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

1991 Soviet Coup Attempt – Part 2

The intern will investigate the 1991 Soviet coup attempt, a critical event in history when some powerful Soviet leaders tried to take control from then-President Mikhail Gorbachev. They will study why it happened, who was involved, and what the consequences were for the Soviet Union and the world. By analyzing historical documents, interviews, and other materials, the intern will gain a deeper understanding of the events and motivations behind the coup attempt. The expected outcomes of this research include: understanding the political context, the key players involved, the timeline of events, and the aftermath and consequences of the coup.

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Faculty Supervisor:

Seva Gunitsky

Student:

Partner:

Taras Shevchenko National University of Kyiv

Discipline:

Sociology

Sector:

Public Service, Policy, and Governance; Information and Communications Technology; Other

University:

University of Toronto

Program:

Globalink Research Award

1991 Soviet Coup Attempt – Part 1

The proposed research project aims to analyze the causes and consequences of the Soviet Coup attempt, including the political and economic factors that led to the coup, the role of the Soviet military and the KGB, and the impact of the coup on the collapse of the Soviet Union. The expected outcomes of the research project are to provide a comprehensive understanding of the events that led to the coup attempt, the factors that contributed to its failure, and the implications of the coup for the Soviet Union and the world. The research findings can contribute to the scholarly literature on Soviet history and politics and inform policymakers and the general public about the risks and challenges of political transitions and reforms.

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Faculty Supervisor:

Seva Gunitsky

Student:

Partner:

Lviv Polytechnic National University

Discipline:

Sociology

Sector:

Public Service, Policy, and Governance; Information and Communications Technology; Other

University:

University of Toronto

Program:

Globalink Research Award

Driver Behaviour Analysis Using Accelerometers on Android Devices

The objective of this project is to develop a software solution that can analyze the accelerometer data on android devices and report certain metrics of a moving vehicle related to road safety and driving conditions. The reports can then be used for immediate call for action in case of detecting an emergency or collecting the statistical data over a longer time period for assessing the general driving behavior. Advanced AI tools can be trained to immediately detect dangerous or harmful situations or patterns by monitoring the data on the device and reporting anomalies or sending alerts to the central control entity. In addition to data science skills, this project requires a good understanding of physics and dynamics of vehicles in driving conditions as well as a strong background in mobile device hardware and software engineering.

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Faculty Supervisor:

Arvind Gupta;Huaxiong Huang

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

CIUSSS-MTL : Apprentissage supervisé en radiologie du thorax

L’Intelligence Artificielle est utilisée dans le domaine médical afin d’offrir d’une part une aide à la décision à un personnel médical généralement surchargé et d’autre part une meilleure personnalisation des soins à partir des données relatives à une population.
Il est nécessaire de vérifier a posteriori, la bonne généralisation du modèle avec des ensembles de données dédiées à la validation et distinctes des ensembles de données utilisés pendant l’apprentissage.
Le projet de recherche est une preuve de concept dont les buts sont de valider :
1. L’utilisation des concepts de l’Intelligence Artificielle dans des applications médicales ;
2. L’utilisation d’une architecture de type CNN pour la détection automatique des anomalies dans des images radiologiques du thorax ;
3. La capacité d’augmenter le nombre de classes en utilisant le transfert de connaissances ;
4. Les résultats à partir d’une connaissance a priori des pathologies présentes dans l’ensemble de validation et de test (comparer ces résultats à ceux obtenus par le groupe ML de Stanford)
La validation du concept si elle parvient à être démontrée ouvrira la voie à un projet de développement.

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Faculty Supervisor:

Christian Gagné;Flavie Lavoie-Cardinal

Student:

Partner:

Centre intégré universitaire de santé et de services sociaux du Centre-Sud-de-l’Île-de-Montréal

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology

University:

Université Laval

Program:

Accelerate

Data Communication Optimization between Mobile Devices and Servers

The proposed research project aims to improve the way data is transmitted between mobile devices and company servers. This will improve the speed and security of file transfers, data synchronization, application deployment, and distant management of mobile devices. The intern will collaborate with experts from the partner organization who will offer guidance and support in this research to identify new techniques that can be used to reduce the amount of data being transmitted, while also ensuring that the data is secure. The resulting improved communications layer will be integrated into the partner’s products, providing better service to their customers.

