Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

29 670 projets achevés

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Projets par catégorie

Development of machine learning and artificial intelligence based tools to improve efficiency in financial services – Year two

Our interactions with actors in the financial services industry, including our partner company, uncovered that they possess large amounts of data pertaining to investors and markets, but have yet to extract/learn information of significant value from that data such as expected actions by clients.
The industry is conscious of this, but while they are making the needful investments in IT, they report lack of academic expertise in machine learning (ML) / artificial intelligence (AI) to unlock full potentials of such investments. This project will combine academic and industrial expertise to resolve this bottleneck. We will develop ML / AI based tool to allow predictions of actions by client, specifically client churn and to help identify optimal fee structures as well as targeted populations, which Purefacts views as necessary to improve productivity and earning potential. We will also develop descriptors of accuracy of such predictions.
The feasibility of the project is assured by deep expertise of each party in respective domains: this applicant’s in applied math, coding and ML, academic supervisor’s in ML methodologies, and Purefacts’ expertise in financial services to individuals and major financial institutions.
Methods and tools developed in the project will be applicable to other industries.

Voir la description complète du projet
Superviseur du corps professoral :

Sergei Manzhos

Étudiant :

Partenaire :

PureFacts

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

Université du Québec : Institut national de la recherche scientifique

Programme :

Elevate

Development of machine learning and artificial intelligence based tools to improve efficiency in financial services

Our interactions with actors in the financial services industry, including our partner company, uncovered that they possess large amounts of data pertaining to investors and markets, but have yet to extract/learn information of significant value from that data such as expected actions by clients.
The industry is conscious of this, but while they are making the needful investments in IT, they report lack of academic expertise in machine learning (ML) / artificial intelligence (AI) to unlock full potentials of such investments. This project will combine academic and industrial expertise to resolve this bottleneck. We will develop ML / AI based tool to allow predictions of actions by client, specifically client churn and to help identify optimal fee structures as well as targeted populations, which Purefacts views as necessary to improve productivity and earning potential. We will also develop descriptors of accuracy of such predictions.
The feasibility of the project is assured by deep expertise of each party in respective domains: this applicant’s in applied math, coding and ML, academic supervisor’s in ML methodologies, and Purefacts’ expertise in financial services to individuals and major financial institutions.
Methods and tools developed in the project will be applicable to other industries.

Voir la description complète du projet
Superviseur du corps professoral :

Sergei Manzhos

Étudiant :

Partenaire :

PureFacts

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

Université du Québec : Institut national de la recherche scientifique

Programme :

Elevate

Machine Learning to Predict Temporomandibular Disorders Risk from Genotypes

The goal of this project is to develop new machine learning methods and computational strategies to mega-analyze data from well-characterized datasets on chronic pain conditions to develop a genetic predictive tool. This tool will be implemented in an online interactive dashboard and used by the Quebec Pain Research Network (QPRN) community. This collaboration with Plotly will make the developed machine learning models more accessible to applied researchers by: 1) visualizing the genetic effects which drive the predictions, 2) allowing users to interactively generate new predictions over a range of parameters and visually compare the outputs, and, 3) producing different graphics of the data to reveal details that might be hidden by summary statistics.

Voir la description complète du projet
Superviseur du corps professoral :

Sahir Bhatnagar

Étudiant :

Partenaire :

Plotly Technologies Inc

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

McGill University

Programme :

Accelerate

Idiomatic foreign function interface generation for user-specified target languages

For different software packages created using different tools to interoperate, an intermediate layer called API bindings is needed. These bindings can be created by hand, but that takes time and needs to be updated whenever one of the packages changes. Since these bindings are often quite repetitive, it is reasonable to try and generate them automatically, saving time both creating them in the first place and updating them due to changes.
There are existing tools that allow different sorts of automation in generating bindings, but these tools often make strong assumptions about what the result should look like. These results can require adapting by hand, which can be as much of a time sink as writing the bindings manually. We propose a more flexible way of generating these bindings, which aims to save time for PDFTron employees by automating more of this process for them.

