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
SK
8841
ON
9197
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95
PE
568
NB
1088
NS

Projects by Category

Libérer le potentiel des entrepreneurs par le mentorat : exploration de différentes pratiques

L’objectif général de ce projet de recherche consiste à mieux comprendre la contribution des différentes formules de mentorat (individuel et en groupe) à la réussite des entrepreneurs novices. Plus spécifiquement, ce projet de recherche en partenariat poursuit les objectifs suivants : 1) Documenter les retombées du mentorat pour entrepreneurs selon différentes formules identifiées; 2) Comprendre comment le mentorat, dans chacune des formules, répond aux besoins des entrepreneurs accompagnés; 3) Développer une nouvelle formule de mentorat (innovation sociale coconstruite) et en vérifier les effets auprès d’entrepreneurs; 4) Démontrer l’effet du mentorat pour entrepreneur et dégager les conditions les plus susceptibles de générer des retombées positives dans leurs parcours. Le partenaire privilégié pour réaliser ce projet de recherche est le Réseau Mentorat. Pour atteindre ces objectifs, le partenariat de recherche va réaliser quatre recherches distinctes mais imbriquées. Les perspectives originales proposées dans ce projet, couplées à l’opportunité de travailler avec le plus grand programme de mentorat pour entrepreneurs au Canada, sont susceptibles de générer des contributions variées et importantes pour comprendre comment mieux soutenir les entrepreneurs par le mentorat.

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Faculty Supervisor:

Étienne St-Jean

Student:

Partner:

Réseau Mentorat

Discipline:

Business

Sector:

Other services (except public administration); Public administration

University:

Université du Québec à Trois-Rivières

Program:

Accelerate

Combating Algorithmic Bias: responsible fairness measures, algorithms and toolkits in retail banking

Fairness has gained unprecedented support in a world of daily emerging scientific inquisition and
discovery, aiming to tackle algorithmic bias effectively. Extensive efforts have been devoted to defining
and embodying what is bias (discrimination) and developing tools that enable machine learning
practitioners to detect and mitigate bias during algorithm design. However, mysteries are yet to be
solved on the practical application of these fairness measures and toolkits. This research proposal
presents a systematic review of identified algorithmic bias issues and the proposed fairness solution
space, focusing on the development of novel approaches to attain fairness in the banking system. The
general objective is broken down into three sub-objectives. The first involves creating fairness
assessments on real banking datasets and implementing existing advanced toolkits to measure bias.
The second sub-objective focuses on deploying appropriate measures of fairness specific to the
available datasets, including group, individual, and causality-based fairness, while the third subobjective
aims to design novel approaches customized to stakeholders and the banking system to
achieve fairness. The methodologies outlined in this proposal offer a comprehensive approach to
measure and mitigate biases in the banking system. By addressing these issues, the insights gained are
to foster collaboration between practitioners and fairness experts, ultimately facilitating the
development of practical and user-friendly fair ML toolkits.

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Faculty Supervisor:

Linglong Kong

Student:

Partner:

Scotiabank

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

University of Alberta

Program:

Accelerate

Prediction and management of the long-term environmental risk of mine waste rock piles via 5G-enabled instrumentation and monitoring

Mining activities generate large quantities of waste rock after valuable metals are extracted. Upon exposure to air and water, a diverse range of metals and metalloids are released and mobilized to the surrounding environment. There is an urgent need to manage the environmental risk posed by mine waste rock piles given the scale of the problem. A major bottleneck is the lack of data on waste rock pile properties. The main objective of this research is to understand how to best use 5G-enabled wireless sensor networks to monitor key characteristics of mine waste rock piles, and to use machine learning to predict drainage quantity and quality. Ultimately, this research aims to establish an industry-standard framework for risk assessment and management of mine waste rock pollution using 5G-enabled instrumentation and monitoring.

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Faculty Supervisor:

Wenying Liu;Roger Beckie

Student:

Partner:

Rogers Communications Inc.