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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

Development of a distributed framework for deep learning models

Layer 6 powers the AI use cases for a variety of banking and financial applications at TD Bank. The goal of the research project is to improve the AI engine by having the training more efficient and distributed among a variety of clusters. The AI engine will allow models to be trained faster and with more optimal performance of the models. An improved AI engine can help deliver better machine learning models to over 25 million customers that rely on TD Bank for their financial decisions. As a whole, millions of Canadians who use TD Bank will benefit from their banking decisions being more accurate and up to date.

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Faculty Supervisor:

Scott Sanner

Student:

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

Other; Finance and Insurance; Artificial Intelligence

University:

University of Toronto

Program:

Accelerate

Development of a system to transform audio and video feeds of medical consultations into structured notes and summaries

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

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Faculty Supervisor:

Dhanya Sridhar

Student:

Partner:

Dialogue Technologies Inc.

Discipline:

Computer science

Sector:

Artificial Intelligence; Health and Related Sciences & Technology

University:

Université de Montréal

Program:

Accelerate

Edge-Cloud Video Streaming Pipeline for Video Action Recognition

Streaming Cameras have become ubiquitous in the urban and industrial landscape. This research project aims to improve the AI-based action recognition capability of consumer-class home camera streams, which often have limited bandwidth and degraded video quality. The project proposes to develop a network-aware, video-action recognition AI pipeline that pushes key operations of traditional action recognition pipelines to the edge and uses this in concert with a cloud-based infrastructure to provide high-precision recognition capability. The benefit to the partner organization, SAIC-Toronto and Samsung Electronics Canada, is advancing the state-of-the-art in action recognition in resource impoverished and dynamic environments, and sharing any newly gained knowledge, patents, and publications resulting from the research.

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Faculty Supervisor:

Nandita Vijaykumar

Student:

Partner:

Samsung Electronics Canada

Discipline:

Computer science

Sector:

Manufacturing

University:

University of Toronto

Program:

Accelerate

ML for Action Detection in Movies for Haptic Effects Generation

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

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Faculty Supervisor:

Aaron Courville

Student:

Partner:

D-BOX Technologies Inc.

Discipline:

Computer science

Sector:

Artificial Intelligence

University:

Université de Montréal

Program:

Accelerate

Deep Learning for drug molecule and target representations

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

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Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

Valence Discovery Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Emerging Event Classification System

The goal is to develop a system that can rapidly detect and report emerging disease outbreaks worldwide by analyzing clusters of news articles using Large Language Models. The objective is to create an efficient and effective way of identifying “disease
events” that can alert public health officials to take prompt action.

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Faculty Supervisor:

Annie Lee

Student:

Partner:

BlueDot Inc

Discipline:

Computer science

Sector:

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

University:

University of Toronto

Program:

Accelerate

Smart Battery Research

The proposed project seeks to develop a Machine Learning-based software solution that accurately measures the capacity, State of Health (SoH), State of Charge (SoC), and cycle count of non-smart batteries utilized in mobile fleets. The project’s primary objective is to bridge the gap between smart and non-smart batteries by monitoring non-smart battery capacity and other pertinent parameters. It includes conducting experiments on Lithium-Ion batteries to obtain valuable data and parameters, which will be utilized to develop mathematical models and Machine Learning algorithms for predicting those parameters for non-smart batteries. The aim is to integrate non-smart batteries into SOTI’s XSight dashboard, providing customers with precise information for a broader range of battery models. This project is expected to benefit SOTI and its customers significantly, while also contributing to sustainable technology development.

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Faculty Supervisor:

Arvind Gupta;Huaxiong Huang

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; 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.