Voir la description complète du projet
Superviseur du corps professoral :

Ond?ej Lhoták

Étudiant :

Partenaire :

Apryse

Discipline :

Computer science

Secteur :

Information and Communications Technology; Technology; Other

Université :

University of Waterloo

Programme :

Accelerate

Etude et optimisation des operations de mise en forme courbee de conduit

Le stagiaire devra ultiliser ses connaissances acquises dans le domaine des composites pour resoudre une problematique de l’entreprise FRE Composites (2005) inc. Celle-ci porte sur le pliage de leurs conduits de composites. La problematique et que lors pliage, il y a parfois creation de rupture du materiau et/ou de flambage local, et que le conduit a tendance a perdre son angle et rayon initial avec le temps. Le projet prend en compte les differentes phases de polymerisation du composit lors des etapes de fabrication (enroulement filamentaire, cuisson, refroidissement, prechauffage, pliage, refroidissement, post-pliage) etplus partciulierement le comportement mecanique lors de la phase de pliage. L’objectif intermediare du projet est donc de comprendre et d’identifier les parametres critiques pour l’operation de pliage d’un thermodurcissable. L’objectif final est d’implanter une nouvelle facon de plier des conduis en materiaux composites permettant d’eliminer ou de reduire les rejets, en plus d’optimiser les proprietes mecaniques……..

Voir la description complète du projet
Superviseur du corps professoral :

Radhouane MASMOUDI

Étudiant :

Partenaire :

FRE Composites Inc

Discipline :

Engineering

Secteur :

Manufacturing

Université :

Université de Sherbrooke

Programme :

Accelerate

Development and validation of an automated diagnostic tool for wound imaging – Year two

Over 6.5 million people in North America live with chronic wounds which pose a burden on their quality of life and the healthcare system. Chronic wounds are estimated to cost over $30 billion per year. Swift Medical is a pioneer in point-of-care imaging for wounds. Their mobile apps allow the reliable and accurate measurement of wound characteristics, making it an ideal tool to track healing and identify healing patterns. Using artificial intelligence/machine learning and a large database of wound data that Swift Medical uniquely possess, we propose the development of a diagnostic tool to classify wound images and its validation in an independent cohort composed of patients receiving care by Professor Gregory Berry at the McGill University Health Center. The resulting algorithms will be used by Swift to enhance the capabilities for their mobile technology, which could improve patient care by monitoring patients at high-risk of chronic wounds, such as people with diabetes or impaired mobility, promote widespread access to telemedicine in remote communities, and reduce the overall cost of chronic wound treatment to the healthcare system.

Voir la description complète du projet
Superviseur du corps professoral :

Gregory Berry

Étudiant :

Partenaire :

Swift Medical

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

Research Institute of the McGill University Health Centre

Programme :

Elevate

Development and validation of an automated diagnostic tool for wound imaging

Over 6.5 million people in North America live with chronic wounds which pose a burden on their quality of life and the healthcare system. Chronic wounds are estimated to cost over $30 billion per year. Swift Medical is a pioneer in point-of-care imaging for wounds. Their mobile apps allow the reliable and accurate measurement of wound characteristics, making it an ideal tool to track healing and identify healing patterns. Using artificial intelligence/machine learning and a large database of wound data that Swift Medical uniquely possess, we propose the development of a diagnostic tool to classify wound images and its validation in an independent cohort composed of patients receiving care by Professor Gregory Berry at the McGill University Health Center. The resulting algorithms will be used by Swift to enhance the capabilities for their mobile technology, which could improve patient care by monitoring patients at high-risk of chronic wounds, such as people with diabetes or impaired mobility, promote widespread access to telemedicine in remote communities, and reduce the overall cost of chronic wound treatment to the healthcare system.

Voir la description complète du projet
Superviseur du corps professoral :

Gregory Berry

Étudiant :

Partenaire :

Swift Medical

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

Research Institute of the McGill University Health Centre

Programme :

Elevate

Development of sc-RNA based methods for mAb identification and validation – Year two

The overall goal of this project is to develop more cost-effective methods for generating monoclonal antibodies (mAb) against cancer biomarkers. While the traditional protocols for mAb generation used by MédiMabs have been established decades ago, recent technological developments have opened the door to new, and potentially far more efficient, methodologies. In this project, we will research the use of single cell DNA sequencing as a method to rapidly identify mAb producing cells without performing traditional hybridoma fusions. At the same time, we will assess various approaches for the rapid expression and translation of sequenced clones to allow their functional validation. These experiments will be done in the context of several biomarkers that have been identified in a subgroup of pediatric acute myeloid leukemia (AML) identified by the Wilhelm lab. Because of the numerous potential advantages of this approach with respect to time and cost of mAb production, there is both a strong scientific as well as economic value underpinning the proposed work.

