Innovative Projects Realized

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

13270 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

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:

Anum Khan

Partner:

Ontario Society of Professional Engineers

Discipline:

Engineering - civil

Sector:

Professional, scientific and technical services

University:

Ryerson University

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:

Malikeh Ehghaghi

Partner:

WinterLight Labs Inc

Discipline:

Computer science

Sector:

University:

University of Toronto

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:

Yunze Pan

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

University:

University of Toronto

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:

Cheng Han Hsieh

Partner:

ODAIA Intelligence Inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Persuasive technology – Guidance to Virtual Relationship Manager (VRM) for effective sales effort basis voice data mining

Voice of the Customer (VoC) is how companies hear and listen to customer feedback about their brand, products, and services. Voice of the Customer solutions convert gathered feedback into valuable data and insights at scale. Data-driven VoC analytics programs are proven to increase customer lifecycle value and lower customer churn. Companies in various industries including insurance, financial services, and healthcare are leveraging this technology to generate insights into customer needs. ICICI Bank has large number for Virtual Relationship Managers who engage with customers assigned to them through electronic channels only for various product sales and servicing. This project’s primary objective is to develop a machine learning model which incorporates both customer record available with the bank along with voice data for finding potential buyers of the products of the bank. We also aim to create a voicebot to make personalized recommendations based on customer history and preferences.

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Faculty Supervisor:

Mark Chignell

Student:

Saarthak Sangamnerkar

Partner:

ICICI Bank Canada

Discipline:

Computer science

Sector:

Finance, insurance and business

University:

University of Toronto

Program:

Accelerate

Convolutional Neural Network for Demand Forecasting

Many retailers are interested in forecasting demand for the products they sell. Deloitte has used machine learning methods to tackle this problem in the past. However, this requires the creation of hand-crafted features based on product sales data, which is a costly and time-intensive process. Using alternative models to perform this task would remove the need for laborious data manipulation. It will also allow model enhancements to scale across many clients rather than requiring from-scratch data manipulation for each new client. Hence, this project will involve the development of a new machine learning model to predict product demand. The model will be trained using historical sales data. Iterative stages of model architecture and fine-tuning will give rise to the final model. Various enhancements to the model architecture will be explored. The main objective is for the final model to be integrated into a modular demand prediction solution at Deloitte.

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Faculty Supervisor:

Huaxiong Huang;Arvind Gupta

Student:

Sasha Nanda

Partner:

Deloitte

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Twin-entry turbine modelling for high efficiency engines

A turbine is placed in an engine system to make use of energy contained in exhaust gases. It receives hot gases that are left over from the combustion process and converts them into useful shaft power that increases overall efficiency. The flow from each cylinder is highly dynamic since it is created by the exhaust valve opening and closing. An engine with more than four cylinders must separate the flows in order to prevent interaction that can lose some of the available energy. A twin-entry turbine is designed with two inlets and a divider to keep these flows separate until they reach the impeller. This research project will aim to simulate a twin-entry turbine by creating a new model that can predict its performance when exposed to realistic pulsating flow. This model needs to be fast-running in order to successfully integrate it into a larger engine system simulation.

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Faculty Supervisor:

Colin Copeland

Student:

Kate Powers

Partner:

Cummins Canada

Discipline:

Engineering

Sector:

Manufacturing

University:

Simon Fraser University

Program:

Accelerate

Cloud platform for machine learning

Surgical Safety Technologies aims to provide healthcare professionals with the opportunity to perform research in areas of surgical performance and education and implement evidence-based solutions to improve patient safety. Search on video content would an ideal functionality to assist with healthcare professionals’ research. This project uses computer vision model to rank the relevance of the surgical video. In addition, this project aims to improve the user experience of the product by analyzing the metadata generated by the cloud platform and developing an efficient data visualization technique.

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Faculty Supervisor:

Maryam Mehri Dehnavi

Student:

Yi Xiang

Partner:

Surgical Safety Technologies Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Hybrid recommender system with multi-source data and social knowledge graph integration

Accurately recommending items of interests is essential for users to improve their experience. To acquire better performance, a recommender system can utilize multiple sources of data and the social knowledge graph. This can lead to efficient use of information to improve the recommender system. By exploring the data and extract crucial features to feed to designed models, the recommending engine can increase its performance dramatically. Furthermore, a social knowledge graph contains the description and relation of users, which can act as a knowledge base for inference. By fusion of those techniques, the resulting recommender system can be optimized.

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Faculty Supervisor:

Peter Marbach

Student:

Yiwen Feng

Partner:

AppDirect Canada Inc

Discipline:

Other

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Application of Heritage Conservation and Temporary Protection Plans in the Planning Act Process.

This project involves researching and developing templates for heritage conservation plans and temporary protection plans to manage impacts to cultural heritage resources, such as historic buildings and landscapes. The objectives of this study are: 1) a Literature Review of best practices and background information; 2) Compare these with current Ontario legislative requirements;3) Develop analytical tables for Heritage Conservation Plans, and Temporary Protection Plans; 4) Apply these plans to two real world examples and 5) Collaborate in research on the histories of Canadian suburban planning. The partner firm will get access to university research facilities (libraries) and best-practice templates for future heritage planning projects.

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Faculty Supervisor:

David L.A. Gordon

Student:

Jenna Dawson

Partner:

Ontario Professional Planners Institute

Discipline:

Geography / Geology / Earth science

Sector:

Other

University:

Queen's University

Program:

Accelerate

Development of artificial intelligence algorithms for microbiome-based classification of disease

The goal of this project is to help build artificial intelligence algorithms for the diagnosis of disease using data derived from the human microbiome. This project will be focused on implementing new statistical methods to reduce “noise” found in data from different sources, allowing for us to improve the training of artificial intelligence algorithms. Another focus of this project will be to implement new types of models that are better suited for microbiome data, allowing for more accurate predictions. With these improved models, Phyla (partner org) will be able to more accurately detect disease from the microbial profile of stool samples.

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Faculty Supervisor:

Adam Oberman

Student:

Noah Marshall

Partner:

Phyla

Discipline:

Statistics / Actuarial sciences

Sector:

Health care and social assistance

University:

McGill University

Program:

Accelerate

Seasonal Change in Roosting Ecology in the Silver-haired bat (Lasionycteris noctivagans)

Silver-haired bats are common species of bat found in North America. They use cavities in trees and space under loose bark to roost, or rest and raise young. The silver-haired bat is thought to migrate south over the winter. Despite this, we have found them in parts of British Columbia during the winter, suggesting they may not migrate in these areas. Our work will help support a MSc student who will investigate how silver-haired bats are using trees in areas where they overwinter in British Columbia and compare with how they use trees in the summer. We will capture bats and track them to identify the tree roosts. Once located, we will record how long the bats stay at each tree, and tree features to identify patterns in tree use. Our partner, the Wildlife Conservation Society of Canada has identified this area of research as a high priority conservation action. The results from this project will be used to plan bat-friendly forestry practices by identifying trees that are suitable as winter and summer roosts for silver-haired bats.

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Faculty Supervisor:

Erin Baerwald

Student:

Emily de Freitas

Partner:

Wildlife Conservation Society Canada

Discipline:

Environmental sciences

Sector:

Professional, scientific and technical services

University:

University of Northern British Columbia

Program:

Accelerate

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