Innovative Projects Realized

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

30156 Completed Projects

2861
AB
5059
BC
812
MB
673
NL
842
SK
8957
ON
9368
QC
96
PE
579
NB
1120
NS

Projects by Category

Machine Learning for Improved Automated Valuation Model (II)

To estimate the market value of a real estate, a computer software program can produce the result by analyzing the location, market conditions, and other characteristics relevant to it. This is known as the “automated valuation model (AVM)”. The previous Mitacs Accelerate project with “Data Nerds” has conducted a series of experiments with the attributes of real estates and geographic dependencies. This project is to further develop the AVM by incorporating temporal factors. The deep learning based approaches will be employed in this project. This development work will enable “Data Nerds” to achieve more accurate estimation of the real estate market by using all relevant data and information.

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Faculty Supervisor:

Zheng Liu;Eric Li

Student:

Partner:

Data Nerds

Discipline:

Engineering

Sector:

Information and cultural industries

University:

University of British Columbia - Okanagan

Program:

Accelerate

Modélisation numérique de scénarios de restauration du parc àrésidus A du site Manitou

La gestion des rejets et stériles miniers constitue un défi grandissant auquel doit faire face le monde minier. Les ouvrages de restauration doivent être conçus pour résister dans le temps à court, moyen et long termes faces
aux instabilités physiques, chimiques ou climatiques. Le site Manitou, ancienne mine de cuivre et de zinc, a longtemps été problématique pour l’environnement. Les résidus miniers ont été abandonnés sans aucune considération environnementale pendant plus de 20 ans. Partenaire d’une entente-cadre avec le Ministère de l’Energie et des Ressources Naturelles (MERN), les Mines Agnico Eagle sont depuis 2006 responsables en partie de la gestion du site Manitou. Le présent projet de stage s’inscrit dans ce vaste programme de restauration minière et a pour but d’étudier la performance du nouveau recouvrement testé sur le parc à résidus. TO BE CONT’D

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Faculty Supervisor:

Bruno Bussière

Student:

Partner:

Agnico Eagle Mines Limited

Discipline:

Engineering

Sector:

Mining

University:

Université du Québec en Abitibi-Témiscamingue

Program:

Accelerate

Universal Data logger using SBC-2800 motherboard

Data collection, recording, and monitoring meteorological, hydrological data is very critical worldwide. Metrological/Hydrological stations usually have a variety of sensors and nstruments whick are often manufactured by different companies that are set to work with other products of those manufacturers. However, each manufacturer indirectly prefers to set each sensor/instruments with its only other products. This may restrict required flexibility for users to enjoy from mixed design of sensors/instruments to get optimized design for their station. Therefore, a new integrated system is needed with ability to record required data for various environmental monitoring stations and to send those aggregated information on line to a main station for display and further analysis. In this proposal, it is desired to develop such an environmental monitoring system integrator.

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Faculty Supervisor:

Javad Alirezaie

Student:

Partner:

Felix Technology

Discipline:

Engineering

Sector:

University:

Toronto Metropolitan University

Program:

Accelerate

A Context Aware, Lightweight, and Adaptive Authentication and Authorization

With increasing security risks in critical network infrastructures and emerging cloud technologies with shared capabilities, as well as increasing regulatory requirements on privacy, and data protection, there is a growing need for new approaches to manage security and privacy compliance. The main related challenges are (a) context-aware and fine-grained security enabling efficient real-time enforcement of authentication and authorization (A&A) and federation of security services between the global ICT providers, service providers and the connected devices, (b) optimized and adaptable encryption services to reach end to end security between service providers and connected devices. Ericsson believes the above challenges represent a promising opportunity to rethink the design of security solutions towards a novel generation of enforcement mechanisms that are autonomous, context-aware, and extendable. TO BE CONT’D

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Faculty Supervisor:

Chamseddine Talhi

Student:

Partner:

Ericsson Canada Inc (Montreal, QC)

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

École de technologie supérieure

Program:

Accelerate

Évaluation de l’érodabilité hydraulique dans les canaux d’évacuation de crues excavés dans le roc

Les barrages sont équipés d’évacuateurs de crue, dont le canal d’évacuation est la plupart du temps excavé dans le roc. L’écoulement intensif de l’eau peut conduire à l’érosion du roc, qui peut potentiellement affecter la stabilité des ouvrages hydrauliques. Afin d’assurer la pérennité de ses ouvrages hydrauliques, la compagnie Hydro-Québec souhaite disposer d’une méthode fiable permettant la prévision de l’érodabilité hydraulique du roc aux niveaux de ses évacuateurs de crue. Ceci permettra d’une part de mieux assurer la sécurité de la population et le paysage environnants, et d’autre part, assurer la production d’électricité au Québec.

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Faculty Supervisor:

Ali Saeidi

Student:

Partner:

Hydro-Quebec

Discipline:

Earth science

Sector:

Natural Resources; Energy and Utilities

University:

Université du Québec à Chicoutimi

Program:

Accelerate

The intelligent virtual agent as a patient Personalized companion

In this project the intern will develop an intelligent virtual concierge and integrate it with Curatio’s social network platform. The intelligent concierge will learn about each user over time and act as a personal assistant of the user in using the social network platform.

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Faculty Supervisor:

Russell Greiner

Student:

Partner:

RxPx Health

Discipline:

Engineering

Sector:

Information and Communications Technology; Health and Related Sciences & Technology; Life Sciences (not health)

University:

University of Alberta

Program:

Accelerate

Vulnérabilité des populations de crevette nordique aux changements climatiques et globaux le long de la côte Est du Canada

La crevette nordique est une des plus importantes espèces exploitées à l’est du Canada, moteur d’approvisionnement et de développement pour de nombreuses communautés côtières. Depuis quelques années, les stocks de crevette nordique semblent en déclin. Le réchauffement, l’acidification et la désoxygénation des océans pourraient venir d’autant plus affecter la viabilité et la rentabilité de cette pêcherie. Cependant la vulnérabilité relative des différentes populations n’est que peu connue et ne permet pas de prédire l’évolution globale de cette pêcherie dans un contexte de changements globaux. En menaçant la ressource, ces changements pourraient à moyen et long terme impacter par extension l’activité économique de la pêche et la vitalité socio-économique de plusieurs régions atlantiques. TO BE CONT’D

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Faculty Supervisor:

Piero Calosi;Fanny NOISETTE;Marco ALBERIO;William Cheung

Student:

Partner:

Ouranos Inc;Merinov (Rimouski, QC)

Discipline:

Life Sciences

Sector:

Accommodation and food services; Agriculture; Professional, scientific and technical services; Public administration

University:

Université du Québec à Rimouski

Program:

Accelerate

Evaluation of targeted alpha-therapy on patient-derived Glioblastoma cells

Glioblastoma multiforme (GBM) is the deadliest form of human brain tumors, systematically recurring despite multimodal treatment. As a consequence, the average patient survival is less than 15 months, and is thought to be linked with the presence of brain tumor stem cells (BTSCs) that are implicated in treatment resistance. GBM BTSCs are radiotherapy and chemotherapy resistant, and BTSCs escaping treatment may explain tumor relapse. In the current era of precision medicine, targeted radiotherapy constitutes an attractive strategy for treating intractable disease. By combining the selectivity of immunotherapies with the proven lethality of radio-isotopes we have the opportunity to deliver irreversible damage to a defined cancer cell population while sparing normal tissue. We have developed an in vivo mouse-model that has the distinct advantage of generating recurrent, human, treatment-refractory GBM. TO BE CONT’D

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Faculty Supervisor:

Sheila Kumari Singh

Student:

Partner:

Longbow Therapeutics Inc

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

McMaster University

Program:

Accelerate

Enhanced Techniques for History Matching and Forecasting of Petroleum Reservoir Data – Year Two