Voir la description complète du projet
Superviseur du corps professoral :

Brian Wilhelm

Étudiant :

Partenaire :

MediMabs

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

Université de Montréal

Programme :

Elevate

Development of sc-RNA based methods for mAb identification and validation

The overall goal of this project is to develop more cost-effective methods for generating monoclonal antibodies (mAb) against cancer biomarkers. While the traditional protocols for mAb generation used by MédiMabs have been established decades ago, recent technological developments have opened the door to new, and potentially far more efficient, methodologies. In this project, we will research the use of single cell DNA sequencing as a method to rapidly identify mAb producing cells without performing traditional hybridoma fusions. At the same time, we will assess various approaches for the rapid expression and translation of sequenced clones to allow their functional validation. These experiments will be done in the context of several biomarkers that have been identified in a subgroup of pediatric acute myeloid leukemia (AML) identified by the Wilhelm lab. Because of the numerous potential advantages of this approach with respect to time and cost of mAb production, there is both a strong scientific as well as economic value underpinning the proposed work.

Voir la description complète du projet
Superviseur du corps professoral :

Brian Wilhelm

Étudiant :

Partenaire :

MediMabs

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

Université de Montréal

Programme :

Elevate

Examining Environmental, Social and Governance (ESG) policy development in early stage-venture capital firms.

How a company today contributes to the environmental and social well-being of society is becoming a more important part of their business activities. Many large companies have an environmental, social and governance (ESG) policy which guides them on making their business activities contribute to environmental and social sustainability of the planet. But what about businesses that are just starting up? They are more worried about their ability to make enough money to survive. However, when they are able to attract funding to help them grow there is an opportunity for certain financing companies, known as startup-venture finance firms, to influence the startup business to add an ESG policy to help make their product more environmentally and socially friendly. This project will research a Toronto-based startup-venture finance company to determine if they have an acceptable ESG policy themselves and how that might influence the startup companies they have invested in.

Voir la description complète du projet
Superviseur du corps professoral :

Philip Walsh

Étudiant :

Partenaire :

ScaleUP Ventures Inc.

Discipline :

Business

Secteur :

Finance and Insurance

Université :

Toronto Metropolitan University

Programme :

Accelerate

Development of a wireless mini-microscope for the study of brain function – Year two

In the last years imaging using miniature microscopes (mini-microscopes also called mini-endoscopes) has become a method of choice for the understanding of activity at the cellular and network levels in different brain regions, in freely behaving animals. However, mini-microscopes are connected with wires to other devices, which largely alleviate the use of these imaging systems for the long-term imaging and monitoring of animals’ behavior during complex behavioral tasks. The aim of this MITACs project is to bring together Doric Lenses, who pioneered several imaging systems, and a research laboratory that performs longitudinal imaging during complex behavioral tasks on a regular basis, in order to develop, test and optimize a wireless version of the mini-microscopes. This project will have a tremendous impact from the research and development point of view for Doric Lenses, allowing for testing and optimization of wireless imaging system that is dedicated to being used in vivo, in freely behaving mice.

Voir la description complète du projet
Superviseur du corps professoral :

Armen Saghatelyan

Étudiant :

Partenaire :

Doric Lenses Inc

Discipline :

Life Sciences

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

Université Laval

Programme :

Elevate

Development of a wireless mini-microscope for the study of brain function

In the last years imaging using miniature microscopes (mini-microscopes also called mini-endoscopes) has become a method of choice for the understanding of activity at the cellular and network levels in different brain regions, in freely behaving animals. However, mini-microscopes are connected with wires to other devices, which largely alleviate the use of these imaging systems for the long-term imaging and monitoring of animals’ behavior during complex behavioral tasks. The aim of this MITACs project is to bring together Doric Lenses, who pioneered several imaging systems, and a research laboratory that performs longitudinal imaging during complex behavioral tasks on a regular basis, in order to develop, test and optimize a wireless version of the mini-microscopes. This project will have a tremendous impact from the research and development point of view for Doric Lenses, allowing for testing and optimization of wireless imaging system that is dedicated to being used in vivo, in freely behaving mice.

Voir la description complète du projet
Superviseur du corps professoral :

Armen SAGHATELYAN

Étudiant :

Partenaire :

Doric Lenses Inc

Discipline :

Life Sciences

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

Université Laval

Programme :

Elevate

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