Innovative Projects Realized

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

13270 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

Evaluating Team Unbreakable: A learn-to-run program for adolescent mental health

Given the rise in adolescent mental health concerns, Canadian secondary schools are increasingly focused on implementing programs aimed at improving mental resilience in students. One such program is Team Unbreakable, a 10-week, learn-to-run program that is based on four theoretically driven program components: goal-oriented, group-structured, within the school context, and physical activity based. This program has been implemented in 117 schools, with nearly 10,000 students participating over the last five years across southern Ontario. Unfortunately, this program has yet to be evaluated. The present collaboration proposes a research study to explore the effectiveness and acceptability of this program. Students will be asked to complete a self-report questionnaire on key outcome indicators and participate in semi-structured interviews to discuss their program experiences. The findings from this evaluation will guide the growth and development of this program as Team Unbreakable goes through critical structural changes.

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Faculty Supervisor:

Catherine Sabiston

Student:

Alyona Koulanova

Partner:

Team Unbreakable

Discipline:

Kinesiology

Sector:

Other services (except public administration)

University:

Program:

Accelerate

Play for Reality: Conveying Sustainability Challenges Through Game Mechanics

Several Ontario regions have agreed to reduce their carbon emissions by 80% by 2050. However, many of these cities don’t actually have a plan for how they’ll reach this noble but challenging goal. In response, the Waterloo Global Science Initiative (WGSI) (in partnership with several Waterloo Region collaborators) has created a prototype board game called Energize: its goal is to draw attention to the challenges and the solutions of how a city can reduce carbon emissions.

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Faculty Supervisor:

Neil Randall

Student:

Aaron Atienza

Partner:

Waterloo Global Science Initiative

Discipline:

Languages and linguistics

Sector:

University:

Program:

Accelerate

Predicting Risk of Aggressive Responsive Behaviours among People Suffering from Dementia using Natural Language Processing (NLP) and Machine Learning (ML).

Patients with dementia will eventually experience significant loss of cognitive function. Many will have difficulty properly communicating life’s challenges and instead become agitated, resulting in verbal or physical aggression. Monitoring the risk of a resident harming themselves or others due to aggressive behavior is a priority within a long-term care facility where dementia is present. Caregivers at Shannex regularly record resident health and behaviour using computing systems. Each of these systems digitally record information either as structured data or unstructured text, providing an on-going log of each resident’s patient history. The objective of this project is to use natural language processing (NLP) and machine learning (ML) techniques to develop models that can predict the probability of a resident exhibiting aggressive behaviours that may harm themselves or others within the next week.

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Faculty Supervisor:

Daniel Silver

Student:

Maryam Tajeddin

Partner:

Shannex

Discipline:

Computer science

Sector:

Health care and social assistance

University:

Program:

Accelerate

Technical Research on Unconventional Resources Development in Canada

Within this project, interns will review the peer-review literature on the four different topics: (1) Liquefied Natural Gas (LNG), (2) Hydraulic Fracturing, (3) Anomalous Induced Seismicity, (4) Surface and Ground Water access, transport, flowback chemistry, conservation, recycling, treatment and disposal after use in hydraulic fracturing operations.
The aim of the project is to perform the qualitative analysis of the listed subjects in order to compile scientific-based, unbiased information that will be used to update the partner website, presented at conferences as well as released to public in a form of bulletins. Moreover, student will be meeting regularly with the industry champions, to exchange the knowledge and discuss the most important aspects of the researched topics. The emphasis will be put on distinguishing true, scientific information from falsehoods and current misconceptions.

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Faculty Supervisor:

David Eaton;Mirko Van Der Baan

Student:

Paulina Wozniakowska;Jieyu Zhang

Partner:

Canadian Society for Unconventional Resources

Discipline:

Geography / Geology / Earth science

Sector:

University:

Program:

Accelerate

Creating a Predictive Vegetation Model to Guide Wetland Restoration in the South Arm Tidal Marshes

I will be focusing my study on a small tidal marsh called Frenchies Island within the South Arm Marshes of the Fraser River, which has become overrun with an invasive species of cattail. Frenchies, like many tidal marsh islands has had a dike constructed around its perimeter and has therefore been cut off from the natural incoming of water from the tidal cycle, as well as from high flows of the Fraser river. The goal of this project is to create a predictive model to forecast the type of plant cover that is likely to grow on the site, once the invasive cattail has been weakened or eradicated on the site. The model will be created by first characterizing both Frenchies and areas that are not dominated by cattail (undisturbed sites) through ground surveys in terms of the distribution of elevation, soil salinity and plant cover. The results from my mapping of the non-disturbed sites will be applied to Frenchies Island to create the predictive model. TO BE CON’T

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Faculty Supervisor:

Anayansi Cohen-Fernandez

Student:

Kyla Sheehan

Partner:

Ducks Unlimited Canada

Discipline:

Environmental sciences

Sector:

University:

Program:

Accelerate

Embedded sensor fusion network

Highly accurate 3D object detectors require significant computational resources, and reducing computation and memory load while maintaining the same level of performance is a critical task for any safe and reliable autonomous vehicle. This research project investigates the deployment of an accurate 3D object detection model to a resource constrained architecture by changing the model structure, its parameters as well as its activity during operation. Through a multi-level optimization, both the amount of computation as well as the memory load will be reduced while maintaining 3D object detection performance. The knowledge gained from this experiment will help the industry partner to develop new architectures for ever more complicated computational tasks.

