Innovative Projects Realized

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

30508 Completed Projects

2882
AB
5105
BC
825
MB
681
NL
860
SK
9051
ON
9491
QC
97
PE
586
NB
1141
NS

Projects by Category

Violacein effects in mice Rho 264.7 cells

Violacein is a molecule naturally produced in some bacteria such as Chromobacterium violaceum. It has been shown that violacein has promising antibiotic, anti-tumor proliferation, anti-leishmanial and anti-fungal properties. Violacein is commercially available. However, it is very expensive. The objective of this proposed research study is to measure the effects of violacein produced by Escherichia coli on mice Rho 264.7 cells. Over a four month period the intern will use biotechnology techniques to accomplish the objective of this project. When this project is finish, there is an expected benefit to Synbiota Inc. as the software tools, as well as storage and management of the associated scientific data were developed and are maintained by Synbiota Inc. Moreover, the reagent kit used to generate the violacein producing E.coli was co-created by Synbiota Inc. and partner Genomikon Inc. Further characterization of the violacein producing E.coli as well as the efficacy of violacein is of importance for quality control metrics and validation that the reagent kit holds value in the research community. The results of this project will be of value for Synbiota Inc. assisting in the development and marketing of the company’s products.

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Faculty Supervisor:

Jeffrey Fillingham

Student:

Partner:

Synbiota

Discipline:

Life Sciences

Sector:

Biotechnology; Life Sciences (not health); Pharmaceuticals

University:

Toronto Metropolitan University

Program:

Accelerate

Machine Learning developer intern working with Financial Services in the private sector to develop and commercialize AI-powered solutions (1)

AltaML builds artificial intelligence (AI)-enabled solutions to business problems. We work with organisations, bringing together their data and domain expertise with our AI expertise, to develop AI solutions that are deployed in their operations. We also commercialize AI-enabled products business via industry-specific ventures, yielding scalability from our investment in the first solution. Competition for tech talent is fierce, and our talent strategy includes a talent accelerator program, designed to rapidly equip highly qualified individuals with hands-on work experience in applied AI while providing partners with continuous and cost-effective development of AI solutions. AltaML’s AI Lab for Government, also known as GovLab, is a talent accelerator for public service professionals, post-secondary students and recent graduates. GovLab.ai’s mission is to set a global example of how to transform the public sector through applied AI, and is designed to encourage the growth of technical and business AI skill sets that are in high demand across Alberta and around the world. The project comprises internships in a variety of technical and business roles within our organization and within our GovLab program. Within the organization, roles include associate machine learning developer, business development associate, communications associate and finance associate. Within GovLab specifically, roles include associate machine learning developer, associate business solutions consultant, and project delivery associate. The difference is mainly that in GovLab, there is a focus on public sector problems, whereas in AltaML overall, we work across sectors.

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Faculty Supervisor:

Carlos Cruz Noguez

Student:

Partner:

AltaML

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Alberta

Program:

Business Strategy Internship

Unconventional quantum annealing in many-body open quantum systems

Quantum computers promise significant advantages in solving complex problems currently intractable for classical computers by leveraging principles of quantum physics such as superposition, entanglement, and tunneling to perform operations on data. One well-known approach to quantum computation is quantum annealing, pioneered by D-Wave Systems Inc., which develops quantum annealing processors to tackle optimization and sampling problems. Recently, D-Wave has advanced its capabilities to perform coherent annealing within short periods of time constrained by the interaction between the processor and the thermal environment. This limited time may not be sufficient to achieve low-energy solutions in a single forward annealing process. In this project, we try to mitigate this shortcoming by iterative or cyclic quantum annealing protocol, a combination of forward and reverse annealings to reduce the residual energy in each cycle. A crucial aspect of this protocol is that coherence within the system is required primarily during a single cycle. Between cycles, the system remains in a deeply glassy phase, where the qubits are frozen. Consequently, cycles can be repeated well beyond the thermal relaxation time without substantial thermal excitation. We use and develop iterative evolution methods with and without biases: the former linked to bang-bang protocol and continuous-time quantum walk, the latter to iterative quantum optimization. We also utilize advanced computational techniques, including tensor network methods and entanglement measures, to further investigate such an open quantum many-body system. This project aims to benefit D-Wave by developing new quantum algorithms that enhance the performance and efficiency of its quantum processors.

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Faculty Supervisor:

Igor Herbut

Student:

Partner:

D-Wave Systems Inc.

