Innovative Projects Realized

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

29670 Completed Projects

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4990
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801
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663
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825
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8841
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9197
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95
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568
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1088
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Projects by Category

The Short- and Long-Term Impacts of the COVID-19 Pandemic on Ontario Manufacturers, Manufacturing Employees, and Supply Chains

The COVID-19 pandemic has had an unprecedented impact on Canada’s economy. More specifically, it has:
1) exposed deficiencies in supply chains, causing manufacturers to pivot production in order to improve the supply of essential goods (e.g. medical devices, personal protective equipment, sanitizer),
2) demonstrated the important role that manufacturing plays in a well-functioning economy and society, and
3) brought to light the lack of comprehensive information related to Canadian manufacturers capabilities.
This project attempts to address questions related to the role of manufacturers in mitigating the impacts of the COVID-19 pandemic, better understanding the capabilities of manufacturers (especially as they relate to the production of essential goods), and ensuring that government policies and programs designed to support manufacturing during the anticipated economic recovery and during future crises are effective.

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Faculty Supervisor:

Gregory Zaric

Student:

Partner:

Trillium Network for Advanced Manufacturing

Discipline:

Business

Sector:

Information and cultural industries

University:

The University of Western Ontario

Program:

Accelerate

An Investigation of Service Mesh(es) and Security Models Within and Across Multiple Distributed Systems

Global service providers in highly regulated financial sectors must accommodate an ever-changing, sometimes competing, landscape of regulatory concerns. This project seeks to determine a reasonable path forward in technology design and adoption to accommodate current and anticipated infrastructure changes. Moreover, bridging the service layer across multiple, distinct distributed systems of varying complexity will pose new challenges while performance and observability of these systems become critical consideration. Parallel architectural patterns require multiple service integration points and the ability to negotiate the movement of data securely. The objective is to review the industry landscape of service meshes and/or cryptographic patterns for suitable accommodation of multiple system architectures. Specific targets for latency (with defined upper bounds) and parity performance for read and write rates must be met. Adjustments based on the assumption of network behaviour are expected to be addressed, including failure conditions and stale data concerns, where applicable.

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Faculty Supervisor:

Ashvin Goel

Student:

Partner:

Ethoca Technologies

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Explore efficiently automated parallel hyperparameter search for optimizing machine learning models over large scale cloud cluster

Machine learning has been applied in various fields and shown promising results in recent years. Researchers have found that tuning machine learning models in a proper way can vastly boost the model performance with respect to the specific AI task. However, tuning machine learning models at scale, especially finding the right hyperparameter values, can be difficult and time-consuming. There is therefore great appeal for automatic approaches that can optimize the hyperparameter of any given model. This project aims to provide an end to end automotive hyperparameter search framework that can help people explore better machine learning models

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Faculty Supervisor:

Gennady Pekhimenko

Student:

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

Information and Communications Technology; Technology

University:

University of Toronto

Program:

Accelerate

Road Accident Severity Detection using Telematics and Environmental Data from Connected Vehicles

Road safety affects everyone, not just Geotab customers. With several years of driving and environmental data collected from over 2 million connected vehicles, there is a great opportunity to leverage big data and machine learning to establish an accident detection system. On top of driving data and environmental data, it also contains machine diagnostic data which is hypothesized to have highly correlated features when it comes to accident detection. As such, the objective is to use this to detect the severity of an accident through a combination of ML approaches which fall under the umbrella of supervised, semi-supervised, and unsupervised learning. Based on our findings, both Geotab’s customers and the entire community will benefit from it as the end goal is to use this as a proactive measure for their clients and the respective city planners.

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Faculty Supervisor:

Marsha Chechik

Student:

Partner:

Geotab Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

University:

University of Toronto

Program:

Accelerate

Interpretable Machine Learning for Predictive Analytics in Employee Benefits Insurance

In recent years, many machine learning methods have been developed for predictive analytics and automated decision making. However, the lack of explanation resulted in both practical and ethical issues. In this project, we will employ and advance interpretable machine learning methods for various predictive analytics tasks in employee benefit insurance. The proposed methods can be used by the partner organization to improve transparency and hence trust in a wide range of applications that involve predictive analytics.

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Faculty Supervisor:

Majid Komeili

Student:

Partner:

Global IQX

Discipline:

Computer science

Sector:

Finance and Insurance

University:

Carleton University

Program:

Accelerate

Described Video and Language Detection on Audio-tracks Using Machine Learning

Bell Media receives content from different providers, including content it produces in-house. There are standards for tagging audio tracks with metadata however many facilities (including Bell) do not adhere to these standards. Currently Bell uses a manual approach to classify unlabeled audio tracks, which is inefficient, and time consuming for massive digital media that Bell has and receives. Bell is developing a single ingest pipeline to accelerate the labeling and processing of media files it receives. This research project will look at two features that Bell Media would like to include in the ingest pipeline. The first feature is the ability to automatically classify the audio track of the media file into its language type, primarily English or French. The second feature is to identify which audio track carries the described video information. In this research we will develop machine learning solutions for these two problems.

