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

Learning representations through stochastic gradient descent by minimizing the cross-validation error

Representations are fundamental to Artificial Intelligence. Typically, the performance of a learning system depends on its data representation. These data representations are usually hand-engineered based on some prior domain knowledge regarding the task. More recently, the trend is to learn these representations through deep neural networks as these can produce significant performance improvements over hand-engineered data representations. Learning representations reduces the human labour involved in any system design, and this allows in scaling of a learning system for difficult problems. In this project, we propose to design a new incremental learning algorithm, called crossprop, for learning representations based on prior learning experiences. Specifically, the algorithm considers the influences of all the past weights while minimizing the current squared error, and uses this gradient for incrementally learning the weights in a neural network. This algorithm is called crossprop because it learns to shape the weights in a neural network through leave-one-out cross-validation procedure.

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Faculty Supervisor:

Richard Sutton

Student:

Vivek Veeriah

Partner:

RBC Financial Group

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

High power all-fiber Raman laser at 1.65 ?m

Fiber lasers have become the fastest-growing laser with a projected worldwide revenue up to $1.41 billion in 2017. In particular, fiber lasers at 1.65 ?m have drawn increasing attention with potential applications in chemical sensing, LIDAR and spectroscopy. All-fiber Raman lasing technology is a promising and efficient technology to achieve high power lasing at 1.65 ?m. However, there are limited all-fiber high power sources at 1.65 ?m that are commercially available. In this project, we will unite the expertise in fiber optics and lasers of the Photonic Systems Group at McGill University with those of O/E Land Inc. to develop a low cost and compact all-fiber Raman laser with a high-power output. Such a laser product addresses a gap in what is commercially available, and will add to their technology portfolio and give them a competitive advantage for different applications in spectroscopy, sensing and instrumentation.

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Faculty Supervisor:

Lawrence Chen

Student:

Chenglai Jia

Partner:

O/E Land

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program:

Accelerate

Virtual and Travelling Exhibitions on the History of Technology and Disability: Interdisciplinary Lessons of the Past for the Future

This project seeks to explore the historical relationship between disability, technology and society, with a focus on Canada, but with global applicability. Through rigorous secondary, primary, oral and archival research, the team will investigate historical instances of innovation, technological use and activism by and for people with disabilities, building relationships and conducting oral interviews with key actors in the development of a more accessible and inclusive society. This project will expand upon IEEE’s longstanding interests in humanitarian goals and oral histories, while at the same time provide IEEE student members with additional training and collaborative opportunities to communicate this knowledge to a broader public through developing virtual and travelling exhibits, collaborative educational workshops.

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Faculty Supervisor:

Dominique Marshall

Student:

Dorothy Smith

Partner:

IEEE Canada

Discipline:

History

Sector:

Education

University:

Program:

Accelerate

Machine Learning methods for Nova Scotia property value prediction

This project will develop and apply machine learning techniques to predict the valuation of the properties in Nova Scotia. The techniques will help Property Valuation Services Corporation (PVSC) assessors with more efficiently and accurately valuing properties. The ultimate goal is to help PVSC reduce the number of annual appeals – which is a costly undertaking. It will also reduce the need to send assessors directly to the property locations, instead they will use machine learning techniques to more accurately predict property values.

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Faculty Supervisor:

Stan Matwin

Student:

Balachandhar Nallasivan

Partner:

Property Valuation Services Corporation

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Sentiment Analysis with Parsed Representation of News Articles

Information published by financial news agencies is used as one of the inputs to make investment decisions. News articles from multiple sources can be used to gauge market sentiment towards an industry or a specific company. Deep learning techniques have been successful in producing state of the art results on various benchmark datasets (Dai & Le, 2015; Miyato et al., 2016). Most of the popular algorithms extract features from words, sentences or paragraphs and represent them as fixed-length vectors (Mikolov et al., 2013; Le & Mikolov, 2014). We propose the use of parsed representations of text along with fixed-length feature vectors as input for recurrent neural networks. The performance of these models will be evaluated on sentiment analysis tasks.

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Faculty Supervisor:

Graham Taylor

Student:

Nikhil Sapru

Partner:

RBC Financial Group

Discipline:

Engineering

Sector:

Information and communications technologies

University:

Program:

Accelerate

3D Heat-Map Development based on Fault Diagnosis Data

This work focuses on generation a framework to employ a set of 3D coordinates, as the input dataset to the model, and generate the 3D heat map based on the 3D shape. The generated 3D heatmap aims to define the most probable areas for fault categories on the 3D surface. To develop such a system, the 3D shape is printed and the 3D coordinates of simulated faults are recorded using a tool tracker. Then, a machine learning platform is employed to use the 3D fault datasets as the input and produce the probabilities of different fault categories on the given location. Finally, the 3D heat map is generated to efficiently visualize the 3D shape with the most probable areas of fault categories. Consequently, the manufacturer can localize the probabilities of fault categories on a given 3D shape.

