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

Segmentation of 3D microscopy images

In-vivo imaging provides a unique opportunity to examine complex cellular activity in live tissue. Images produced by these experiments are difficult to analyze manually, typically applied to mono-layer cell culture assays (i.e. cells in a dish). Recent advances in deep learning enable the opportunity to analyze these in-vivo tissue images with greater efficiency and accuracy. This project will apply deep learning based segmentation and classification technology to a dataset provided by a collaborating pharmaceutical company. Deep learning algorithms will be developed to segment different cell types and vascular structures in the dataset and quantify features (i.e. length, volume, protrusion number, marker intensity) of these objects. These features will be used to evaluate the effectiveness of therapeutic treatments.

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Faculty Supervisor:

Sanja Fidler

Student:

Kshitij Gupta

Partner:

Phenomic AI Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Intra-operative Error Detection on Surgical Video based on Computer Vision Analysis

The intra-operative errors that occurs in adverse events have been a major concern in healthcare and surgical industry. Conventionally, error-event assessment is done by peer surgeon review, which is time consuming and costly. With the advances in machine learning and computer vision techniques, it is possible to keep track of the operation surgical procedures based on recorded surgical videos to evaluate and classify the errors occurred. With the proposed computer vision-based algorithm, it is expected to predict the error event during surgery in a scalable process to ensure a better and safer patient and surgical environment.
Since the intra-operative error detection algorithm is mainly trained on recorded surgical video data, it is expected to have an impact on improving decision-making and performance in future operations for complex patients and surgical circumstances.

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Faculty Supervisor:

Sanja Fidler

Student:

Yichen Zhang

Partner:

Surgical Safety Technologies Inc

Discipline:

Computer science

Sector:

Medical devices

University:

Program:

Accelerate

Exploring the connection between design and computational thinking across industry and educational contexts

In today’s fast-paced, technologically-driven world, it is of paramount importance that we take advantage of different ways to solve problems, to generate the most efficient solutions. One way to accomplish this goal is to adopt different ways of thinking about how we solve problems. It may be of great value to base problem-solving that we teach in education using basics of design thinking and computer science, two existing disciplines. However, research has not yet assessed how these types of thinking may be related across different contexts, like education and industry. In this project, the intern will work with an academic expert and leading industry partner to develop and assess technology-enhanced educational materials. In doing so, the intern will gain experience collaborating with experts from education, business and robotics, and the industry partner will benefit from the intern’s research findings in developing curriculum-based educational materials.

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Faculty Supervisor:

Julie Mueller

Student:

Eden Hennessey

Partner:

InkSmith Ltd

Discipline:

Education

Sector:

Education

University:

Program:

Accelerate

Assessing Gaps and Opportunities: Industry Knowledge and Capacity for Large-Scale Heat Pump Uptake

This research is based on the recognition that heat pump technology has the potential to reduce greenhouse gas (GHG) emissions and reliance on fossil fuels, while providing space heating, space cooling and domestic hot water. Both internationally and in the Ontario context, a lack of industry knowledge and capacity has been noted to be a barrier to the uptake of heat pump technologies. This project therefore seeks to gain a better understanding of the industry knowledge and capacity gaps for heat pump retrofits in Ontario, and more specifically, in Ontario’s multi-unit residential building sector. The research will investigate potential strategies for overcoming this barrier and creating greater alignment between key actors such as (but not limited to) policymakers, training/education institutions, utilities and trade associations. TO BE CONT’D

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Faculty Supervisor:

Mark Winfield

Student:

Susan Wyse

Partner:

Toronto and Region Conservation Authority

Discipline:

Environmental sciences

Sector:

Alternative energy

University:

Program:

Accelerate

Voice Cloning Optimization

An artificial intelligence tool that is capable of generating natural-sounding speech can be embedded into many valuable services such as conversational agents for the disabled and conversational assistants. Such tool, when equipped with the capability of mimicking individuals’ vocal characteristics, will improve personalization of these services. In this project the intern will seek to develop a state-of-the-art voice cloning model. The two major research agendas underlying this project are as follow: to develop a data-efficient voice cloning model with high speech synthesis quality; to enrich voice cloning models with the capacity of voice properties manipulation for speech synthesis. On the former agenda the intern will set out to develop a model that requires minimal fine-tuning to pick up a new speaker’s vocal characteristics and use them to generate realistic audio samples. TO BE CONT’D

