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
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8841
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9197
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95
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568
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1088
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Projects by Category

Victoria Green Economy

The proposed research is a collaboration between Vancity, the City of Victoria, and the
University of Victoria to bring together the necessary information (financial, regulatory, and
knowledge, respectively) to build a plan that will initiate Victoria’s Green Economy. Interns will
be part of distinct interviewing panels that will research the needs, benefits, and obstacles of
the development of a green economy. With that necessary information the interns will
collaborate with the industry partners to devise strategic plans that would lead to a roadmap
of how a green economy can be developed in Victoria. The major work will be to identify the
drivers of a green economy and then how those drivers can be advanced in Victoria. This
research has yet to be done in Canada and will serve to complement sustainability work done
on the environment with a focus on the economic drivers.

View Full Project Description
Faculty Supervisor:

Matt Murphy

Student:

Partner:

Vancity;The Corporation of the City of Victoria

Discipline:

Business

Sector:

Finance and Insurance

University:

University of Victoria

Program:

Accelerate

Investigating strategies for optimizing immunity to COVID-19: examining the impact of probiotic lactic acid bacteria-derived secretomes on epithelial cell and macrophage immune activity

Strategies to promote immune defences against COVID-19 infection are urgently needed. The gastrointestinal tract is a potentially important route for COVID-19 infection and for generating protective anti-viral immunity against this pathogen. Certain features of COVID-19 contribute to its ability to evade and subvert our immune defences. Type I interferon is a key immune protein that shuts down viral replication during virus infections. However, SARS-CoV-2 evades this defence by failing to induce these protective interferons, allowing the virus to replicate and cause symptoms of COVID-19 infection. Macrophages are another key host immune defence. Severe COVID 19 infections can result in acute respiratory distress syndrome (ARDS), an outcome linked to the disastrous effects of pro-inflammatory macrophage activity and “cytokine storm” production. In our research analyzing probiotic bacteria communication with the immune system, we found that Lactobacillus rhamnosus R0011 acts on intestinal epithelial cells and macrophages via secreted molecules (a secretome). This secretome-mediated communication induced Type I interferon production and drove macrophage differentiation into a regulatory phenotype, in contrast to the pro-inflammatory macrophages causing damage in severe COVID-19 cases.

View Full Project Description
Faculty Supervisor:

Julia Green-Johnson

Student:

Partner:

Lallemand Bio Ingredients

Discipline:

Life Sciences

Sector:

Manufacturing

University:

University of Ontario Institute of Technology

Program:

Accelerate

Multilingual B2B Supplier Detection and Information Extraction

At Tealbook, we search the web to make the world’s business-to-business supplier websites readily accessible. We extract important sentences and keywords to create a searchable database that buyers can then use to find the right supplier for their needs. But right now, we are limited to servicing English-language organizations. Can we expand our services to French? To German? To Korean? To any of the other 7000 languages in the world? Doing so would not only allow Tealbook to reach a wider audience, but also help the world stay interconnected in any language.

View Full Project Description
Faculty Supervisor:

Gerald Penn

Student:

Partner:

Tealbook

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

A smart edge computing infrastructure to support workflows, communication and logistic in COVID-19 healthcare facilities

The ability of the health system to manage a massive influx of patients is based on the combination of four factors: the personnel, the equipment, the physical spaces and the system in place. A combination better known in jargon as the 4 “S” (staff, stuff, structure / space, system). A fifth factor that is often misunderstood is synchronicity. With great adaptation to the workspace and team structures, a newly trained staff with new equipment, and a system of critical processes that evolve according to the evolution of the environment and the healthcare system status, synchronicity is essential. This synchronicity requires real time data and automations to enable already pressured teams and a stressed healthcare organization to adapt to unforeseen requests and needs.
In the actual project, we present a real time management system designed to empower healthcare systems and workers in the logistical chain of operation under a turbulent environment. This project will significantly help our organization to timely develop a more efficient and scalable C4 solution, capable of offering a wider range of OTT-services to medical personnel and, ultimately, save lives.

