Innovative Projects Realized

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

29670 Completed Projects

2811
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4990
BC
801
MB
663
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825
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8841
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9197
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95
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568
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1088
NS

Projects by Category

Compound recommendation for plant health using machine learning and computational chemistry

Virtual screening is a computational technique used in drug discovery to search large libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme. Virtual screening is thought to have the potential to speed the rate of discovery by reducing the need for expensive and time-consuming lab tests to physically test thousands of diverse compounds, often with an expected hit rate on the order of 1% or less with still fewer expected to be real leads following further testing. The proposed project aims to develop computational models that would screen through large libraries of chemical compounds and recommend those with potential efficacy against desired indications in plants and crops. The models will be developed using data collected by Terramera, with high classification accuracy, according to threshold tolerances defined by Terramera scientists.

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Faculty Supervisor:

Martin Ester

Student:

Partner:

Terramera Inc

Discipline:

Computer science

Sector:

Agriculture; Manufacturing; Professional, scientific and technical services

University:

Simon Fraser University

Program:

Accelerate

Anomaly Detection in Land Vehicle Traffic Activity

This project’s objective is to develop a capability to detect and describe anomalous situations in ground vehicle traffic. Anomalous situations are described as substantial/important changes from the traffic frequently observed for a particular route and/or time. In this sense, anomaly can be quantitatively measured by the degree of predictability of current traffic given historical observations. In the use case of interest, information from traffic will be captured from a GMTI sensor performing recurrent surveillances (1-3 hours per day, multiple days per week) over the same area. By developing such capability, Thales wishes to create a new service offer (based on existing but still un tapped historical data): a decision support capability for GMTI analysts that will draw their attention on suspicious activities that would normally be unnoticed. This project does not deal with raw data processing or tracking problems, but uses vehicles tracks as input data.

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Faculty Supervisor:

Lijun Sun

Student:

Partner:

Thales Canada Inc

Discipline:

Engineering

Sector:

Information and Communications Technology; Technology; Transportation (excluding aerospace)

University:

McGill University

Program:

Accelerate

Stories without borders: A Canadian/Mexican study of translation practices and Indigenous language literacy

“Stories Without Borders” is a collaborative educational project between a Canadian and a Mexican research team, with the support of the Canadian NGO Education without Borders (EwB). The research problem addressed is: How can children’s stories written in English and other Indigenous languages for Indigenous children in Canada best be translated into Indigenous Mexican languages for Indigenous children in Mexico? The study addresses both translation challenges and solutions described by Mexican translators of 7 Indigenous languages in the process of translating 12 children’s stories available on the Canadian open access site: Indigenous Storybooks, found at (http://indigenousstorybooks.ca/). The Mitacs intern, UBC PhD candidate Liam Doherty, will develop a translation app for the project, and help with data collection, coding, and analysis of the qualitative data. There will be 84 new translations available on the Indigenous Storybooks site, and a publication in preparation. TO BE CONT’D

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Faculty Supervisor:

Bonny Norton

Student:

Partner:

Education without Borders

Discipline:

Sociology

Sector:

Education

University:

The University of British Columbia

Program:

Accelerate

Méthodologie d’identification des facteurs et schémas responsables de la baisse de productivité

À l’ère de ‘l’industrie 4.0’ qui prône le numérique et la connectivité, la gestion et l’exploitation des connaissances fournissent un avantage significatif dans un contexte manufacturier toujours plus concurrentiel. Cela permet le transfert de connaissances, la formation, la résolution de problèmes complexes ainsi que la prédiction de phénomènes. Les données de production peuvent provenir de sources variées (contrôleur machine, capteurs installés sur la machine, contrôles qualités, opérateurs, etc). Ces données contiennent des informations et des connaissances qui, si elles étaient collectées et traitées adéquatement, pourraient être exploitées et intégrées dans un système de fabrication pour améliorer la prise de décision et améliorer la productivité. Le projet de recherche proposé vise à développer une méthodologie de collecte, d’exploration et d’exploitation des données de production pour permettre d’identifier les facteurs et les schémas responsables de la baisse de productivité parfois observée.