Discipline:

Engineering

Sector:

Water; Sustainability & the Environment; Mining

University:

The University of British Columbia

Program:

Accelerate

Enhancing smart thermostat performance through customer data analysis and the integration of AI techniques

Enhancing the efficiency of expanding HVAC systems is a complex task. Utilizing Artificial Intelligence (AI) techniques like Machine Learning (ML) and Deep Learning offers promising solutions. ENA Solution’s smart thermostats provide the potential to employ AI methods for temperature control and energy optimization. In recent years, the company has made efforts to integrate AI for automated solutions in their smart products, but the research is still in its early stages. Moreover, considering recent advancements in AI algorithms, active R&D is essential to maintain product accuracy and performance leadership in the market.
The goal enhancing the efficiency of HVAC systems can be achieved by studying available methods, analyzing company resources, proposing practical solutions, and monitoring small-scale candidate approaches. Analysis of customer feedback and prototype data, recognizing vulnerabilities and technology bottlenecks, and applying stable, optimized methods on a large scale are also vital. The study will leverage practical knowledge of complex systems, thermodynamics, and statistical mechanics to understand influential factors and design AI models based on scale and complexity. Potential approaches may include Convolution Neural Networks (CNNs) for large-scale data or techniques like k-nearest neighbors algorithm (KNN) and decision tree algorithms for smaller scales. Alternative solutions will also be explored based on study outcomes.

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Faculty Supervisor:

Raymond Spiteri;Terry Peckham

Student:

Partner:

ENA Solution Inc

Discipline:

Computer science

Sector:

Manufacturing

University:

University of Saskatchewan

Program:

Accelerate

Towards Production of Industrial Oils from Camelina

In addition to food uses, vegetable oils are increasingly a source for renewable biomaterials and biofuels. Recent progress in the development of a biobased economy is focused on introduction and improvement of novel crop plants for non-food applications. The proposed project is part of an international collaborative effort to develop camelina as a new industrial oil platform. Our role is to increase oil content of camelina by (1) using a directed-evolution technique and our unique high-throughput screening system to boost the activity of the key enzymes in seed oil accumulation, and (2) introduce these variants into camelina. This project further advances the sponsor’s existing camelina development program aimed at developing a new crop alternative with economic benefits for producers and the emerging bioproducts industry. It has the potential to create new intellectual property and germplasm for both the sponsor and academic partner.

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Faculty Supervisor:

Randall Weselake

Student:

Partner:

Alberta Innovates - Technology Futures (Vegreville)

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Alberta

Program:

Elevate

Development of standard sample processing procedure for multi-omics analysis of small intestinal samples collected by an ingestible capsule and understanding of relationship between gut microbiota and IgA production

Recent studies have shown that the gut microbiome works as an organ of the human body which produces bioactive molecules and metabolites. Since the gut microbiome is in close interaction with the human internal environment and changes in the compositions of gut microbiota can impact host physiology through many pathways. Precise determination of gut microbiota compositions depends on the ideal sampling methods which are non-invasive, has little cross-contamination, and collect samples at different sites. The current investigational swallowable SIMBA capsule by Nimble Science can provide a non-invasive, inexpensive, and convenient sampling method to collect small intestine microbiota. The aim of this study is to optimize and evaluate the efficiency of SIMBA sampling capsule using optimization of sample processing procedures and the determination of microbial diversity followed by metabolomic profiling of samples from healthy controls and patients compared with samples obtained by endoscopy and feces.

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Faculty Supervisor:

Kathy McCoy

Student:

Partner:

Nimble Science Ltd.

Discipline:

Life Sciences

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Calgary

Program:

Accelerate

Building a Digital Twin for the Pearson Airport

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

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Faculty Supervisor:

Elmira Nezami Far

Student:

Partner:

Greater Toronto Airports Authority

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Transportation and warehousing

University:

George Brown College of Applied Arts and Technology

Program:

Accelerate

Performance issues for embedded and random problems in adiabatic quantum optimization

Current and near-future D-Wave processors implement optimization using quantum effects on a fixed processor layout. This layout can be modeled as a graph with certain constraints, and the optimization is applied to a specific problem: fixed-topology Ising spin optimization. It is therefore important to study how best to use this hardware to solve combinatorial optimization problems in various forms, and to study performance versus conventional optimizers. The intern will study problems in this area, as well as the problem of predicting performance of proposed processor topologies. D-Wave expects to benefit from this partnership by gaining a clearer view of these issues, which are of fundamental importance to designing software applications for quantum annealers.