View Full Project Description
Faculty Supervisor:

Esma Aimeur;Gabriela Nicolescu;Sofiane Achiche;Maxime Raison;Maxime Raison;Sofiane Achiche;Gabriela Nicolescu

Student:

Partner:

Humanitas Solutions

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology; Information and cultural industries; Professional, scientific and technical services

University:

Polytechnique Montréal; Université de Montréal

Program:

Accelerate

Using NLP models to fetch SQL data via voice command for SOTI SNAP Analytics (NL-to-SQL)

This project is focusing on creating an integration of a database and mathematical calculation, with the use of Alexa, Google home, Cortana, so that users can use Natural Language to aggregate meaningful data and answer questions from a given database. For example, suppose there is a database about car sales. If I asked Siri, “who is the best sales for BMW in Toronto?” Our designed algorithm should return the name of the top sales from this given database. The project also builds out an Analytics Engine that will perform mathematical calculations on the data submitted by the SNAP APP dynamically.

View Full Project Description
Faculty Supervisor:

Gerald Penn

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Designing ‘Zero credit touch’ (ZCT) pre-approved credit underwriting program for retail customers

ICICI Bank has developed various ‘Zero credit touch’ (ZCT) strategies where without any credit intervention and additional information taken from customers, credit facilities can be provided. But there are several challenges in the expansion of ZCT strategies, namely, (i) current credit models which are a combination of business rules, scorecards and machine learning models, do not qualify a significant proportion of existing ICICI Bank customers; (ii) wherever customers do not have a salary account with the Bank, estimated income is lower leading to the customer being offered an amount lower than his/her requirement; (iii) customers with fraudulent intentions can open accounts and over time, these profiles would qualify for ZCT. To tackle these problems, we propose a novel ZCT system incorporating several state-of-the-art methodologies to build one go-to product to reduce credit and operations cost of lending whilst providing a superior customer experience.

View Full Project Description
Faculty Supervisor:

Sebastian Jaimungal

Student:

Partner:

ICICI Bank Canada

Discipline:

Computer science

Sector:

Finance and Insurance

University:

University of Toronto

Program:

Accelerate

Learning Discussion Thread Representations to Empower Content-based Recommendation

VerticalScope is a company that owns online forums in many domains, such as automotive, health, technology, and powersports. VerticalScope uses a content based recommender system to mitigate the cold start problem, where a large portion of traffic on the forums are made by unregistered users. The goal of this project is to learn representations of discussion threads. Thread representations that capture semantic and contextual information can improve the recommender system to suggest more relevant threads to users, and boosts search engine optimization and user retention rate. Understanding user sentiments also allows for the discovery of trending topics, and personalized homepage and advertisements.
Learning thread representations has various challenges. Within the automotive forums for example, there may be multiple threads talking about buying and selling cars. However, though these threads may have similar context, the object of discussion (e.g. the specific car model) can be different, and the learned representations should capture these differences. Another related problem is that there are many out of vocabulary words that may be very important to the relevancy between two threads (e.g. the name of a specific product).

View Full Project Description
Faculty Supervisor:

Gerald Penn

Student:

Partner:

VerticalScope

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Goal-Conditioned Reinforcement Learning

The goal of the project is to improve upon the methodology behind goal conditioned learning. In this framework, similar to the setup in traditional reinforcement learning, an agent interacts with an environment. However, instead of training the agent to maximize return, the agent is trained to reach a given goal at the end of the trajectory. That is, given a rollout-specific goal, the agent attempts to reach it. This goal conditioned paradigm is particularly promising for applications where the objective changes in every episode, for example, controlling a robot or a drone for different tasks; or self-driving vehicles, where the destination might change between episodes. In this project, we will explore potential improvements within the goal conditioned framework, both in the discrete and continuous action space settings.

