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

Visualizing how Physiology affects Tactile Audio

Wearables are starting to include advanced haptic technology, touch feedback like vibrations, to help people create and listen to music, experience virtual reality (VR) environments, and improve accessibility for people who are Deaf or hard-of-hearing. Designing haptic feedback takes advanced knowledge about physiology and neuroscience that engineers and designers may not have access to. We are using computer-aided visualizations, like maps of the skin or mini-simulations about how the skin reacts to vibration, to help engineers and designers build better, more effective devices and experiences.

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Faculty Supervisor:

Oliver Schneider

Student:

Diana Khater

Partner:

SUBPAC

Discipline:

Other

Sector:

Manufacturing

University:

University of Waterloo

Program:

Accelerate

Application of Data Analytics in Industrial CFD

Due to the potential for significant cost-savings, many companies are turning their attention to digital simulations which produce an enormous amount of data. For companies to realize the benefits of having access to this data they need tools that allow efficient and accurate extraction of information from the data set. The goal of this project is to conduct the research required to develop a framework for the implementation of machine learning algorithms to provide engineering predictions for industrial applications. With its mandate to develop state-of-the-art tools for physics-based engineering applications, SOTAES is well-positioned to develop this innovative product.

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Faculty Supervisor:

Mohamed Belalia;Christopher Houser

Student:

Emanuel Raad;Guanjie Lyu;Maziar Mosavati;Akindolu Dada

Partner:

SOTAES Inc.

Discipline:

Engineering - mechanical

Sector:

Professional, scientific and technical services

University:

University of Windsor

Program:

Accelerate

Smart disinfection in a nosocomial setting: Requirements capture and operation design.

In hospitals, surface disinfection is critical in reducing the spread of disease and the risk of nosocomial infection. In research institutions, where disinfection practices are less rigorous, fomite control is critical in keeping students, faculty and staff safe. Ultraviolet germicidal irradiation (UVC) is an effective disinfection technology that is widely used in hospitals but relies on manual operators. The aim of this project is to design a smart disinfection system for use in hospitals and research laboratories, in partnership with an autonomous robotics company (AIS) and the local health authority (VCH). The input and insights of hospital staff and university researchers will be solicited by means of interviews and taken into account in redesigning the operation of an AIS robot to be used for smart disinfection. The operation of the robot will also be tested on location at a local hospital and within a research laboratory.

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Faculty Supervisor:

Bern Klein;Homayoun Najjaran;Titus Wong

Student:

Mohammadsadegh Mashayekhi;Eleni Patsa;Kyle Low;Jesse Roch;Brett Cosco

Partner:

Advanced Intelligent Systems Inc.

Discipline:

Medicine

Sector:

University:

University of British Columbia

Program:

Accelerate

IoT, AI and Analytics for Smart Urban Water Systems

The Advanced Data Analytics Platform for the Transformation (ADAPT) developed herein integrates quality assurance, quality control and Artificial Intelligence techniques to identify trends and relationships in water networks data. The importance of this information for fostering utility communications and business processes is demonstrated through a visualization dashboard built based on ADAPT. These tools will provide insights regarding constantly generated utility data streams, increase staff effectiveness, identify benchmarks for judging the quality of new data, and embody a foundation on which to evaluate urban water management decisions and set expectations for performance. They will reduce economic losses by improving asset efficiency, and by improving usability of data, assuring that investments in sensor networks are well spent. The integration and assimilation of data from ADAPT, and next generation telemetry, industrial and IoT, will support utility management needs such as in predicting sewer overflows, water supply flows and quality, and optimal sensor placement.

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Faculty Supervisor:

Barbara Jean Lence

Student:

Eric Vaags

Partner:

Global Quality Corp

Discipline:

Engineering - civil

Sector:

Professional, scientific and technical services

University:

University of British Columbia

Program:

Architecture of Opportunity with 3D Printing Technology

Large scale 3D printing is a promising and fast developing technology. The main advantages being the speed of execution, less labour needs, material efficiency, and design freedom. This project aims to identify the opportunities that exist in utilizing this technology for building structures with an emphasis on finding opportunities for solving current housing needs and affordability issues. Alterativ Design Lab (the partner organization) intends to create a solution for the housing affordability crisis in Canada and beyond using 3D printing technology. This project is the first step towards that goal. It will help the firm identify the opportunities and will contribute to the creation of a 3D printed housing company in the near future.

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Faculty Supervisor:

AnnaLisa Meyboom;David Vogt

Student:

Mehdi Einifar

Partner:

Alterativ Design Lab

Discipline:

Other

Sector:

Professional, scientific and technical services

University:

University of British Columbia

Program:

Characterizing the Neural Network Method of Solving Differential Equations on Low-Dimensional Parametrized Problems from Biophysics

The neural network method (NNM) is a relatively new way of solving mathematical problems called partial differential equations (PDEs). PDEs are used as mathematical models for a wide range of phenomena in science, engineering, finance, and elsewhere. Recently, the NNM is receiving attention because several studies have shown that it can solve certain PDE problems that are impossible to solve using most traditional methods. Paradigm AI Incorporated, a start-up specializing in the use of neural network techniques to solve engineering problems, is interested in leveraging these advantages of the NNM to develop next-generation simulation algorithms. Unfortunately, the basic NNM implementations presented in those proof-of-concept studies are slower than traditional techniques for many practical problems, such as those commonly arising in engineering. The proposed research project will attempt to accelerate the NNM using a variety of well-established techniques that have already been applied successfully in machine vision and natural language processing.

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Faculty Supervisor:

Hendrick W de Haan;Lennaert van Veen

Student:

Martin Magill;Andrew Nagel

Partner:

Paradigm AI

Discipline:

Other

Sector:

Professional, scientific and technical services

University:

Ontario Tech University

Program:

Can targeting substance misuse risk in university students be an effective strategy for injury prevention?

