Innovations 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

Detection and Characterization of the Synovium in Musculoskeletal Ultrasound

Arthritis is a chronic disease that severely decreases the quality of life and affects almost 4.6 million Canadians, costing $33 billion for the Canadian economy every year. Affected individuals experience pain and disability through an extended period of time. Rheumatoid arthritis (RA), a common form of arthritis, is an autoimmune disease characterized by the inflammation of the synovium, or synovial membrane, a connective tissue that provides a cushion between bones and tendons and muscle around a joint. Characterization of the synovium is crucial for the management of RA as well as a useful marker of treatment efficiency. Ultrasound imaging is an affordable non-invasive technique to assess the synovium for thickening. The examination of the images is often contingent on sonographers and clinicians. Therefore, the current research proposes to utilize deep learning to accurately and reliably assess the synovium on ultrasound images, and then implement the network in the software implementable in medical settings.

View Full Project Description
Faculty Supervisor:

Pascal Tyrrell

Student:

Dmitrii Paniukov

Partner:

16 Bit Inc.

Discipline:

Medicine

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

CFD simulation and analysis of gas separation using dual-spinning disk reactors

Reducing greenhouse gas (GHG) emissions is an important global issue as GHGs from human activities are a significant contributor to global climate change. Greenhouse gases cause climate change such as rising average temperatures, extreme weather events. Carbon dioxide is the primary greenhouse gas, responsible for about three-quarters of emissions. This project aims to reduce global carbon emissions by extracting CO2 from waste gas streams and converting it into fuel. For this, Vorsana’s radial counter-flow device: a low-cost and efficient gas separation method based on the concept of flow between two parallel counter-rotating disks at an axial distance, is used. As the disks rotate, a complex turbulent vortex system is created in the gap. The rapidly rotating flow inside each vortex applies localized centripetal accelerations on the gases and forces the light gas fraction towards the axis and heavy gas fraction towards the periphery, where they are collected through outlet channels.

View Full Project Description
Faculty Supervisor:

Joshua Brinkerhoff

Student:

Nidhi Sharma

Partner:

Vorsana

Discipline:

Engineering

Sector:

Manufacturing

University:

Program:

Accelerate

Identification of Key Microbes from Spontaneous Beer to Improve Mixed Fermentations

Sour beers, traditionally made via spontaneous fermentation, are growing in popularity; particularly examples produced using the modern technique of mixed fermentation. These mixed fermentation beers present unique challenges during production. We hypothesize that by examining traditional process spontaneously fermented beer, we can identify new methods and strategies for improving the quality of mixed fermentation beers. Blind Enthusiasm operates a brew house uniquely built to produce both traditional method spontaneous beer, as well as mixed beer. In order to improve mixed fermentation beer it is important to understand how different populations of microorganisms develop during fermentation in spontaneous beer. Through this collaboration we hope to understand how spontaneous fermentation progresses and how individual microbes from these beers impact the final flavor of the beer.

View Full Project Description
Faculty Supervisor:

Benjamin Willing

Student:

Ben Bourrie

Partner:

Blind Enthusiasm Brewing Company

Discipline:

Food science

Sector:

Manufacturing

University:

University of Alberta

Program:

Accelerate

Getting to Work: Investigating Labour Market Challenges in Saugeen Shores, Ontario

How do you support the fastest growing municipality in a largely rural region as it works through its growing pains? What happens in a rapidly growing community that’s been recognized as one of the best places to live as it tries to keep up with labour demands, changing demographics, and challenges to the enabling infrastructure that support a strong labour market and economic development? This research focuses on understanding the implications of the unique labour market dynamics of a rural region in transition through a case-study of Saugeen Shores, Ontario. With better understanding, businesses and community leadership can better address the challenges and opportunities emerging from changing demographics and economies. The outcomes of this research will benefit local and regional stakeholders, including Bruce Power, to build enhanced economic strategic plans that target key issues in the local labour market – an area of evidence-based research that remains an area of significant focus for business, government, and rural development researchers and practitioners across Canada and around the world.

