Innovative Projects Realized

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

30508 Completed Projects

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

Projects by Category

Investigation of the impact of global supply chain disruptions on international trade

This research project aims to study how natural disasters, political events, and other factors can disrupt global trade and supply chains, and how this affects the economy. The study will focus on the impact of the war in Ukraine and natural disasters in Japan. By identifying the products and industries most affected, the intern hopes to help policymakers and businesses develop strategies to mitigate the negative effects of supply chain disruptions on international trade. Intern will also analyze how the disruptions affected trade flows between Ukraine and neighboring countries, as well as global trade flows, and evaluate the impact on the global economy, including GDP growth and employment. The intern will use advanced research methods including statistical analysis to provide new insights into the complex dynamics of global trade and supply chain disruptions, ultimately contributing to more effective risk management strategies and more resilient global trade networks.

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Faculty Supervisor:

Anindya Sen

Student:

Partner:

Precarpathian National University

Discipline:

Sociology

Sector:

Public Service, Policy, and Governance; Commercial Services; Finance and Insurance

University:

University of Waterloo

Program:

Globalink Research Award

Frost removal in the presence of an electric field on a fin and tube evaporator

High voltage electrodes will be used to remove the frost from the front part of the fin and tube evaporator inside the case of the refrigeration system provided by the company. Over the period of a few hours the frost buildup can become quite substantial on the fin leading edge. It is expected that the electrostatic forces will affect the way that frost forms on the surface and the efficiency of the evaporator can be increased. This defrosting system leads to better performance of the refrigeration system and increasing the time between the traditional defrosting cycles. The anti-frosting performance of this new defrosting system will be tested in a commercial refrigerator.

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Faculty Supervisor:

Kazimierz Adamiak

Student:

Partner:

Cayuga Displays Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

Western University

Program:

Accelerate

Développement d’une méthode non-supervisée d’apprentissage profond pour la détection de défaillance à partir de signaux acoustiques et vibratoires

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

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Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

Institut de Recherche Hydro-Québec

Discipline:

Computer science

Sector:

Professional, scientific and technical services; Utilities

University:

Université de Montréal

Program:

Accelerate

Wicking and reinforcing behaviour of a novel wicking nonwoven geotextile – geogrid composite

Excess water in the road base can lead to damage to roads from several mechanisms including decreasing stiffness of road base, freeze-thaw cycles, and swelling/shrinking of subgrades in expansive soils. Reducing the time that a pavement system is saturated is known to increase the lifespan of roads. Recent innovative geosynthetic products can further remove water from base materials due to suction or “wicking”. A novel geosynthetic product has been developed consisting of a wicking nonwoven geotextile bonded with a geogrid. The objective of this research is to test and quantify its wicking abilities and to determine its ability to improve performance of pavements with expansive subgrades. It is anticipated that this novel wicking geotextile – geogrid composite will improve drainage in unsaturated soil and limit differential subgrade swelling in expansive soil. The outcomes can increase adoption of wicking geosynthetics in Canada and increase lifespan of roads.

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Faculty Supervisor:

Jamie Bartz;Marolo Alfaro

Student:

Partner:

Titan Environmental Containment

Discipline:

Engineering

Sector:

Construction and infrastructure; Manufacturing; Professional, scientific and technical services

University:

University of Manitoba

Program:

Accelerate

DNS tunneling detection method based on ML & DL models

Domain Name System (DNS) tunnels as a covert communication channel between a controlled host and a master
server can be utilized by malicious attackers disguising the master server as an authoritative domain name server.
DNS tunneling can cause significant harm due to its ability to easily evade network security mechanisms by using
DNS traffic, so it is crucial to detect the malicious domain in advance. In this research, the performance of the
machine learning and deep learning models with existing detection methods are compared to determine their
effectiveness in detecting DNS tunneling activity, and optimize the models by tuning hyperparameters, adjusting
the architecture of the models, or combining multiple models to achieve better performance in the real-time
prediction.