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Faculty Supervisor:

Christophe Danjou

Student:

Partner:

APN Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

École Polytechnique de Montréal

Program:

Accelerate

Clustering Generating Mechanisms in a Mixture of Additive Noise Models

Pattern analysis is the study of patterns in observed data. One important question in pattern analysis is to find and group similar objects together, e.g., those people with cardiovascular diseases among a set of volunteers willing to donate an organ. This frequently-encountered problem in the domain of data analysis is referred to as classification and clustering with some differences between the two. Despite being common, the problem still carries many aspects that are not well established and need to be dug further. In the current project, we study one of the relatively less discussed features of the clustering problem. That is, instead of grouping the data points based only on the observable values of some features, we allow for the possible associations among the features of a single object as well. TO BE CONT’D

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Faculty Supervisor:

Francois Soumis

Student:

Partner:

Huawei Technologies Canada Co Ltd (Montreal, QC)

Discipline:

Mathematics

Sector:

Technology; Information and Communications Technology; Other

University:

École Polytechnique de Montréal

Program:

Accelerate

Identifying microRNA expression profiles and biomarkers in peripheral whole blood in patients with chronic obstructive pulmonary disease

Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death in Canada and may affect up to as many as 3 million Canadians. However, due to inadequate diagnostic tools, 50% remain undiagnosed. Furthermore, COPD patients also suffer attacks, or exacerbations, which can hospitalise the patient, worsen their condition and take weeks to recover from. Exacerbations are difficult to predict and it is even harder to identify which patients are more susceptible to repeated attacks. I will investigate a group of small molecules, microRNAs, in patient blood, in order to identify which specific miRNAs are associated with COPD exacerabations and to identify whith patients are more susceptible to repeated exacerbations. Ultimately these findings will allow researchers a better insight into the mechanisms of COPD exacerbations, which in turn will lead to improved predictive and diagnostic tools and allow doctors to tailor patient management and reduce future lung attacks.

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Faculty Supervisor:

Scott Tebbutt

Student:

Partner:

St Paul's Hospital

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology

University:

The University of British Columbia

Program:

Accelerate

Exploring Deep Learning Architectures for Automatic Casting from Movies

Automatic casting applications aim is to accurately recognise facial regions that correspond to a same actor appearing in a movie to produce described video. This project will focus on the tasks of re-identify the face of each principal actors when they appear in different scenes of a movie. This is a challenging task because although recent movies are typically high resolution, the faces are often occluded and their appearance varies significantly according to pose, scale, illumination, blur, etc. This project will focus on developing and evaluating convolutional neural network (CNN) architectures that are suitable for accurate face re-identification in automatic casting applications. Deep learning architectures have recently been shown to provide a significantly higher level of accuracy compared to conventional methods on many challenging visual recognition problems. However, these architectures are complex, and the unlabelled facial trajectories captured in a movie provide a limited reference data to adapt or fine-tune CNNs. TO BE CONT’D

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Faculty Supervisor:

Éric Granger

Student:

Partner:

Centre de recherche informatique de Montréal (CRIM)

Discipline:

Computer science

Sector:

Information and Communications Technology; Technology; Entertainment and Media

University:

École de technologie supérieure

Program:

Accelerate

Study clay deposits within the Northern Rockies Regional Municipality (Fort Nelson area) for industrial applications

The town of Fort Nelson has been hugely impacted economically with the drop in oil prices and cancellation of west-coast LNG export. Local companies and entrepreneurs are looking for other natural resources in an attempt to save the region’s economy. Mindbody Networks Inc. has access to a variety of natural clay deposits distributed within the area that hold huge economic potential. In this research project, the suitability of this clay deposit for different industrial applications will be evaluated. The clay mineral will be sampled and then analyzed for its physical and chemical characteristics using different instrumental techniques. Physical, thermal and chemical treatment will be used to purify the clay. Properties of the natural and modified (treated) clay samples will be tested in the context of evaluating their potential for commercial applications including but not limited to environmental processes, oil/gas industries and cosmetic products.