The proposed research project is a novel addition to a larger funded trial evaluating a substance misuse intervention program for university students, Univenture, that is being carried out at five universities across Canada. The postdoctoral project is experimental in nature and aimed to investigate whether the personality-targeted psychological intervention for substance misuse also results in reduced risk-taking behaviors and physical injury among 1st and 2nd year university undergraduate students. The intervention program targets four personality traits – anxiety sensitivity, hopelessness, sensation seeking, and impulsivity – which have been found to be positively associated with both substance use and other risk-taking behavior in emerging adults in prior research. Results of the postdoctoral project could have substantial positive influence on university policies and programming for the prevention of substance-related injury on Canadian campuses. Injury Free Nova Scotia (IFNS) – a non-profit organization based in Nova Scotia – is partnering on the research project with Dalhousie University.

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Faculty Supervisor:

Sherry H Stewart

Student:

Fakir Md Yunus

Partner:

Injury Free Nova Scotia

Discipline:

Psychology

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program:

Accelerate

AI Optimized On-Board Computer for Edge computing in Aerospace Applications

Space systems such as small satellites and rovers operating in earth’s orbit, or more recently in interplanetary missions are starting to utilize the features of Artificial Intelligence (AI) in their designs, to reduce human interactions, minimize error and preserve communication bandwidth. AI in space applications can be seen in service vehicles, autonomous image and signal processing, Earth observation, telecommunication and surveillance. However, given that communication bandwidth is limited in most space missions, it is important to optimize the communication link of the spacecraft, and offload some of the computational tasks to the spacecraft’s On-Board Computer. This way, only the important information is transmitted to the terrestrial network and the core of the mission. This research aims to develop a space ready computer that could perform AI based algorithms in orbit and at the edge of the network, or also known as Edge Intelligence in Space

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Faculty Supervisor:

Kevin Plucknett

Student:

Arad Gharagozli

Partner:

GALAXIA Mission Systems Inc

Discipline:

Engineering - computer / electrical

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program:

Augmenting Movies with Interactive Narrative Agent

Netflix recently released an interactive movie that brings the concept of interactive media from video games into movie form. In contrast to the passive viewing experience with conventional movies, the interaction brings viewers into the movie scenes for an immersive experience. However, existing interactive movies are still scripted. Though there have been proposals towards freeform conversational for games and agent-directed interaction, viewers are still not in control of the story development. In this project, our objective is to transform characters in a movie into interactive objects offering viewers a real interactive experience, neither pre-scripted nor pre-filmed. We will propose and develop a self-learning avatar shadowing a selected character to learn the story from the perspective of the shadowed character. We will augment a movie with the avatar to offer interaction with a character. This proof-of-concept will shed light on turning all characters alive to offer viewers unlimited interactive experience.

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Faculty Supervisor:

Mea Wang

Student:

Lynshao Celina Ma

Partner:

TCL Research America

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Calgary

Program:

Quo Vadis? Ontologies for Geospatial Question Answering and Consumer Behaviour

A geospatial query is a question where the concept of location is necessary for formulating the answer. Furthermore, we are not simply interested in spatial relationships, but also with the ways in which people can possibly move through space given the goals that they want to achieve. We therefore want to predict the behaviour of people moving through urban environments based on observations about their purchases. In this project, we will explore how can models of commonsense knowledge can be used for automated reasoning to answer geospatial queries and to infer consumer behaviour.

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Faculty Supervisor:

Michael Gruninger

Student:

Bahar Aameri;Whitney Bai

Partner:

Royal Bank of Canada

Discipline:

Computer science

Sector:

Finance, insurance and business

University:

University of Toronto

Program:

Accelerate

Development of an Al first molecular database to accelerate drug discovery

Using simplified language understandable to a layperson; provide a general, one-paragraph description of the proposed research project to be undertaken by the intern(s) as well as the expected benefit to the partner organization. {100 – 150 words)
The project aims to develop a molecular compounds database to accelerate drug discovery. Compounds shared by chemical providers are currently stored in large library files. Due to their size and number, these files are a bottleneck in virtual screening. The molecular database will gather them in a centralised entity, thus making library processing efficient. The project will also develop partner relationships by offering a better data sharing experience through a user interface and a programmatic client. Besides storing known compounds, the database will implement an unknown compound enumeration feature. Combined with an active learning model, experts will be able to use the database to manually explore chemical subspaces to find hit compounds. The last phase of the project will focus on extending open source work to provide a privacy preserving machine learning framework based on the developed molecular database to enable federated learning pipelines for virtual screening.

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Faculty Supervisor:

Reihaneh Rabbany

Student:

Sacha Levy

Partner:

InVivo AI

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

Multiparametric Analysis of Brain & Lung Imaging from COVID-19 Patients

The intern will participate in NEUROCOVID19, a project studying how the COVID-19 virus can potentially infect and damage the brain. The intern will develop methods for analyzing magnetic resonance imaging (MRI) of the brain and of the lung, as acquired from people who are no longer infected, and people who were never infected. The intern will also develop new MRI methods for enhanced imaging of brain areas that are damaged by COVID-19 infection. This work will help raise awareness – among physicians, scientists and the lay public – of the possibility of brain infection, and will help affected individuals to seek appropriate healthcare. By working on this project, the intern will receive training towards becoming a Siemens MRI support scientist. By the end of the work term, Siemens will be able to consider whether the intern is suitable to fill any positions in MRI research support that are available.

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Faculty Supervisor:

Simon James Graham

Student:

Aravinthan Jegatheesan

Partner:

Siemens Canada

Discipline:

Engineering - biomedical

Sector:

University:

University of Toronto

Program:

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