Discipline:

Physics

Sector:

Manufacturing; Professional, scientific and technical services

University:

Simon Fraser University

Program:

Elevate

Virtual Home Staging with Generative Artificial Intelligence (GenAI)

This proposal aims to revolutionize the process of virtual home staging by using advanced technology called Generative Artificial Intelligence (GenAI). Traditional staging, which involves professionals arranging furniture and decorations in a home to make it more attractive to buyers, can be expensive and time-consuming. By harnessing GenAI, we can speed up this process and reduce costs significantly. However, current methods lack the ability to remove unwanted objects, replace colors, textures, and styles. This project plans to address these challenges by developing new AI models that can accurately perform these tasks. The goal is to create a virtual home staging system that is not only fast and cost-effective but also produces high-quality results, benefiting users including any homeowners, designers, and real estate agents.

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Faculty Supervisor:

Di Niu

Student:

Partner:

Greentown Homes

Discipline:

Computer science

Sector:

Construction and infrastructure; Real estate and rental and leasing

University:

University of Alberta

Program:

Accelerate

A probabilistic cluster expansion based approach for predicting synergism of mixtures of compounds in treating cancers

Combination therapy has been one of the cornerstones for combating cancers. Even though the survival rate of cancer patients after receiving combination therapy is improved dramatically, there are still many unsolved issues to prevent us from claiming combination therapy is a cure for cancers. Those issues include it does not apply to all types of cancers; there are increased risks of side effects, and it requires long developmental time and huge costs. One approach to resolve these issues is applying multiple-compound multiple-target paradigm directly during discovery processes, which exactly is the backbone of the partner organization’s proprietary drug discovery technology. Yet the technology lacks an appropriate and efficient way to predict the effects of combinations. Therefore, the proposal aims not only to improve this technology but also resolve issues in combination therapy.

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Faculty Supervisor:

Jack Tuszynski

Student:

Partner:

SinoVeda Canada Inc

Discipline:

Physics

Sector:

Agriculture; Manufacturing; Professional, scientific and technical services

University:

University of Alberta

Program:

Elevate

Addressing Endogeneity in count data using a two-stage copula generated regressor approach

This research project addresses a common problem in studying medical data: understanding the true effects of different treatments when the data is not from a controlled experiment. Specifically, we are looking at patients with systemic lupus erythematosus (SLE) and the number of infections they get. Often, traditional methods need special, hard-to-find data to ensure accurate results. Our new method skips this requirement by using advanced statistical techniques to account for unmeasured factors affecting the results. By applying this method to SLE patient data, we aim to see how Glucocorticoid influences infection rates. This research could lead to better treatment strategies, improving patient care. The partner organization will benefit from a practical and widely applicable tool to analyze complex medical data more accurately, even when ideal data conditions are not met.

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Faculty Supervisor:

Hui Xie

Student:

Partner:

Arthritis Research Canada

Discipline:

Mathematics

Sector:

Professional, scientific and technical services

University:

Simon Fraser University

Program:

Accelerate

“But…I don’t feel recovered”: Enhancing concussion care in partnership with youth who have experienced ‘prolonged recovery’

Youth concussion rates are rising nationally. Accordingly, efforts are being made to enable the recovery of injured youth. However, little is known about what recovery ‘is’ from the perspective of youth (eg, the care youth need to support recovery). This represents a major gap in knowledge of youth concussion. This research project will partner with youth living with ‘prolonged’ recovery to produce urgently needed insights into their concussion care experiences. It will also refine a research design that leverages the expertise of youth to inform a more supported approach to concussion care that attend to diverse visions of recovery. In the context of a film screening event, 8-10 participants (15-24 years) living with concussion (=4 weeks) will view short films of ‘recovery’ produced by youth in a related project. Participants will then engage in a focus group to explore their reactions to the films, their own care-related experiences, and how the care they received did (or did not) meet their priorities. The knowledge generated is expected to contribute to a (re)visioning of care that accounts for diverse experiences of ‘recovery’. Outputs will include an actionable list of recommendations for youth-informed concussion care and plain language summaries that will be shared widely (with youth, families, clinicians, general public), and housed on the partner organization’s website. Longer term, this research has the potential to reorient concussion care to the real-world needs and priorities of young Canadians. This research will affirm Holland Bloorview as a leader in youth-centered concussion research, supports, and services.

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Faculty Supervisor:

Gail Teachman

Student:

Partner:

Holland Bloorview Kids Rehabilitation Hospital

Discipline:

Sociology

Sector:

Health and Related Sciences & Technology; Retail trade

University:

The University of Western Ontario

Program:

Elevate

Prévision de la charge électrique dans un système de contrôle en boucle fermée

La prévision de la charge, ou de la demande électrique, est une activité fondamentale pour les utilités électriques et en particulier pour Hydro-Québec. Dans un contexte opérationnel, elle vise à prévoir sur un horizon de temps relativement court, typiquement 24 ou 48 heures la quantité d’électricité qui sera consommée par un ensemble de clients donnés. Ce problème a été étudié depuis longtemps sous plusieurs angles et une grande variété de solutions pour le résoudre ont été proposées. Lorsqu’un modèle produit des prévisions, celles-ci peuvent ensuite être utilisées afin de prendre différentes décisions opérationnelles : démarrer ou arrêter des générateurs, augmenter ou diminuer les transactions d’énergie sur les marchés voisins, accélérer ou retarder l’entretien planifié d’équipements, charger ou décharger des stocks d’énergie, ou encore recourir à l’effacement d’une charge flexible pendant une période donnée. Ce projet de recherche vise à concevoir un module autonome de prévision de la demande dans un contexte en boucle fermée, c’est-à-dire un agent dont le comportement évolue en fonction des conséquences de la prise de décisions opérationnelles. Le module devra entre autres gérer le cycle de vie des modèles de prévision disponibles et s’adapter à des contextes changeants en continu.

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Faculty Supervisor:

Eric Beaudry

Student:

Partner:

Institut de Recherche Hydro-Québec;Hydro-Quebec

Discipline:

Computer science

Sector:

Energy and Utilities; Information and Communication Technology; Artificial Intelligence

University:

Université du Québec à Montréal

Program:

Accelerate

UI-Copilot: developing large multimodal models for open-ended web navigation

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW.

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Faculty Supervisor:

Reihaneh Rabbany

Student:

Partner:

ServiceNow Canada

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

LeddarTech : Simulation basée sur les données pour la conduite autonome

Les enjeux de données, collection et annotation, sont des points clés de l’industrie des véhicules autonomes et des systèmes d’aide à la conduite. La simulation apprise depuis les données, sans passer par une modélisation de l’environnement, est une technologie à haut potentiel pour toute l’industrie. L’enjeu principal reste la séparation du background foreground, permettant la composition de scénarios difficilement enregistrables dans le monde réel.
Ces deux sujets de stages se concentrent sur deux points essentiels, les séparations des objets par rapport au fond statique, et la réinsertion d’objets appris dans un background de notre choix. En effet, aujourd’hui de nombreux artéfacts de reconstructions ne permettent pas une qualité d’image suffisamment haute.

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Faculty Supervisor:

Christian Gagné;Jean-François Lalonde

Student:

Partner:

LeddarTech Inc

Discipline:

Computer science

Sector:

Manufacturing

University:

Université Laval

Program:

Accelerate

Jumine – Détection des anomalies sur les convoyeurs

Le transport du minerai par convoyeur dans les mines est crucial pour l’efficacité et la rentabilité. Les convoyeurs nécessitent une préparation préalable du minerai par concassage pour améliorer la durée de vie de la bande, la fiabilité du système et réduire les coûts. Comparés aux camions, les convoyeurs présentent un avantage énergétique grâce à leurs moteurs électriques à haut rendement. En collaboration avec les Mines Agnico Eagle et le Corem, Jumine a lancé OptimaVue – Flottation, qui utilise une caméra pour détecter les anomalies à la flottation et développent maintenant OptimaVue – Convoyeur, pour détecter en temps réel les corps étrangers sur les convoyeurs des mines. Ceux-ci risquent d’endommager les équipements, de bloquer la chaîne de valeur et de poser des risques pour la sécurité des opérations minières. Le système utilise les caméras déjà en place chez les clients. Ce projet est soutenu financièrement par quatre mines, le groupe MISA et par Jumine.

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Faculty Supervisor:

Christian Gagné;Philippe Giguère

Student:

Partner:

Jumine Inc

Discipline:

Computer science

Sector:

Mining; Professional, scientific and technical services

University:

Université Laval

Program:

Accelerate

Opportunités durables dans la bio-économie basée sur l’utilisation de la biomasse Boréale perturbée

Le projet de recherche sera réalisé sur le site de Muskrat Falls, le nouveau barrage électrique situé au Labrador. Ce projet rendra disponible, d’ici 2016, 450 000m3 de bois coupés en prévision de la construction du réservoir hydraulique. Le projet de recherche constitue à réaliser des études technico-économiques et environnementales (analyse du cycle de vie) sur l’utilisation de ce bois dans un procédé de bioraffinage. De plus, le projet permettra d’inclure dans les modèles économiques et environnementaux, le bois perturbés de la région de Goose-Bay (feu, insectes et vent). Le projet répondra parfaitement aux besoins du partenaire puisqu’il identifiera l’option d’utilisation du bois la plus prometteuse et la plus durable et proposera des pistes d’amélioration en considérant le bois perturbés en place dans les forêts.

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Faculty Supervisor:

Paul Stuart

Student:

Partner:

Natural Resources NL (Corner Brook);EnVertis

Discipline:

Engineering

Sector:

Forestry; Sustainability & the Environment; Natural Resources

University:

École Polytechnique 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.