View Full Project Description
Faculty Supervisor:

Arvind Gupta

Student:

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

Information and Communications Technology

University:

University of Toronto

Program:

Accelerate

Développement d’un béton électriquement conducteur par utilisation des résidus de bauxite brutes ou transformés

L’objectif principal de ce projet consiste à développer une formulation de béton électriquement conducteur basé sur l’utilisation des résidus de bauxite calcinés.
Les retombées de ce projet de recherche pour le Canada sont doubles puisqu’elles permettront, dans un premier temps, une valorisation de produits recyclés issus de la production d’aluminium canadienne, réduisant ainsi l’impact environnemental de ces derniers. Dans un second temps, cela représente un potentiel économique non négligeable avec le développement d’un nouveau type de béton chauffant pouvant être utilisé dans plusieurs domaines en lien direct avec les conditions climatiques exigeantes du Canada tel que la protection hivernale des tabliers des ponts ou encore des pistes aéroportuaires qui requièrent une quantité importante de produits déverglaçants nocifs pour l’environnement et les structures.

View Full Project Description
Faculty Supervisor:

Guy Simard;Christophe Volat;Ahmed Rahem

Student:

Partner:

Rio Tinto Alcan (Jonquière, QC)

Discipline:

Engineering

Sector:

Manufacturing; Mining; Professional, scientific and technical services

University:

Université du Québec à Chicoutimi

Program:

Accelerate

Combined relational and BERT-ranking for multilingual ad hoc document retrieval

With increasing amounts of information available online on the web, it’s crucial for search engines to filter out the content they think is useful and rank that content in decreasing order of relevance to the user’s query so that the user can just focus on the top results. Traditional techniques in search ranking focused on presence of the user’s search terms in the documents being returned by the search engine. Now, modern advances in machine learning allow us to understand complex relationships between what the user really is looking for and what the documents really are about and this research will use this understanding to make better search engine rankings. This research also leverages these advances in technology to also understand the similarities between documents for return meaningful results to user search queries in multiple languages.

View Full Project Description
Faculty Supervisor:

Gerald Penn

Student:

Partner:

Tealbook

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Net-shape Manufacturing of Fins for High Efficiency Heat Exchangers

Environmental pressure to reduce greenhouse gas emissions and fuel consumption has prompted
significant worldwide activity to find effective renewable/regenerative energy solutions. Solutions
where power is produced and waste heat from the exhaust is recuperated have great potential,
especially for distributed (decentralized) power generation (DPG). The latter approach reduces energy
losses that are caused by the absence of long-distance transmission lines and minimizes the risk of
widespread electrical failure. The development of high efficiency microturbines (MT) is critical to the
success of DPG and relies on efficient heat exchangers. Brayton Energy Canada has recently
developed a new proprietary design of high-efficiency heat exchangers that could allow MT to reach
higher thermal efficiency and thus quickly become a solution for DPG if parts (the fins) could be
manufacture in a cheaper way. The proposed project aims at developing a new manufacturing process
to produce the fins in a cheaper way.

View Full Project Description
Faculty Supervisor:

Bertrand Jodoin

Student:

Partner:

Brayton Energy Canada

Discipline:

Engineering

Sector:

Utilities

University:

University of Ottawa

Program:

Accelerate

Targeting SARS-CoV-2 (COVID-19) methyltransferases (nsp10-nsp14 and nsp10-nsp16 complexes) toward developing small molecule antiviral therapeutics

COVID-19 pandemic has brought the world to standstill with more than 3 million people infected and more than 200 000 mortality so far. It has literally brought the health care systems in many countries to the breaking point, if not beyond. The economic consequences have been devastating with millions of people out of work. We are taking a novel approach by focusing on two SARS-CoV2 (COVID-19) methyltransferases that are essential for viral replication. Both enzymes (nsp14 and nsp16) are druggable. Therefore, identifying potent inhibitor of these two proteins could be used in providing new therapeutics for COVID-19. In addition, because these proteins are highly similar in other coronaviruses such as SARS (SARS-Cov) and MERS (Middle East Respiratory Syndrome), the same drugs likely could be effective in treatment of other coronavirus infections.

View Full Project Description
Faculty Supervisor:

Masoud Vedadi

Student:

Partner:

Structural Genomics Consortium

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

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