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Faculty Supervisor:

Shahram Shirani

Student:

Partner:

BCE Inc

Discipline:

Engineering

Sector:

Information and cultural industries

University:

McMaster University

Program:

Accelerate

Fall Self-Recovery Lift Assist to Reduce Exposure to COVID-19 Among Seniors in Assisted Living & Long-term Care by Enabling Independent Living

More than one in five seniors aged 65 and over will fall at least once a year, many of them repeatedly. Fortunately, most of these falls don’t result in major injuries but often the fall victims are unable to get back up on their own and require assistance from someone. For those that live alone or whose partners are unable to assist them, they need to either call for Emergency Medical Services or call on a friend or neighbour to help. Falls are one of the leading causes for admission to assisted living and long-term care facilities where we have seen the devastating impacts from COVID-19. This project seeks to design an affordable, in-home portable assistive device to allow seniors or those with mobility limitations to recover from falls themselves where there is no resulting injury and remain living independently.

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Faculty Supervisor:

Clifton Johnston;Glen Hougan

Student:

Partner:

Axtion Independence Mobility Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

Dalhousie University

Program:

Accelerate

Project 4C: Cumulus Cloud Cluster Computing

Forests are one of Canada’s greatest commodities in terms of natural resources—almost two-thirds of British Columbia alone is populated by forests, measuring 60 million hectares. Worldwide, forests continue to impact global infrastructure, economy and environment. Understanding how to monitor and track usage and health of our forests is essential for the management and longevity of this essential resource.
This project is intended to support the research and development of a pilot project for a new Global Forest Monitoring System in B.C. through a joint industry / government initiative. Data collection through use of satellite feeds and other sources will leverage secure cloud computing to layer data on a hectare by hectare basis providing varying authentication and authorization for each layer. The goal of the system is to not only collect data about BC forest in a real-time manner but to perform computation on this data in a way that is both…

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Faculty Supervisor:

Yvonne Coady

Student:

Partner:

MacDonald, Dettwiler and Associates Inc (Richmond, BC)

Discipline:

Computer science

Sector:

University:

University of Victoria

Program:

Accelerate

Investigating inclusive blood donation policy options for sexual and gender minorities

This project will investigate possible alternative blood donation screening criteria that could lead to sexual and gender minorities who are sexually active being allowed to donate blood without a mandatory waiting period since their last sexual encounter. Currently in Canada, any cisgender man who has sex with another man must wait three months since that sex before they are eligible to donate blood; this also applies to trans women who have not had lower gender affirming surgery. Community advocacy organizations such as the Community-Based Research Centre, have long argued that this type of time-based deferral policy is unnecessarily discriminatory on the grounds of sexual orientation and gender identity. This project will investigate claims that the current policy is necessary, and propose alternative policies for future safety evaluation.

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Faculty Supervisor:

Nathan Lachowsky

Student:

Partner:

Community Based Research Centre Society

Discipline:

Life Sciences

Sector:

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

University:

University of Victoria

Program:

Accelerate

Exploring and Improving Self-supervised Methods for Large-scale Video Recognition

With the advancement of modern technology, especially the increase in network speed, videos are taking more and more important places among media types. With vast potential applications, video recognition has received great attention. However, video recognition is a non-trivial task: a lot of training data are needed for complicated neural networks, but annotated data are hard to acquire. As a result, there is a growing tendency to bank on self-supervised learning approaches that can make use of unlabeled data. Some results have been made but it is still a pretty preliminary topic with a lot of room to improve. This project aims to dig into this topic, design and experiment more efficient algorithms and train on larger-scale datasets. The expected result would be an improved large-scale video recognition pretrained model that achieves competitive performance.

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Faculty Supervisor:

Animesh Garg

Student:

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

Information and Communications Technology; Technology

University:

University of Toronto

Program:

Accelerate

SOTI SNAP Widget SDK Back-end Deployment

SOTI has developed a software product called SOTI SNAP that allows anyone to create an app with no programming or technical knowledge. SOTI SNAP allows users to create apps by dragging and dropping widgets onto a canvas and connecting them together to create an app. With SOTI SNAP apps can be created in minutes and the apps created can run on both Android and iOS devices.
SOTI is interested in created a widget SDK that will allow third parties to extend the capabilities of SNAP. The SDK will allow new widgets to be created that will allow SNAP apps to connect to a wide variety of external systems, and IoT devices. SNAP widgets could then be downloaded and installed from a widget store. In this way the capabilities of SOTI SNAP could be dramatically enhanced by leveraging the capabilities of the greater community.

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Faculty Supervisor:

Eyal de Lara

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Preparation of Sentinel Wraps for Detection of Pathogens in Food Packages

The goal of this project is to develop and integrate the required technology to create food wrapping materials that can tell any untrained consumer if the food is safe to eat, without the need to use sophisticated equipment. This will be achieved by incorporating a barcode-type biosensor on an antifouling food wrap. The biosensor is capable of detecting traces of specific pathogenic strain of bacteria Escherichia coli O157:H7. Therefore, food contamination can be screened on the shelf via a simple handheld fluorescence detector device. We anticipate that the developed intelligent food packaging material will report the presence of target pathogens without the need to open the packaging. This research is undertaken in collaboration with an industry partner, Toyota Tsusho Canada Inc. (TTCI), who will ultimately validate and commercialize the final technologies and products.

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Faculty Supervisor:

Tohid Didar

Student:

Partner:

Toyota Tsusho Canada Inc

Discipline:

Engineering

Sector:

Transportation and warehousing

University:

McMaster University

Program:

Accelerate

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