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Faculty Supervisor:

Matthew DeVos

Student:

Partner:

D-Wave Systems Inc.

Discipline:

Mathematics

Sector:

Technology; Technology

University:

Simon Fraser University

Program:

Accelerate

A Comprehensive Approach to Automated Essay Scoring through Weak Supervision, AutoML, and Interpretability

Scoring essays is important for learning and business. With AI and machine learning, we can save time and resources by automating the process. This project looks at automatic essay grading from three angles. First, we generate more data using automatic methods called data programming. This helps improve the machine learning models. Then, we refine the data using NLP techniques. Second, we will use Auto Machine learning techniques to easily determine the parameter for training. Third, we make the model more understandable. Deep learning models are often hard to understand, so we’ll use methods to explain the decisions made by the algorithm. We’ll focus on local and post-hoc explainability models. For data programming, we’ll use weak supervision. For interpretability, we’ll use methods like SHAP and LIME.

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Faculty Supervisor:

Mucahit Cevik

Student:

Partner:

Blees AI

Discipline:

Engineering

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Toronto Metropolitan University

Program:

Accelerate

Methodology to Collect, Calibrate and Process Thermal RPAS Imagery into Radiometric Orthomosaics for Salmon Habitat Preservation

This project proposes to create a standardized methodology to collect, calibrate and process thermal drone imagery into ortho-rectified maps showing accurate temperature data for streams and rivers. These temperature maps are quickly becoming a useful tool with which to detect influxes of cold water that are critical for the preservation of salmon habitat. As our streams and rivers become increasingly hotter due to the effects of climate change, it imperative that new, efficient methods are developed to identified stream reaches that help to cool the stream and preserve salmon-rearing habitat.

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Faculty Supervisor:

Eric Saczuk

Student:

Partner:

Pacific Salmon Foundation

Discipline:

Earth science

Sector:

Agriculture; Other services (except public administration); Professional, scientific and technical services

University:

British Columbia Institute of Technology

Program:

Accelerate

PandoPartner Internship Project

This project will help PandoPartner train its clients on how to use our software to better their sponsorship marketing operations, as well as help PandoPartner promote its software. It will involve the creation of User Guide Content & client resources, Marketing materials, and more to help clients and prospects understand how they can leverage PandoPartner’s software.

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Faculty Supervisor:

Sandy Staples

Student:

Partner:

PandoPartner Inc.

Discipline:

Business

Sector:

Administrative and support, waste management and remediation services

University:

Queen's University

Program:

Business Strategy Internship

Prey Availability and Predation as Limiting Factors of Burrowing Owl Population Growth in British Columbia

Alison’s research, conducted in partnership with the Burrowing Owl Conservation Society of BC, aims to better understand what is limiting the endangered burrowing owl’s population recovery in British Columbia. The Burrowing Owl Conservation Society of BC has implemented a species reintroduction program that includes breeding and releasing owls. They then track how many owls survive, reproduce, and return from migration. Alison’s research will focus on understanding whether release site prey availability and predation rate are potentially limiting burrowing owl reproductive success and survival during the breeding season. Analysis of data collected via wildlife cameras, insect sampling and direct observations will provide critical information on how prey availability and predation rates differ between release sites. This information will be useful in guiding where future releases occur, as well as making informed management decisions regarding the habitat currently used to maximize burrowing owl population growth.

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Faculty Supervisor:

Douglas Ransome

Student:

Partner:

Burrowing Owl Conservation Society of BC

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

British Columbia Institute of Technology

Program:

Accelerate

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