Innovative Projects Realized

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

31620 Completed Projects

2978
AB
5221
BC
856
MB
696
NL
899
SK
9419
ON
9858
QC
98
PE
619
NB
1192
NS

Projects by Category

Multiplexed Source of Photons for Next Generation Quantum Secure Networks

With quantum computers which are just starting to hit the market, today’s encryption methods will soon become obsolete. In order to ensure an unconditionally secure data storage/encryption, communications will have to rely on a technology making use of the laws of quantum mechanics. The present MITACS application proposes to build the prototype of an optical source specifically designed for quantum secure communications as well as the development of quantum networks. This proposal represents a significant step towards the realization of compact, stable and energy efficient sources, thus addressing two major issues in quantum communications i.e. scalability and flexibility. Thanks to this MITACS application and stemming from the fast evolution of the quantum market which demonstrates the great efforts made by the scientific community to bring quantum applications to the market, we intend to push forward the development of practical photon sources.

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Faculty Supervisor:

Roberto Morandotti

Student:

Partner:

Ki3 Photonics

Discipline:

Engineering

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Université du Québec : Institut national de la recherche scientifique

Program:

Accelerate

Data Science and Machine Learning Algorithms for Event Sequence Data

Everyday millions of customers move through the sales cycles of companies, generating numerous data for potential use. The main objective of the research project is to advance the current state of the art techniques inside the company, with respect to the application of new algorithms on customer behavior data. From a research perspective, access to large sets of complex real world data can enhance great possibilities to apply and evaluate existing techniques at scale and to develop exciting new ones. This research project will involve developing, improving and implementing algorithms that provide advanced analysis of large sources of event data. In addition, the research projects will contribute directly and immediately to ODAIA’s range of products.

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Faculty Supervisor:

Dehan Kong

Student:

Partner:

ODAIA Intelligence Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Generating Personalized Exercises in an Intelligent Tutoring System

Intelligent tutoring systems (ITS) are computer programs powered by artificial intelligence that can automate the tutoring process. They have the potential to provide low cost and highly scalable one-on-one tutoring to students around the globe in real-time. Their continued improvement is exciting for the future of education, and should be encouraged. Recent years have seen advances in the personalization of each student’s learning experience with an ITS (Kochmar et al., 2020). To further research into personalization, these systems should have the ability to provide question versions that are customized to the needs of each student. Pedagogical studies show that asking questions in the right way can hugely improve the learning gains of the student (Ashton-Jones, 1988) (Hrastinski, 2019). Generating question variants that can better match the needs of different students is the first step to help ITS ask each student the right questions, which is the goal of this project.

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Faculty Supervisor:

Jackie Cheung

Student:

Partner:

Korbit Technologies

Discipline:

Computer science

Sector:

Information and cultural industries

University:

McGill University

Program:

Accelerate

Video Spatial Recognition

Autonomous unmanned aerial vehicles (UAVs) are receiving significant attention in many communities, including academia, industry, and consumer electronics. SOTI is the world’s most trusted provider of mobile and IoT management solutions and its new aerospace division, SOTI Aerospace is focusing on hardware and software systems to support self-navigating situationally aware aerial drones. This project belongs to SOTI aerospace division and focuses on a vision system for the indoor environment. The primary objective of this project is finding out a real time 3D object recognition methodology to support drone navigation. Initial applications will be focused on the medical sector and search & rescue operations.

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Faculty Supervisor:

Anthony Bonner

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

COVID-19 and resiliency: How Registered Practical Nurses working in long-term care adapt in times of personal, professional, and institutional crisis

Working in partnership, interdisciplinary professionals and the Registered Practical Nurses (RPNs) Association of Ontario (WeRPN) aim to understand what contributes to and detracts from personal, professional and institutional resiliency for RPNs during COVID-19. This is a rare opportunity to learn from front-line workers directly during historical social periods, such as COVID-19, and even less often does the public become acutely aware of the residents’ social inequities within the long-term care (LTC) sector and the indispensable nature of the largest regulated LTC workforce. This partnership will research ongoing first-hand experiences of RPNs as a training project for Canadian students. This information will inform institution-level policy and practices to address the systemic challenges that have plagued the sector for years, offer solutions from invaluable front-line RPNs, and will be accessible to researchers and public audiences, globally

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Faculty Supervisor:

Denise Connelly

Student:

Partner:

WeRPN

Discipline:

Sociology

Sector:

Professional, scientific and technical services

University:

The University of Western Ontario

Program:

Accelerate

Interpretability of machine learning models that predict cognitive impairment from human speech and language

Machine learning has great potential in detecting cognitive, mental and functional health disorders from speech, as acoustic properties of speech and corresponding patterns in language are modified by a variety of health-related effects. Specifically, neural language models, have recently demonstrated impressive abilities in tasks involving linguistic knowledge. Their success in language understanding and classification tasks could be attributed to their effective representations of linguistic knowledge. However, the increasing complexity of the state-of-the-art models make them behave in a black box manner when the models are not easily interpretable. The successful adoption of machine learning models in healthcare applications relies heavily on how well decision makers are able to understand and trust their functionality. Only if decision makers have a clear understanding of the model behavior, can they diagnose errors and potential biases in these models, and decide when and how much to rely on them. As such, it is important to create techniques for explaining black box models in a human interpretable manner.

