Innovative Projects Realized

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

30156 Completed Projects

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812
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673
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842
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8957
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96
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1120
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Projects by Category

Machine learning model for corrosion detection and assessment for pad mounted equipment

Corrosion of electrical utility infrastructure represents a major operational challenge that currently relies on labor-intensive manual inspections. This project aims to develop an automated machine learning system to detect and assess corrosion using publicly available street-level imagery, enabling frequent, low-cost monitoring across the distribution network. The proposed methodology will leverage recent advances in object detection and semantic segmentation. Upon successful development, the corrosion detection system will be integrated into EPCOR’s asset management workflow. Automated analysis of public imagery can provide frequent, low-cost monitoring to prioritize field inspections and maintenance activities. Reducing operational costs while extending asset life represents major potential cost savings for the utility industry.

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Faculty Supervisor:

Zhigang (Will) Tian

Student:

Partner:

EPCOR Utilities Inc.

Discipline:

Engineering

Sector:

Utilities

University:

University of Alberta

Program:

Accelerate

Optimal motion planning under kino-dynamic constraints

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

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Faculty Supervisor:

Igor Gilitschenski

Student:

Partner:

Ocado Technology

Discipline:

Computer science

Sector:

Artificial Intelligence; Advanced Manufacturing; Technology

University:

University of Toronto

Program:

Accelerate

Recommending Investment Opportunities using Reinforcement Learning

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

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Faculty Supervisor:

Silvana Pesenti;Scott Sanner

Student:

Partner:

Balyasny Asset Management (Canada) ULC

Discipline:

Computer science

Sector:

Finance and Insurance

University:

University of Toronto

Program:

Accelerate

Exploring and Applying Streaming Data Analytics in IoT Big Data Environments to Enhance Fleet Safety and Smart City Infrastructure.

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

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Faculty Supervisor:

Nick Koudas

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

Increasing emissions reporting accuracy and providing actionable reduction strategies with AI

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

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Faculty Supervisor:

Andrei Badescu

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

Identifying Causal Risk Factors for Hazardous Driving and Accident Propensity for Safer Fleets and Smart Cities

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

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Faculty Supervisor:

Andrei Badescu

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

Use of Natural Language Models to Support Assessment of Cognitive and Socio- Emotional Dynamics Among Learners in Online Active Learning Environments

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

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Faculty Supervisor:

Steve Engels

Student:

Partner:

University of Toronto Schools

Discipline:

Computer science

Sector:

Education

University:

University of Toronto

Program:

Accelerate

Explanations with Meaningful Predictive Properties via Clustered Shapley Values

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

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Faculty Supervisor:

Sheila McIlraith

Student:

Partner:

Signal 1 AI

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Structural Health Monitoring of Rebecca Street Bridge

Cole Engineering Group Ltd. is conducting rehabilitation for the Rebecca Street Bridge (William Anderson Bridge) located in the Town of Oakville. The four-lane steel girder bridge with a composite concrete deck was constructed in 1961 over the Sixteen Mile Creek. The bridge also spans over Water Street, parking lots and a rowing club storage facility. Ryerson University, along with a graduate student proposes to engage in the Structural Health Monitoring (SHM) of the Rebecca Street Bridge using the MIRA 3D Shear Wave Tomographer. The MIRA 3D shear wave Tomographer provides three-dimensional location of flaws or regions of deterioration within structural members. This project will conducted by Ryerson University and Cole Engineering to provide the Town of Oakville with a new and effective monitoring technique and management strategy that will not only be used on this bridge, but can be implemented on virtually any bridge structure.

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Faculty Supervisor:

Hesham Marzouk

Student:

Partner:

Cole Engineering

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Toronto Metropolitan University

Program:

Accelerate

Advanced MR-based biomarker development in brain movement disorders using a data-driven AI approach

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

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Faculty Supervisor:

Lueder Kahrs

Student:

Partner:

University Health Network

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology

University:

University of Toronto

Program:

Accelerate

Game On: comment développer des productions vidéoludiques à l’ère de la cancel culture? Un projet de recherche et de formation

Après le phénomène #MeToo et les tensions associées à la cancel culture (ou « culture de l’annulation »), une prise de conscience émerge dans l’industrie du jeu vidéo à l’égard des problématiques de sexisme et de discrimination liée au genre, à l’orientation sexuelle et aux sexualités sous-représentées. Les professionnel.le.s du jeu vidéo doivent se former et adapter leurs pratiques pour plaire à un marché dont les standards et les exigences en inclusivité évoluent rapidement. L’objectif global de la présente étude est d’étudier les facteurs qui conduisent les professionnel.le.s du jeu vidéo à reproduire ou à s’écarter des stéréotypes de genre et sexuels. Au total, entre 30 à 40 professionnel.le.s ont été recrutés avec l’aide des partenaires de l’étude : la Guilde du jeu vidéo et le collectif Asylum. Les retombées de l’étude contribueront à la transformation nécessaire des pratiques créatives dans une optique de diversité et d’inclusion.

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Faculty Supervisor:

Léa Séguin

Student:

Partner:

Club Sexu

Discipline:

Sociology

Sector:

Information and cultural industries

University:

Université du Québec à Montréal

Program:

Accelerate

Millimetres Matter: Deep learning-driven brainshift assessment and correction in neurosurgical MRI

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

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Faculty Supervisor:

Dehan Kong

Student:

Partner:

University Health Network

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology

University:

University of Toronto

Program:

Accelerate

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