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Faculty Supervisor:

Hossein Kazemian

Student:

Partner:

Mindbody Networks Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Northern British Columbia

Program:

Accelerate

Evaluation of neuro-protective effects of novel water-soluble formulation of Ashwagandha root extract alone and in combination with water-soluble Ubisol Q10 using in vitro and in vivo models of Parkinson’s disease

There is an increase in brain diseases like Alzheimer’s and Parkinson’s diseases as the number of aged individuals is increasing. Currently there is no treatment available that can halt the progression of these diseases. Previous work has shown that a natural compound CoQ10 can prevent brain cell death. Similarly an Ayurvedic Herbal extract, Ashwagandh Root Extract has also shown promising results in some studies. In the present research project we will prepare a novel water-soluble formulation of CoQ10 and Ashwagandha Extract using a patented technology of Nextremedies Inc. We will test this novel formula in cellular (in petri dishes) and animal models of Parkinson’s disease to find out if it can protect brain cells and halt the progression of Parkinson’s disease. Successful completion of this project will lead to the development of a natural neutraceutical formula that can be used to treat Parkinson’s disease.

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Faculty Supervisor:

Siyaram Pandey

Student:

Partner:

Next Remedies Inc

Discipline:

Life Sciences

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Windsor

Program:

Accelerate

Kali: Optimizing Resource Utilization in Distributed Clusters

Decreasing operational costs is a key criterion for organizations that manage compute clusters, such as Amazon, Microsoft, Google, Alibaba, etc. One way to decrease costs it to improve resource utilization in the cluster [13, 14]. Yet, high resource utilization can negatively affect workload performance and thus user satisfaction. Performance degradation happens when workloads running on the same machine “compete” for shared resources, e.g., a workload that consumes a large portion of memory delays execution of other, memory-intensive workloads. Such “competition” for resources is referred to as resource interference in the literature.
Existing work on predicting and avoiding interference mainly relies on (a) stress-testing the workloads before scheduling, to estimate their constraints and (b) extracting interference-related constraints while observing real executions. TO BE CONT’D

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Faculty Supervisor:

Julia Rubin

Student:

Partner:

Samsung Research and Development Canada (SRCA)

Discipline:

Computer science

Sector:

Technology; Information and Communications Technology

University:

The University of British Columbia

Program:

Accelerate

Addressing homelessness in Prince Albert and surrounding community: A public policy and program evaluation approach

The proposed research project is a follow-up to the SSHRC funded Partnership Engage study entitled: “Addressing homelessness in Prince Albert: A multi-disciplinary, intersectoral approach. The purpose of the SSHRC study is to enhance the ability of PA to respond to homelessness by stimulating discussions and mutual collaboration between academic researchers, community leaders, social agencies, front-line workers, and the homeless population. The current Mitacs funded project will build upon the SSHRC study by providing a secondary analysis of project results from a public policy framework and conducting a formative evaluation of the Homeward Bound Housing First initiative. These reports will be valuable to YWCA in leveraging future funds, advocating to policy makers, and improving their own services. The intern will work closely with YWCA personnel to ensure the project meets their needs and reflects their interests at all stages.

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Faculty Supervisor:

June Anonson

Student:

Partner:

YWCA Prince Albert

Discipline:

Sociology

Sector:

Health and Related Sciences & Technology

University:

University of Saskatchewan

Program:

Accelerate

Development of New Chromophores for Soil Detection on Surgical Instruments

Small surgical instruments can be used multiple times if cleaned properly after each surgery. At the end of the cleaning process, a visual inspection of each instrument is performed in order to detect any traces of soil such as blood, tissue or any other biological materials. However, the human eye is not a flawless sensor and soils that are not visible to the naked can be present. The objective of this project is to develop new chromophores that could bind selectively to biological materials and emit light in a specific region of the electromagnetic spectrum, allowing quantitative analysis of the soil in an ambient environment without the need for complex setups that are difficult to operate. This technology will give STERIS a significant edge over the competition as no other reliable method for quantitative analysis has been commercialized yet.

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Faculty Supervisor:

Jean-François Morin

Student:

Partner:

STERIS Canada Corporation

Discipline:

Physics

Sector:

Manufacturing; Professional, scientific and technical services

University:

Université Laval

Program:

Accelerate

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