Innovative Projects Realized

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

30508 Completed Projects

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

Projects by Category

Development of clinical best practice guidelines for the treatment of allied health conditions

The proposed research project will involve the completion of a systematic literature review of published studies addressing clinical best practices for the treatment of health conditions in the social work field. Products of the research study will include a library of comprehensive clinical guidelines for practitioners to follow, which can serve as the foundation for subsequent individualized treatment for clients.
The clinical guidelines will be published within the Curv Health app, enabling the partner organization access to the highest quality research evidence for therapeutic approaches.

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Faculty Supervisor:

Catherine de Boer

Student:

Partner:

Curv Health

Discipline:

Sociology

Sector:

Information and cultural industries

University:

Memorial University of Newfoundland

Program:

Accelerate

Build and improve image embedding models of cellular phenotypes

The over-arching goal of the project is to explore the use of several recently developed self-supervised image representation learning methods in an attempt to improve performance across several biologically relevant benchmarking tasks at Recursion. At recursion, deep-learning based models are used to generate feature embeddings for our imaging data and these embeddings to generate downstream biological insights for drug programs. A large proportion of our in-house data is unlabeled, and we’re exploring the use of novel self-supervised representation learning models to improve our existing image embedding models. The end objective is to improve the ability to extract high-value insights from the data generated, typically called ‘maps of biology’, and lead to scientific breakthroughs by our disease biology scientists.

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Faculty Supervisor:

Rahul Krishnan

Student:

Partner:

Recursion Canada Inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Mental illness after exposure to medical cannabis

Although most cannabis use is recreational, cannabis is approved for various medical indications. One of most major safety concerns regarding the use of cannabis is its association with mental illness, in particular, induction or exacerbation of psychosis and schizophrenia, suicidal attempts and depression. That said, current available data in the field is based mostly upon studies of recreational users of cannabis and is therefore subject to inherent limitations and weaknesses. This study’s main objective is to study the association between exposure to medical cannabis and the risk for mental illness, including the risk for psychotic episodes, anxiety and suicidal behavior in meticulously designed studies. The data will be retrieved from three Israeli databases
Expected results: We expect this study will enable us to learn the association between medical cannabis and mental illness. This study will either confirm or refute the current concern about the mental effects of medical cannabis. The findings from the study will provide crucially needed data that could be immediately implemented in clinical practice, will contribute to the ongoing debate about legalization of recreational cannabis and may lay the groundwork for additional research.

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Faculty Supervisor:

Laurent Azoulay

Student:

Partner:

The Hebrew University of Jerusalem

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Pharmaceuticals; Biotechnology

University:

McGill University

Program:

Globalink Research Award

Deep Learning Based Approaches to Synthetic Data Generation

Synthetic population generation is the process of combining multiple socioeonomic and demographic datasets from various sources and at different granularity, and downscaling them to an individual level. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this project, we propose a multi-stage framework called SynC (Synthetic Population via Gaussian Copula) that is novel and scalable to address this research problem. We aim to make both theoretical advances by developing new algorithms, as well as release easy-to-use implements for others addressing similar challenges.

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Faculty Supervisor:

Periklis Andritsos

Student:

Partner:

Arima Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

The affect of the perceptions of social workers and policymakers regarding people living in poverty on policies and their implementation

The research examines how the perceptions of social workers and policymakers regarding people living in poverty affect policies and their implementation. The study will focus on exploring ideas about the perceived values of people in economic need, the conceptual values associated with policy decisions concerning the needy, the way such ideas affect the construction of social constructions related to the needy and how they are influenced by these ideas. Additionally, a comparison will be made with Canada to examine imagination and variation between the countries and impact of international social learning on social policy.

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Faculty Supervisor:

Daniel Béland

Student:

Partner:

The Hebrew University of Jerusalem

Discipline:

Sociology

Sector:

Public Service, Policy, and Governance; Other

University:

McGill University

Program:

Globalink Research Award

Improving the First Pass Yield of an industrial electroplating line through a combined Design of Experiments and Causal Inference integrated with Deep Learning algorithms (DLs)

The performance of an industrial electroplating line is evaluated using the First Pass Yield (FPY).
Improving the FPY of an industrial electroplating line is complex and depends on lots of environmental
and technical parameters. These parameters have different nature that makes it hard to assess the
interaction between them and consequently to detect the causes of plating defects. In a bid to tackle this
problem, we will apply a casual reasoning model equipped with deep learning to find the underlying
causes of process failures. Moreover, we will take advantage of Design of Experiments (DoE) techniques
to learn more about the weight of each parameter in the result of the plating process, leading to a lowvalue
FPY.

