Innovative Projects Realized

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

29670 Completed Projects

2811
AB
4990
BC
801
MB
663
NL
825
SK
8841
ON
9197
QC
95
PE
568
NB
1088
NS

Projects by Category

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

Assessing Risk for Hazardous Driving and Accident Propensity

Road safety affects everyone, and companies are looking for ways to identify the risk factors for their fleet drivers, and to reduce the chance of accidents. This project will build on Geotab’s existing methods for assessing driving risks, and develop new techniques to better identify risky drivers and risky behaviours. The project will focus on predicting a safety score that is more meaningful to fleet managers and more indicative of risky behaviours. The produced safety scores will help fleet managers decide which driver(s) are more at risk, and take appropriate actions accordingly.

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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

Design and development of a need analysis method based on cognitive mapping techniques

Analyzing needs is essential in any innovation project. A product/service should be designed and developed according to users’ needs so that it could be successful in the market and satisfy its users. However, it is not just the users who are interacting with a product. Designers, developers, project managers, business analysts and other stakeholders might be interacting with the product and will thereupon have needs to be considered. Thus, it is important to deeply analyze the needs and the interrelationships among them to empathize with users and stakeholders while detecting their points of conflict. Cognitive maps are powerful tools in visualizing enormous amounts of data in a network and combined with a data analysis method, they could act as a decision support for designers and developers in innovation projects. Hence, the main objective of this project is to propose a needs analysis method based on cognitive mapping acting as a decision support in the product/service development process.

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Faculty Supervisor:

Fabiano Armellini

Student:

Partner:

Canadian National

Discipline:

Engineering

Sector:

Transportation and warehousing

University:

Polytechnique Montréal

Program:

Accelerate

Better forecasting of weather-related operational shutdowns in forestry activities on Vancouver Island, British Columbia, Canada.

Forestry operations on Vancouver Island are carried out in a region of difficult, mountainous landscapes with very large amounts of rain and snow falling in the area. Heavy precipitation, especially liquid rain falling on snow, can cause not only landslides and floods, but water quality issues as well. These issues affect the environment, people, infrastructure, and the economy associated with the forestry industry. To reduce the risks of landslides and flooding to forestry workers and operations, the shutdown of logging operations during wet weather occurs when enough rain has fallen to meet a shutdown . However, these thresholds have not been standardized across BC, and certainly do not reflect important local conditions; this reduces the utility and accuracy of shutdown criteria. The objective of this project is to improve the guidelines for forest operation shutdown during storms that can trigger landslides and/or floods. By investigating the large-scale weather patterns that cause problematic weather, improvements to the shutdown decision-making process will be accomplished. Financial costs can be reduced while simultaneously improving the protection of people and infrastructure in the process.

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Faculty Supervisor:

David Atkinson

Student:

Partner:

Mosaic Forest Management Corp

Discipline:

Earth science

Sector:

Agriculture

University:

University of Victoria

Program:

Accelerate

Identifying Microaggressions Experienced by BIPOC Engineering Students across Higher Education in Ontario

Acknowledging that discrimination and prejudice of various sorts (e.g., verbal, behavioural, environmental) continue to exist in the education system, this research seeks to address how and why microaggressions against Black, Indigenous, and People of Colour (BIPOC), within engineering departments, show up among peers in classrooms, across interactions in lab environments, group-activities, and more. This research aims to shed light on the prevalence of these microaggressions as they appear in not only in-person learning, but rather, on how they have become embedded within virtual learning environments as well. With a focus on BIPOC in the engineering community, the research includes findings based on extensive literature review, 1-on-1 interviews, and Focus Groups. Further, the research will be carried out across higher education and across Ontario’s Society of Professional Engineers’ (OSPE) growing network of engineering students and alumni. OSPE has developed a four-point action plan to address systemic bias in the culture, training, and its licensure process. This Mitacs project will provide useful information for Diversity and Inclusion Task Force (TF) members, all of whom are Professional Engineers. As well, TF members can provide the Mitacs intern with insights and input that could benefit the research.

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Faculty Supervisor:

Medhat Shehata

Student:

Partner:

Ontario Society of Professional Engineers

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Real estate and rental and leasing

University:

Toronto Metropolitan University

Program:

Accelerate

Project B: Support indication selection for HyperMabs’ developmental therapeutic by building translational rationale for target prevalence.

HyperMabs is a protein engineering company focused on the development of therapeutics for diseases with high unmet need including Covid-19.
The role of the Translational Science Team is to discover and develop biomarkers that indicate pharmacodynamic response to the company’s therapeutics as well as support the relevance of the targeted biological pathways in diseases of interest (indication selection).
The goal of the two MITACS projects within HyperMabs’ Translational Science Department is to develop and validate scientific methods (i) for sample collection to gain insight into pharmacodynamic biomarkers and (ii) to analyse the relevance of the company’s drug targets in a range of diseases. The results of the project will add value to the company’s ongoing efforts in rapidly developing drugs for serious diseases.

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Faculty Supervisor:

Elias Georges

Student:

Partner:

HyperMabs Inc

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

Project A: Feasibility study for the use of saliva samples to understand the Pharmacokinetic (PK)/Pharmacodynamic (PD) relationship for a developmental therapeutic

HyperMabs is a protein engineering company focused on the development of therapeutics for diseases with high unmet need including Covid-19.
The role of the Translational Science Team is to discover and develop biomarkers that indicate pharmacodynamic response to the company’s therapeutics as well as support the relevance of the targeted biological pathways in diseases of interest (indication selection).
The goal of the two MITACS projects within HyperMabs’ Translational Science Department is to develop and validate scientific methods (i) for sample collection to gain insight into pharmacodynamic biomarkers and (ii) to analyse the relevance of the company’s drug targets in a range of diseases. The results of the project will add value to the company’s ongoing efforts in rapidly developing drugs for serious diseases.

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Faculty Supervisor:

Elias Georges

Student:

Partner:

HyperMabs Inc

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

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