Innovative Projects Realized

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

31133 Completed Projects

2940
AB
5159
BC
837
MB
685
NL
882
SK
9292
ON
9695
QC
97
PE
601
NB
1161
NS

Projects by Category

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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Mitacs — Return on Innovation

Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.