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Faculty Supervisor:

Usef Faghihi;Laurent Cormier

Student:

Partner:

Héroux Devtek Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

Université du Québec à Trois-Rivières

Program:

Accelerate

Diagnostics and Prediction of Mobile Device Behavior using Data Analytics and Machine Learning

SOTI has accumulated a large number of devices in the past ten years, and how to diagnose and predict the status of these devices has become a very important topic. Through this research we hope to find various parameters and patterns contributing to particular negative impacts. Additionally we would like to be able to Create demonstrable visualizations of such factors, patterns, that affect device performance or behaviour over time. Finally, we will build prediction models to quantify and visualize progression of negative trends up to terminal failures

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Faculty Supervisor:

Bo Wang

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Mobile Electroencephalography and Mobility in Parkinson’s Disease

Attention, an important aspect of human cognition, is needed for safe mobility and navigation through the environment. With age, the ability to move and navigate through the world requires greater cognitive resources. Previous brain imaging research has shown that mobility impairments are associated with reduced attention. However, previous work was limited to assessing attention while participants were stationary and/or in a laboratory environment, which does not necessarily translate to what would occur in the real-world. My research will use mobile neuroimaging to observe and compare brain activity in a real-world environment across younger adults, older adults (with and without a history of falls), and adults diagnosed with Parkinson’s disease. Participants will be required to pay attention to naturally occurring hazards while walking outside. These findings have the potential to expand current understandings of brain function in Parkinson’s disease, human mobility, and fall risk using real-world methods and technology.

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Faculty Supervisor:

Lindsay Nagamatsu

Student:

Partner:

Parkinson Society Southwestern Ontario

Discipline:

Life Sciences

Sector:

Other services (except public administration)

University:

The University of Western Ontario; University of Western Ontario.

Program:

Accelerate

Modular Magnetorheological Actuators Controls

Since its foundation in 2013, Exonetik has been developing and manufacturing magnetorheological (MR) actuator systems that enable novel functionalities to satisfy unmet customer needs. Exonetik’s patented actuators offer a unique combination of torque-density, high bandwidth, superior back-drivability, low-cost and aerospace-level reliability. To reach its ambitious goals, Exonetik has built several strategic relationships with major OEMs in the aerospace and automotive sectors and now wishes to repeat the process in the robotics’ field.
In 2018, Exonetik started exploring collaborative robotics, where it saw a unique opportunity for its MR actuators. In this field, Exonetik initially investigated the potential of remote actuator systems through the development of various prototypes (e.g.: 3 degree-of-freedom – DOF – wearable hydraulic robotic manipulator and teleoperated cable-driven arms). While the performance of these prototypes was excellent, this approach leads to unnecessary complexities (e.g.: routing) for 6 DOF robotic arms. In 2020, Exonetik thus developed its first modular MR robot actuator, and in 2021, its first 6 DOF robot for human-robot interaction. The modular MR robot’s promising performance has led compagnies to sign agreements with Exonetik to develop robotic arms for various robotics tasks.

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Faculty Supervisor:

Alexis Lussier Desbiens

Student:

Partner:

Exonetik Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Université de Sherbrooke

Program:

Accelerate

Automatic Machine Learning for Recommender Systems

Crossings Minds provides machine learning-based recommendation systems that allow companies to integrate their data and easily obtain on-demand personalized recommendations for their users. Currently, onboarding a new customer requires significant work on the behalf of Crossing Minds engineers to create and polish a machine learning model for that specific customer. The research project will explore the field of auto-machine learning, which studies the way that machine learning models themselves learn. The goal would be to provide tools that make the machine learning decision-making process easier for Crossing Minds engineers and will explore potential opportunities in each step of the machine learning pipeline. Such tools will reduce the turnaround time for onboarding of new customers and will therefore improve the company’s scalability.

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Faculty Supervisor:

Nisarg Shah;Scott Sanner;Eldan Cohen

Student:

Partner:

Crossing Minds Canada Inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Towards Development of a Decision Making Tool for Owners, Designers and Municipalities: Surveying Current Domestic Water Conservation Technologies and Tools for Single Family Residential Dwellings

The proposed project will survey “best-in-class” decentralized water conservation technologies and approaches such as rainwater harvesting systems and greywater recycling systems for use in single family residential dwellings in Canada. The proposed project will also survey all tools and resources provided by water conservation authorities such as CMHC to homeowners, building designers and municipalities. The proposed project will work with S2E to develop a final comprehensive report which will allow for the research results to be applied to S2E’s Project Smart Community. This project is providing the basis for comparisons between the available water conservation technologies and will act as a background for development of a decision making tool which could be used by the building industry.

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Faculty Supervisor:

Miljana Horvat

Student:

Partner:

S2E Technologies Inc

Discipline:

Engineering

Sector:

Construction and infrastructure; Finance and Insurance; Professional, scientific and technical services

University:

Toronto Metropolitan University

Program:

Accelerate

Multi-modal machine learning for business-critical insights in video conversations

The team is building a machine learning platform and solution to extract meeting insights from online meetings. Meeting insights denote moments from these meetings that may impact the company’s future product features, revenue, and customer satisfaction. This platform is driven by the market created by the widespread adoption of online virtual meetings as the main means of reaching clients in recent years. The internship will focus on developing a multi-modal solution that takes audio, visual, and its transcriptions as features and outputs moments of key insights. Specifically, the detection task is to detect interruptions, which identifies video and audio snippets where individuals are talking over another happening in business meetings.

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Faculty Supervisor:

Scott Sanner;Anthony Bonner

Student:

Partner:

Talka AI Canada

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

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