View Full Project Description
Faculty Supervisor:

Ryan Gibson

Student:

S. Ashleigh Weeden

Partner:

Bruce Power

Discipline:

Environmental sciences

Sector:

Energy

University:

University of Guelph

Program:

Accelerate

Applied Machine Learning for Early Detection of Retinal Toxicity

Hydroxychloroquine (HCQ) is an anti-inflammatory drug that is widely prescribed for a range of auto-immune disorders such as lupus and rheumatoid arthritis. An unwanted side effect of long-term use of HCQ is vision loss by retinal toxicity. If detected early, it could lead to early intervention to prevent vision loss and improve the quality of life for patients.
The project involves research on current machine learning approaches for the development of a system that would aid in the early detection of retinal toxicity. Current approaches involve qualitative interpretation of multifocal electroretinogram (mfERG) and optical coherence tomography (OCT) images by an expert. The project aims to develop a system that automates the interpretation of mfERG and OCT images to assist medical professionals in making an accurate diagnosis.

View Full Project Description
Faculty Supervisor:

Huaxiong Huang;Arvind Gupta

Student:

Faisal Habib

Partner:

Kensington Eye Institute

Discipline:

Computer science

Sector:

Health care and social assistance

University:

University of Toronto

Program:

Accelerate

Forum Representation for Cross-Domain Recommendation

An internet forum is an online site for people to have conversations. It contains threads to hold discussions between users. Recommending appropriate threads to forum users is one of the main goals of an internet forum. To provide positive user experience, cross-domain thread recommendation is required, which can be benefited greatly from the help of forum representations. This research project aims to use two different approaches to create forum representation. One approach is to use the content-based method that utilizes textual data in each subforum and build a topic model to generate subforum embedding vectors. Another approach is the user-based method. It generates subforum embeddings by using a modified skip-gram model, which uses the subforum to predict its user contexts. Lastly, the research project will explore the possibility for a hybrid user-content based approach to further increase the embedding performance.

View Full Project Description
Faculty Supervisor:

Gerald Penn

Student:

Yizhan Jiang

Partner:

VerticalScope Inc.

Discipline:

Computer science

Sector:

University:

University of Toronto

Program:

Accelerate

Recommending Benefits Utilization to Promote a Healthy Lifestyle

Users on the League platform have access to a number of health and wellness benefits including massage, physiotherapy, personal trainers and a variety of other programs; however, not all of them fully utilize them to maximize their wellbeing. Utilizing the health and program utilization data we want to develop robust personalized predictions that will suggest to individuals, programs that they are eligible for and would benefit their health. We are hoping to further develop League’s platform into a health hub where every user will be promoted healthy behavior and wellness programs optimized for their health profile. Our recommendations will strive to make our users happier and healthier.

View Full Project Description
Faculty Supervisor:

Scott Sanner

Student:

Puneet Gupta

Partner:

League Inc

Discipline:

Computer science

Sector:

Finance, insurance and business

University:

University of Toronto

Program:

Accelerate

Goal-Conditioned Reinforcement Learning

The goal of the project is to improve upon the methodology behind goal conditioned learning. In this framework, similar to the setup in traditional reinforcement learning, an agent interacts with an environment. However, instead of training the agent to maximize return, the agent is trained to reach a given goal at the end of the trajectory. That is, given a rollout-specific goal, the agent attempts to reach it. This goal conditioned paradigm is particularly promising for applications where the objective changes in every episode, for example, controlling a robot or a drone for different tasks; or self-driving vehicles, where the destination might change between episodes. In this project, we will explore potential improvements within the goal conditioned framework, both in the discrete and continuous action space settings.