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Faculty Supervisor:

Robert Laganiere

Student:

Rytis Verbickas;Yahya Massoud

Partner:

Synopsys Inc.

Discipline:

Engineering - computer / electrical

Sector:

University:

Program:

Accelerate

Multi-Channel User Linkage through Probabilistic Matching

People utilize multiple devices to complete various tasks, making their online identities fragmented. Advertising is as much about knowing when not to promote a product as it is about when to do it. For example, before being sent alcohol and cannabis ads, the user must be identifiable as being over 19. Age information may only be available on a channel different than the one through which the user is connecting. This makes it difficult to gain a holistic understanding of users and develop a single marketing strategy across devices. Pelmorex Audience, mobile advertising division of Pelmorex Corp., would like to explore advanced large-scale probabilistic matching techniques to link different representations of the same user across channels, in order to create a master set of user profiles. This will enable them to enhance the user experience by limiting ad repetition on different environments, while also customizing ads to their interests.

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Faculty Supervisor:

Marsha Chechik

Student:

Kavita Srinivasan

Partner:

Pelmorex Media Inc

Discipline:

Computer science

Sector:

University:

Program:

Accelerate

Be Yourself: How to be a Positive Influencer On and Offline

It has been shown that a mother’s conduct, together with her relationship with her daughter, can directly and indirectly impact her daughter’s well-being and development (e.g., eating habits, body image, and self-esteem). Studies have shown that the mother/daughter relationship influences every stage of the daughter’s development, with particular influence in the formation of the young adolescent girl’s perception of herself and her body. As parent modelling exists offline between mothers and young adolescent daughters (11-14 years old), a similar influence is being exercised by the fast evolution of digital culture, such as social networking sites (SNSs). Working together with community members, mothers, scholars, and educators, we will co-create and evaluate a transformative learning experience for positive influencers, which will consist of a tangible Toolkit and Community Workshop. The materials will be an operational and sustainable outreach service for the Bulimia Anorexia Nervosa Association (BANA) to use after the funding period.

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Faculty Supervisor:

Sarah Woodruff

Student:

Sara Santarossa

Partner:

Bulimia Anorexia Nervosa Association

Discipline:

Kinesiology

Sector:

Health care and social assistance

University:

Program:

Accelerate

Research into grid-forming methods for off-shore wind farms

Conventional power systems rely on synchronous machines for generation of power and also for formation of an interconnected network of generation to which loads are connected via a transmission system (known as a grid). Increasingly renewable sources of energy are interconnected to a grid via power electronic converters. These converters have been traditionally operated with the assumption of an existing grid thanks to the presence of synchronous machines. In recent years, it has been noted that operating conditions have arisen that require the normally grid-following converters to assist in establishment of a grid, a mode known as grid forming. This proposal looks into the methods and algorithms that are required for grid-forming operation of wind energy systems in particular those with a diode rectifier unit. TO BE CONT’D

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Faculty Supervisor:

Shaahin Filizadeh

Student:

Shirosh Peiris

Partner:

Manitoba Hydro International Ltd

Discipline:

Engineering - computer / electrical

Sector:

University:

Program:

Accelerate

Statistical Learning for Financial Time Series

Given a time series of returns for a portfolio of financial instruments, develop a model that accurately predicts returns which maximize profits. The objective function will take an input of financial indicators from the previous time interval and the returns from the current time interval. These indicators can explain relationships between financial instruments in the portfolio of interest, thus are important for explaining their returns and associated risk. A common challenge with these types of problems is how easy it can be to over-fit your model. In this project, we seek to explore state-of-the-art machine learning models to determine a model with high prediction accuracy that generalized well to unseen data.

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Faculty Supervisor:

Mark Coates

Student:

Cody Mazza-Anthony

Partner:

Squarepoint Technologies

Discipline:

Engineering - computer / electrical

Sector:

University:

Program:

Accelerate

Applying Machine Learning to Predict Demand Transference

The project will help us design a machine learning model that can determine the demand transference of our customers. The key objective of this project is to design, research, build, and experiment with machine learning models to ensure low product waste and high customer satisfaction. The model will have several impactful applications across the organization.

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Faculty Supervisor:

Qiang Sun

Student:

Zi Yi Chen

Partner:

Loblaw Company Limited

Discipline:

Computer science

Sector:

Service industry

University:

Program:

Accelerate

Multi-morbidity Characterization and Polypharmacy Side Effect Detection for designing Optimal Personalized Healthcare with Machine Learning

Despite a significant improvement in healthcare systems over the past decades, the rapid growth in the number of patients with multiple chronic diseases – called multimorbidity – stands as a complex challenge to healthcare services that are primarily designed to treat individuals with single conditions. Advances in machine learning as well as in computing power now enable us to exploit a vast amount of healthcare data. The main goal of this project is to propose a data-driven approach to characterize patients with multimorbidity in such a way that an optimal care can be given to each of them, using machine learning techniques. The project will use the ICES (Institute for Clinical Evaluative Sciences) dataset, Ontario public health data that is completely anonymized and collected from 1992, containing information on around 15 million Ontario residents. TO BE CONT’D

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Faculty Supervisor:

Marzyeh Ghassemi

Student:

Seung Eun Yi

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

University:

Program:

Accelerate

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