History matching refers to calibrating numerical or analytical models by the observed data. However, this task can be very challenging in presence of complex geology and/or many unknown data .
The purpose of this project is to introduce and apply the new techniques for efficient creation of predictive history-matched models for reservoir characterization of conventional and unconventional reservoirs, which can be used for probabilistic forecast and uncertainty quantification. It is expected to implement as set of code and introduce new workflows that can enhance the history matching task in various problems. This include the use and applications of the state-of-the-arts methods that can represent the geology and can efficiently and accurately calibrate the dynamic models by minimizing the computational cost.
This postdoctoral program provides a unique opportunity to further my studies in history matching and uncertainty quantification to a new level within Rock Flow Dynamics (RFD). This project helps me utilize interactions with industry and receive industrial feedback on the practicality of my algorithms.

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Faculty Supervisor:

Mario Costa Sousa

Student:

Partner:

Rock Flow Dynamics Inc;University of Calgary

Discipline:

Earth science

Sector:

Professional, scientific and technical services

University:

University of Calgary

Program:

Elevate

Enhanced Techniques for History Matching and Forecasting of Petroleum Reservoir Data

History matching refers to calibrating numerical or analytical models by the observed data. However, this task can be very challenging in presence of complex geology and/or many unknown data .
The purpose of this project is to introduce and apply the new techniques for efficient creation of predictive history-matched models for reservoir characterization of conventional and unconventional reservoirs, which can be used for probabilistic forecast and uncertainty quantification. It is expected to implement as set of code and introduce new workflows that can enhance the history matching task in various problems. This include the use and applications of the state-of-the-arts methods that can represent the geology and can efficiently and accurately calibrate the dynamic models by minimizing the computational cost.
This postdoctoral program provides a unique opportunity to further my studies in history matching and uncertainty quantification to a new level within Rock Flow Dynamics (RFD). This project helps me utilize interactions with industry and receive industrial feedback on the practicality of my algorithms.

View Full Project Description
Faculty Supervisor:

Mario Costa Sousa

Student:

Partner:

Rock Flow Dynamics Inc;University of Calgary

Discipline:

Earth science

Sector:

Professional, scientific and technical services

University:

University of Calgary

Program:

Elevate

Exploring optimal trading rules in a high-frequency portfolio

Given a set of financial instruments with inherent characteristics at different time intervals, we are interested in finding an optimal trading rule in a high-frequency trading context. A trading rule is defined as a combination of indicators as well as an entry threshold (and potentially other trading parameters). The objective function we are trying to maximize is the profits of the strategy based on the trading rule. One impact of the non-linearity of such problems is that the gradient of the objective function is hard to estimate using a black-box approach. High frequency tick data are used so a high volume of samples is available and this fact compounds the problem. In this project, we set to explore different methods to perform an efficient random search such as the use of low discrepancy sequences, simulated annealing and evolutionary procedures. TO BE CONT’D

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Faculty Supervisor:

Manuel Morales

Student:

Partner:

Squarepoint Technologies

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

Université de Montréal

Program:

Accelerate

Automated transaction classification using machine learning algorithm

The procurement process of an organization is key to understand company costs. Organizations gather large amounts of data coming from different sources (e.g. income statement, balance sheet, general ledger lines). This information is heterogeneous in nature as it is a mix of unstructured and structured data. Moreover, it needs to be cleaned and consolidated in a taxonomy to enable category management. The objective is to group like-to-like items and/or services into categories from Supply Market Analysis point of view and consider category management for the holistic spend. Supervised and unsupervised machine learning algorithm seemed to be natural choices for this kind of problem because of the nature of the available data. PwC has already a first iteration of a classification product, dubbed SAM (Spend Analysis Machine) and it is based on supervised learning for text classification on general ledger accounts and supplier characteristics. TO BE CONT’D

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Faculty Supervisor:

Maciej Augustyniak;Manuel Morales;Manuel Morales;Maciej Augustyniak

Student:

Partner:

PwC Management Services LP

Discipline:

Mathematics

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

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