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Faculty Supervisor:

Jonathan Wu

Student:

Eman Nejad

Partner:

Radix Inc

Discipline:

Engineering - computer / electrical

Sector:

Advanced manufacturing

University:

Program:

Accelerate

Validation of the educational impact of a holographic lecture

UBC and Microsoft intend to collaborate on an applied research project where 3D models of the brain will be used to create an interactive Holographic lecture using Microsoft’s new augmented reality device, the HoloLens. The will form the basis for a lesson or “HoloLecture,” and will feature new interactions to take advantage of the HoloLens’s technology. The ability to manipulate the 3D objects and dynamically adapt them to a live lecture format will form the basis of a HoloLecture prototype that can be applied across disciplines. The investigation and validation of this approach will include not only an evaluation of the student learning experience and their learning outcomes, but also the ease of use for instructional staff to ensure that barriers to adoption of this new technology are lowered.

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Faculty Supervisor:

Claudia Krebs

Student:

Tamara Bodnar

Partner:

Microsoft Canada

Discipline:

Biology

Sector:

Information and communications technologies

University:

Program:

Accelerate

Interactive preference elicitation application for book recommendations

Kobo is an online e-book retailer that provides recommendations for future purchases to its user base. One difficulty that recommendation systems face is what is known as the “cold-user” problem. In this scenario, when we know so little of a user’s preferences (for example, if they are new to the platform), we do not have any basis for recommendations. The goal of this project is to develop an interactive application that can elicit such preferences from users about whom we have little information, and that can help improve recommendations for power users. For new users, the preference elicitation process during onboarding can help them find books of interest much faster; for established users, it gives them the ability to refine their recommendations. Such improvements facilitate a more streamlined discovery experience.

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Faculty Supervisor:

Scott Sanner

Student:

Mary Malit

Partner:

Rakuten Kobo Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Construction of a Genetic Variant Store

This project proposes to explore and implement a method of storing and retrieving data relating to genetic variation across a population of individuals. Due to the large amount of genetic information each person possesses, such a database requires special attention to minimize the amount of data stored and to create efficient methods of accessing the data. This work will research and test different strategies to build a compact data store that will return results quickly. This data store will be incorporated into the PhenoTips software provided by Gene42 Inc. for use by hospitals specializing in genetic diseases.

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Faculty Supervisor:

Eyal de Lara

Student:

Scott Mastromatteo

Partner:

Gene42 Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Webpage customer persona discovery and push notification guidelines

Cellphones get notifications from different companies every day, but we do not know whether these notifications have a significant impact on customers’ behaviour. Knowing the impact of these notifications would provide useful insights to marketing strategists. Since user behaviour will determine the efficacy of push notifications, this project initially aims to build a behavioural model, which will group customers based on their web site navigation behaviour. Phase 2 of this project will use that behavioural model to propose strategies for using push notifications to target different customer types. Phase 3 of this project will examine the effect of the notifications and generalize to a wider range of webpage datasets.

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Faculty Supervisor:

David Campbell

Student:

Haoxuan Zhou

Partner:

Mobify Research and Development Inc

Discipline:

Statistics / Actuarial sciences

Sector:

Information and communications technologies

University:

Program:

Accelerate

Image Style Classification and Its Application on User Engagement

In this project, we will apply machine learning to perform image style classification. We will build a system that uses image style classification to increase user engagement in an eCommerce platform setting. We will study the effects of user preferences for particular image styles on their engagement with the platform.
Image style classification is the task of categorizing an image based on attributes such as composition style (e.g., minimal, geometric, etc.), atmosphere (hazy, sunny), or colour (pastel, bright). Several machine learning techniques that perform automatic image style classification have been proposed recently. We will create a new large-scale dataset of images and critically evaluate the different techniques.
We hypothesize that individual users have a consistent preference for particular image styles, and that this fact can be used to increase user engagement using an automatic image style classification system. A rigorous user study will be conducted to test this hypothesis.

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Faculty Supervisor:

Matt Medland

Student:

Thi Hai Van Do

Partner:

ContextLogic Technologies Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

NLP Techniques for Automated Entity Recognition

The primary goal of this project is to explore a variety of new and existing Natural Language Processing (NLP) techniques to improve the performance, and further the automation of, Knote’s text analysis software – specifically with entity recognition. Entity recognition is the process of identifying all groupings of words in a collection of documents that fall within that entity’s purview, such as proper names or chemical compounds. We will study the applicability of classic statistically driven approaches to classification, and evaluate the viability of newer techniques that make use of semantic encoding (such as word2vec).

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Faculty Supervisor:

Frank Rudzicz

Student:

Colton Chapin

Partner:

9636668 Canada Corp

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

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