improve the prediction of clinical outcomes. Upon successful
completion, the machine learning models will effectively assist clinical decisions, which will benefit both the
company and the Canadian healthcare community.

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Faculty Supervisor:

Bo Wang

Student:

Partner:

Signal 1 AI

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Evaluating self-efficacy and performance outcomes in an elementary school mentoring program

This project will evaluate an elementary school mentoring program, the Learning Buddies Network (LBN), which targets challenged students in reading and math, in terms of its outcomes in respect to the mentee’s self-efficacy beliefs and performance. The intern will consult with senior staff members of the LBN organization to identify improvements and extensions to the existing program evaluation tools and he will develop additional instruments to perform the evaluation. The outcomes of the project will include an assessment of the program outcomes during a three-month period of the mentoring program implementation, evaluation of the pairing procedures between mentors and mentees and suggestions on how these outcomes may be further improved in the future, regarding mentor training, type of mentoring (face to face or remote), and methods / strategies implemented. The resulted conclusions will be the basis for further evaluation of the program in the future, iterating additional expansions, improvements, and updates.

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Faculty Supervisor:

John Nesbit

Student:

Partner:

Learning Buddies Network

Discipline:

Sociology

Sector:

Education

University:

Simon Fraser 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.