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

Automated Model Tuning for Retail

Artificial intelligence, especially Machine learning algorithms, plays important roles in building recommendation systems and promotional forecasting systems for retailers. However, training a machine learning model requires the choice of a number of significant features and requires tuning a large set of configurations. Therefore, it takes a long time for humans to find the optimal configuration for one or more predictors. However, the predictive performance of existing automated tuning models is not as good as manually tuning. Besides, the approach cannot be applied to more than one model. This project, will propose a system that can automatically come up with a set of models with corresponding features and configurations for a specific problem (e.g., promotional forecasting) that provides good or acceptable performance for the prediction.

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Faculty Supervisor:

Anthony Bonner

Student:

Lan Yao

Partner:

Rubikloud Technologies Inc.

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Development of a Bidding simulation environment

Addictive Tech Corp is a fast-growing ad-tech company. They use real-time advertisement bidding software which is massive and sophisticated. The actual dynamics involved in any given bid are complex and hard to predict. This makes writing test logic for such a system cumbersome and catching all corner cases next to impossible. Because of the scale of operations, understanding the environment in which the bidding software operates is difficult. This is problematic as such software needs to be highly optimized to be competitive. Creating a simulation of the environment the system operates in allows for a controlled study of said system and environment. Having fine-grained control over environment parametrization in the simulation will make testing and analysis more streamlined.

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Faculty Supervisor:

Cristiana Amza

Student:

Vincent Tembo

Partner:

Addictive Tech Corp

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Operating Room Traffic Assessment: A Video Analysis Approach

Surgical Safety Technology aims to improve operating room safety by capturing and analyzing operation videos. Usually, operating room traffic (like people displacement) has a huge impact on surgery. Unnecessary movements can cause distraction of surgeons and pollution of the sterile environment. This project applies computer vision models to detect and track people movements in the operating room and assesses the relationship between adverse events and errors. Popular machine learning models such as Deep Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have the capability to analyze time sequential data. Trained on the well-labeled data directly from specific hospitals, these models could work out precise operating room traffic trace and its correlation with surgical events.

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Faculty Supervisor:

Sanja Fidler

Student:

Tianbao Li

Partner:

Surgical Safety Technologies Inc

Discipline:

Computer science

Sector:

Medical devices

University:

Program:

Accelerate

Hand Pose Reconstruction Based on Fast Multi-Touch Sensors

Serving as the most widely-used body part for communication, hand is a very important tool for human to interact with the world. Especially with the continuing development of virtual reality and augmented reality, hand pose information has gradually become an indispensable component for improving users’ experience in interacting with computing devices. Therefore, this project aims at achieving hand pose reconstruction based on capacitive sensing technology using machine learning algorithm. The capacitive sensor that will be utilized in this project is supported by the project partner, Tactual Labs who, by the end of this project, will benefit by having its current innovative capacitive controller more intelligent.

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Faculty Supervisor:

Karan Singh

Student:

Yanjun Jiang

Partner:

Tactual Labs Co.

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Examining Media for Fraud Detection

Nowadays a corporation’s public image plays a major role in that company’s decisions and financials. This project involves predicting fraud and errors within the financial statements of publicly traded companies. The goal is to incorporate information such as press releases and industry media coverages to provide an insight to these companies under audit and their industries. Ultimately, this would be used as a tool to assist auditors identify fraud and errors within these financial statements.
This project covers analyzing collected media information for features such as corporate sentiments, public opinions, and trend predictions. This can help identify misstatement within annual financial statements, stock manipulation, and other issues.

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Faculty Supervisor:

Suzanne Stevenson

Student:

Ran Zhang

Partner:

CaseWare International

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Fraud Prevention in Real-Time B2B Payments Using Streaming Algorithms

The Pungle payments-as-a-service platform delivers low cost, real-time, friction free business disbursements, peer-to-peer (P2P) transfers, and B2B supplier payments. Pungle’s mission is to enable businesses with a digital payments platform that provides real-time disbursements and transfers. The problem that arises with digitization of business payments is higher risk for fraud due to its electronic nature. Therefore, there’s a need to be absolutely certain that both the sender and recipient of payments are the intended parties and that there are no anomalies in payment volume and frequency. This project is to build a streaming data pipeline, including data warehousing, that will allow us to log and persist transaction data for both audit trails and as a data set with which we will then develop and train real-time fraud prevention system using the latest research in streaming / machine learning algorithms.

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Faculty Supervisor:

Richard Zemel

Student:

Xu Sun

Partner:

Pungle

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

An Artificial Agent for Light Switch

Smart home devices with artificial intelligence (machine learning and deep learning) will change our lifestyles in the near future. The objective of this project is to develop an artificial agent, which will power the smart light switches produced by ecobee. The artificial agent, a machine learning program, will use the data collected by the sensors in the smart light switches and help the users operate the light switches without the users’ manual control. The goal of this project is to develop an underlying smart program to learn the behaviors and of users with the light switches. The progress of this project will help ecobee provide better smart light switches for its clients and potentially incorporate this smart program to other similar ecobee smart home devices. Therefore, the success of this project can eventually contribute the smart home industry.