View Full Project Description
Faculty Supervisor:

Arvind Gupta

Student:

Panteha Naderian

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Analysis and optimization of a novel thermal storage system for ground-source heat pumps

Researchers at Ontario Tech University are collaborating with McClymont and Rak geotechnical engineers to develop a new sustainable thermal storage technology that addresses the main challenges that have prevented geothermal heating and cooling systems from being adopted in a meaningful way in Ontario. An innovative storage medium, based on a construction slurry, will be developed and used in the underground thermal storage, and coupled to a geothermal heat pump to provide high-efficiency and clean building heating and cooling. This new, inexpensive technology will be able to be effectively integrated with solar heating, making it more cost competitive with natural gas, and leading to increased adoption and considerable CO2 emissions reduction in the building sector.

View Full Project Description
Faculty Supervisor:

Marc Rosen

Student:

Seyed Masih Alavy Ghahfarrokhy

Partner:

McClymont and Rak Engineers Inc

Discipline:

Engineering - mechanical

Sector:

Professional, scientific and technical services

University:

Ontario Tech University

Program:

Accelerate

Learning Discussion Thread Representations to Empower Conten-based Recommendation

VerticalScope is a company that owns online forums in many domains, such as automotive, health, technology, and powersports. VerticalScope uses a content based recommender system to mitigate the cold start problem, where a large portion of traffic on the forums are made by unregistered users. The goal of this project is to learn representations of discussion threads. Thread representations that capture semantic and contextual information can improve the recommender system to suggest more relevant threads to users, and boosts search engine optimization and user retention rate. Understanding user sentiments also allows for the discovery of trending topics, and personalized homepage and advertisements.
Learning thread representations has various challenges. Within the automotive forums for example, there may be multiple threads talking about buying and selling cars. However, though these threads may have similar context, the object of discussion (e.g. the specific car model) can be different, and the learned representations should capture these differences. Another related problem is that there are many out of vocabulary words that may be very important to the relevancy between two threads (e.g. the name of a specific product).

View Full Project Description
Faculty Supervisor:

Gerald Penn

Student:

Ding Tao Liu

Partner:

VerticalScope Inc.

Discipline:

Computer science

Sector:

University:

University of Toronto

Program:

Accelerate

Designing ‘Zero credit touch’ (ZCT) pre-approved credit underwriting program for retail customers

ICICI Bank has developed various ‘Zero credit touch’ (ZCT) strategies where without any credit intervention and additional information taken from customers, credit facilities can be provided. But there are several challenges in the expansion of ZCT strategies, namely, (i) current credit models which are a combination of business rules, scorecards and machine learning models, do not qualify a significant proportion of existing ICICI Bank customers; (ii) wherever customers do not have a salary account with the Bank, estimated income is lower leading to the customer being offered an amount lower than his/her requirement; (iii) customers with fraudulent intentions can open accounts and over time, these profiles would qualify for ZCT. To tackle these problems, we propose a novel ZCT system incorporating several state-of-the-art methodologies to build one go-to product to reduce credit and operations cost of lending whilst providing a superior customer experience.

View Full Project Description
Faculty Supervisor:

Sebastian Jaimungal

Student:

Sanghamesh Vastrad

Partner:

ICICI Bank Canada

Discipline:

Computer science

Sector:

Finance, insurance and business

University:

University of Toronto

Program:

Accelerate

Using NLP models to fetch SQL data via voice command for SOTI SNAP Analytics (NL-to-SQL)

This project is focusing on creating an integration of a database and mathematical calculation, with the use of Alexa, Google home, Cortana, so that users can use Natural Language to aggregate meaningful data and answer questions from a given database. For example, suppose there is a database about car sales. If I asked Siri, “who is the best sales for BMW in Toronto?” Our designed algorithm should return the name of the top sales from this given database. The project also builds out an Analytics Engine that will perform mathematical calculations on the data submitted by the SNAP APP dynamically.

View Full Project Description
Faculty Supervisor:

Gerald Penn

Student:

Sarasadat Golestaneh;Ronak Patel;Lixiang Wei

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

University:

University of Toronto

Program:

Accelerate

Previous
1…214215216217218219220…1,106
Next

Join a thriving innovation ecosystem.

Subscribe to our newsletter now!

Subscribe