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Faculty Supervisor:

Murat Erdogdu

Student:

Partner:

BlueCat Networks

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Real-time DNS tunneling detection using Machine Learning & Deep Learning techniques

Domain Name System (DNS) tunneling is a malicious technique that enables attackers to bypass network security
measures and steal sensitive information. Traditional detection methods that rely on signature-based approaches
are often ineffective against advanced attacks. In light of this, recent years have seen a growing interest in the
use of deep learning techniques for network intrusion detection. This research aims to explore the feasibility of
using deep learning algorithms for the detection of DNS tunneling in real-time network traffic. The study will involve
the analysis of a large dataset of DNS traffic to develop and evaluate a deep learning-based model. The model’s
performance will be compared against existing detection methods, and the outcomes of this research will
contribute to improving the effectiveness of DNS tunneling detection, enhancing network security overall.

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Faculty Supervisor:

Murat Erdogdu

Student:

Partner:

BlueCat Networks

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

The development of predictive capabilities in terms of pH and solubility for complex mixtures of organic acids and organic acid salts

This project will require a combination of theoretical and empirical modeling based on extensive experimentation to develop a predictable solubility model in organic acids and salts mixture systems. The successful completion of the project will allow the company to predict capability of the complex mixtures during products development. It can be used by formulators & scientists to develop desired new products in food and beverage industry.

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Faculty Supervisor:

Fei Geng

Student:

Partner:

Bartek

Discipline:

Engineering

Sector:

Manufacturing

University:

McMaster University

Program:

Accelerate

Cylinder Detection on Point Cloud data

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

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Faculty Supervisor:

Carlton Davis;Terry Peckham

Student:

Partner:

Antea Canada Inc.

Discipline:

Physics

Sector:

Mining; Professional, scientific and technical services

University:

Collège Dawson

Program:

Accelerate

Feature Search using Automatic Machine Learning

This research project focuses on developing an automated system to search and analyze time-series tabular features in the financial institution’s machine learning pipeline. The goal is to identify relevant features and improve efficiency in the decision-making process. The project will begin by prototyping a system to support automated feature search patterns and researching feature search approaches. The system will be tested and deployed in a use case, with appropriate governance for production systems. Successful completion of the project will contribute to the feature search automation of the machine learning pipeline at the financial institution.

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Faculty Supervisor:

Gennady Pekhimenko

Student:

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

Technology; Finance and Insurance; Artificial Intelligence

University:

University of Toronto

Program:

Accelerate

The effects of various dairy products on the gut microbiota

Probiotic bacteria may be the reason why dairy products are good for you. Some dairy foods may change how the gut microbiota
is made up, which can help with weight control and metabolic health. But it’s not clear how dairy products could fix the bad effects
of a high-fat, low-carbohydrate diet on lipid and glucose metabolism by changing the GI microbiota. So, the goal is twofold: 1) to
find out how the different types of dairy affect the gut microbiota in a mouse model of a high-fat, high-sugar diet; and 2) to find out
how gut microbes change the way the body uses energy.

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Faculty Supervisor:

Catherine Chan

Student:

Partner:

Helmholtz Centre Munich

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Agriculture and Food

University:

University of Alberta

Program:

Globalink Research Award

Development of a virtual active learning environment: Making use of digital knowledge objects, data visualizations, and smart assessments to engage students in collaborative deeper learning in online teaching contexts

This project will be focus on developing a digital learning platform that is: grounded in the science of learning research; informed by established pedagogical approaches for supporting collaborative learning; responsive to principles of equity and inclusion; and based on principles of effective assessment to provide high-quality online and hybrid delivery modes of distance learning education for students.

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Faculty Supervisor:

Fanny Chevalier

Student:

Partner:

University of Toronto Schools

Discipline:

Computer science

Sector:

Education

University:

University of Toronto

Program:

Accelerate

Automatic Machine Learning for Recommender Systems

This project aims to improve recommendation systems by using advanced computer techniques called Auto
Machine Learning and Meta Machine Learning. This involves automating parts of the machine learning process,
like finding similar data and picking the best settings for the computer model. This project also aims to make it
easier for others to set up these systems by automating significant portions of the work, like deciding which
features to use. Current research methods will be analyzed and evaluated to find the best methods. Overall, the
goal is to create smarter recommender systems that can learn on their own with less human help, while also
making the recommendations more accurate and helpful for customers.

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Faculty Supervisor:

Eldan Cohen

Student:

Partner:

Crossing Minds Canada Inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

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.