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Faculty Supervisor:

Frank Rudzicz;Andrei Badescu

Student:

Partner:

WinterLight Labs Inc

Discipline:

Computer science

Sector:

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

University:

University of Toronto

Program:

Accelerate

Searching for In-Situ Resources on the Moon

As lunar exploration is ramping up, resources present on the Moon are valuable and the distribution of these resources will directly affect future human surface operations. Water ice and other volatiles will be a key factor in surface operations so small rover prospecting missions to identify these resources are essential. In order for such focused rover missions to be successful, a well-developed concept of operations (ConOps) is required. Developing these ConOps through analogue missions as well as testing rover and instrument capabilities through lunar simulations will ensure these prospecting missions are prepared for efficient surface operations and can achieve their science objectives within limited timeframes.

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Faculty Supervisor:

Edward Cloutis

Student:

Partner:

Mission Control

Discipline:

Earth science

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Winnipeg

Program:

Accelerate

Improved Model Compression Techniques and Processes for Large Scale Pretrained Language Models

Over the past few years, the abilities and performances of deep-learning natural language processing (NLP) have evolved dramatically. A main reason for those improvements is the scaling of the number of parameters in models to tens or hundreds of billions. However, it also becomes impractical to deploy those models in production as the computation cost becomes prohibitive for the vast majority of applications. The project aims to develop a structured compression process incorporating the state-of-the-art techniques (and potentially new ones) allowing quick move from large-scale models to smaller, faster production-ready ones. Additionally, we will investigate the impacts and traits of the devices in industry to further optimize the compression techniques. The ultimate goal is to result in the development of general-purpose compression techniques which can be applied to the whole range of Turing language models. This project can benefit several NLP-related Microsoft services on various devices.

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Faculty Supervisor:

Eyal de Lara

Student:

Partner:

Microsoft Canada

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Toronto

Program:

Accelerate

Efficacy of end-to-end digitizited eye movement therapy for trauma desensitization, on Savyn’s app for treating PTSD symptoms

One of ten Canadians will experience trauma at least once in their lives and will develop post-traumatic stress disorder (PTSD). Many people cannot access PTSD care because of the high cost, long-waiting time, stigma and other barriers etc. Amygdala (deep brain) is important to traumatic emotions and memories. One way to access these memories is to use bi-lateral eye movements. Eye Movement Desensitization & Reprocessing (EMDR) is a well-established treatment for PTSD. Savyn’s app is based on some aspects of EMDR so more people who face barriers to accessing PTSD mental health support can have better access.
This is a research project to determine the efficacy of Savyn’s app in treating PTSD patients and to determine if it improves their PTSD symptoms The study will benefit Savyn in providing insight into how well PTSD symptoms are reduced, therefore providing evidence of benefit for trauma survivors to heal and feel better.

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Faculty Supervisor:

Benjamin Dunkley

Student:

Partner:

Savyn

Discipline:

Life Sciences

Sector:

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

University:

University of Toronto

Program:

Accelerate

Vehicle Minor Collision Detection Using Telematics and Environmental Data

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 a minor collision detection system. On top of driving data and environmental data, it also contains machine diagnostic data. All these datasets are hypothesized to contain hidden features that are highly correlated to minor collisions. Since minor collisions are harder to identify, high frequency event data will be required. Telematics data is typically heavily compressed, however Geotab has a system that is capable of capturing higher frequency sampled data that will make this possible. The objective is to use this to detect minor collision occurrences through a combination of machine learning approaches which fall under the umbrella of supervised and semi-supervised learning.

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Faculty Supervisor:

Andrei Badescu;Sheldon Lin

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

Big Data Research for Open Source Applications

Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. In this internship, we analyze a real-world big data set(s) to make sensible inferences by taking into account a selected range of criteria. A number of methods and algorithms are investigated, evaluated and evolved to advance the development of specialized tools and processes.

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Faculty Supervisor:

Mark Coates

Student:

Partner:

Appnovation Technologies Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

SOTI SNAP Data Acquisition and Display

Companies spend a large amount of money and time on mobile application development which requires knowledge of various native platform programming languages and the different characteristics of these platforms. However, demands for mobile applications are increasing and are becoming difficult to follow for the IT department. One solution seems to be no-code development platforms that allow anyone to create applications with no coding experience. The SOTI SNAP is one such cross-platform mobile application development software with a user-friendly drag-and-drop interface. In addition, with SOTI Blockly, a block-based programming language, no prior programming experience is required even for custom scripting. One challenge of such software is the secure retrieval, transfer, display, and manipulation of data without a single line of code. The goal of this project is two folds. First, we will research and find a way to retrieve and transfer data securely from / to potentially multiple data sources that are not exposed externally. Second, we will perform user experience research to design and evaluate an interface to display, control, and manipulate data without code. The resulting deliverable will enhance the existing functionality of SOTI SNAP.

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Faculty Supervisor:

Marsha Chechik

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.