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Faculty Supervisor:

Roger Grosse

Student:

Han Meng

Partner:

Ecobee Inc.

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Improvement of fluorescent bead detection and classification algorithms for CD4 cell counting in portable flow cytometers

This research project focuses on the development of software for a device that can diagnose HIV and monitor its severity by taking a small sample of the patient’s blood. The software will count the number of immune cells present in the sample by taking images of the patient’s blood as it flows through the device. It will then report the cell count to the medical professional who will be able to make a diagnosis.
This project will ease the process of HIV diagnostics, and make HIV testing more accessible in general. This could greatly improve outcomes for patients in developing countries, where access to healthcare is sub-par and the need for accessible medical equipment is growing.

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Faculty Supervisor:

Jan Andrysek

Student:

Vyshnavi Kommu

Partner:

ChipCare

Discipline:

Engineering - biomedical

Sector:

Medical devices

University:

Program:

Accelerate

Kappa architecture serving layer suitability

Global service providers in highly regulated financial sectors must accommodate an ever-changing, sometimes competing, landscape of regulatory and business concerns. This project seeks to define a technology infrastructure design that supports current and anticipated data privacy and data residency concerns, making it possible to keep data within borders while still facilitating collaboration across those borders. Consumers are increasingly aware of the collection of their private data, but are often unaware of cross-border movement of their data. Businesses are cognizant of the limited (or untested) reach of legal and regulatory recourse when their data (and their customers’ data) unnecessarily crosses borders. This project seeks to add technology frameworks to aid in addressing those customer and business concerns.

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Faculty Supervisor:

Ashvin Goel

Student:

Alexandre Luiz Brisighello Filho

Partner:

Ethoca Technologies

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Evolving data storage design for a highly regulated financial sector.

Global service providers in highly regulated financial sectors must accommodate an ever-changing, sometimes competing, landscape of regulatory concerns. This project seeks to determine a reasonable path forward in technology design and adoption to accommodate current and anticipated data privacy and data residency concerns, making it possible to keep data within borders while still facilitating collaboration across those borders. Consumers are increasingly aware of the collection of their private data, but are often unaware of cross-border movement of their data. Businesses are cognizant of the limited (or untested) reach of legal and regulatory recourse when their data (and their customers’ data) unnecessarily crosses borders. This project seeks to add technology frameworks to aid in addressing those customer and business concerns.

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Faculty Supervisor:

Matt Medland

Student:

Dana Alpysbayeva

Partner:

Ethoca Technologies

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Greener Routes to Value-Added Fluorocarbons by Metal-Catalyzed Reactions

The current production methods for new generation refrigerants (HFO-1234yf) used in cars, refrigerators, air-conditioners, etc. require energy intensive and sometimes corrosive conditions. The current project seeks to reduce or eliminate these two caveats. We propose, by using readily available feedstock or by-products from Teflon manufacturing, we could use our process to easily manufacture HFO-1234yf. Using our less energy intensive, heating to only 50 °C, and mild conditions could lead to significant cost reductions in plant equipment and energy demands. Thus, the reduction in energy demand will bring about lower production cost. This will also reduce CO2 emissions which could prove beneficial, in Ontario, with the current cap & trade program. These improvements would benefit our partner by allowing them to produce the same quality of product at lower-cost and includes the possibility of the development of new products.

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Faculty Supervisor:

Tom Baker

Student:

Nicholas Andrella

Partner:

Arkema

Discipline:

Biochemistry / Molecular biology

Sector:

Alternative energy

University:

Program:

Accelerate

Improving Social Conditions for Indigenous Youth: A Case Study of Organized Sports in Canoe Lake Cree Nation

This Internship project will look at issues affecting the overall social conditions of First Nation youth in Northern, rural and remote communities, with a specific focus on the Northern community of Canoe Lake Cree Nation. Historically, the people of Canoe Lake lived a traditional lifestyle off the land, including traditional land that was lost to the Cold Lake bombing range. The loss of traditional land and livelihood has had enormous social effects on the community, and its peoples. Organized sport and recreation provide an opportunity to improve social conditions for northern communities, and to rebuild the First Nation youth identity and confidence. There is a tremendous amount of untapped potential for First Nations youth, and this research will highlight how we can better the youth social outcome.

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Faculty Supervisor:

Ken Coates

Student:

Blaine Mirasty

Partner:

Meadow Lake Tribal Council

Discipline:

Public administration

Sector:

Aboriginal affairs

University:

Program:

Accelerate

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