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Faculty Supervisor:

David Duvenaud

Student:

Wei Zhen Teoh

Partner:

Lyrebird AI

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Reservoir Analytical Model Pattern Recognition

The proposed production optimizer uses production (rate, water/oil ratio, pressure) data, in either isolation or with geological data, and artificial intelligence to determine limiting factors in wells and fields. More specifically, the proposed production optimizer determines Original Oil in Place (OOIP), average permeability, permeability distribution, and relative permeability for wells and, by extension, reservoirs. This reservoir characterization information then is used to optimize the field.

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Faculty Supervisor:

Stevan Dubljevic

Student:

Junyao Xie

Partner:

Res-Solve Solutions

Discipline:

Engineering - chemical / biological

Sector:

Oil and gas

University:

Program:

Accelerate

Early detection of Alzheimer’s disease symptoms using speech longitudinally

An early symptom of Alzheimer’s Disease is difficulty in remembering recent events. These trends are reflected in problems in language and patterns of speech. Speech patterns of an individual can hence be used to determine the trajectories of preclinical cognitive decline. The difference in the cognitive trends over subject groups, analyzed using speech data collected over a long period of time, can be used to detect Alzheimer’s even before it can be confirmed clinically.
With the help of machine learning models, this process can be automated completely by using automatic speech recognition systems to transcribe the speech followed by analysis of these transcripts. This project will explore machine learning- based strategies to automate the early AD diagnosis pipeline.

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Faculty Supervisor:

Yang Xu

Student:

Aparna Balagopalan

Partner:

WinterLight Labs Inc

Discipline:

Computer science

Sector:

Medical devices

University:

Program:

Accelerate

Hand Hygiene Tracking Prototype Development

SafeContact is a Hospital Acquired Infection (HAI) prevention and reduction Platform-as-a-Service (PaaS) company. We are tackling the global HAI epidemic by tracking the source of HAI spread using a unique technological concept in the marketplace. SafeContact is building a smart healthcare platform that combines traditional techniques with the latest advancements in technology: Computer Vision, Artificial Intelligence, Internet of Things devices, Big Data, and Machine Learning to monitor hand hygiene behavior for lasting impact on patient safety. This project will assist SafeContact with prototyping to track hand hygiene events in a healthcare setting. The completion of this project will allow SafeContact to secure Venture Capital to commercialize the hand hygiene methodology and business model, which will benefit not only Alberta but also other provinces. While initially targeting at healthcare services, eventually this platform will be available in other sectors, e.g. restaurants and other food supply chains, worldwide, bringing economic and technological benefits to Canada.

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Faculty Supervisor:

Irene Cheng

Student:

Leyuan Yu

Partner:

SafeContact Solutions Inc.

Discipline:

Computer science

Sector:

Medical devices

University:

Program:

Accelerate

Cognitive Risk Sensing Using Deep Learning

CRISP is an international Deloitte development initiative aimed at helping some of our largest clients understand and managed corporate risk. CRiSP stands for “Cognitive Risk Sensing”, and it centers around using large sources of mostly unstructured data (i.e. 10% sample of all of Twitter, thousands of news aggregators, etc.) to understand and forecast risk for the clients. The goal for the student is to apply new methods in machine learning, data mining and natural language processing to extract user opinion of products from social media and customer feedback. The challenge is that such data is very large in volume and may be sourced from multiple distinct domains.

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Faculty Supervisor:

Frank Rudzicz

Student:

Scarlett Guo

Partner:

Deloitte

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Demonstration-Based Initialization of Reinforcement Learning Algorithms for Efficient Robotic Control

Kindred’s Sort product is a robotic system that operates in e-commerce distribution centers to sort and handle apparel and general merchandise. The deployed system is controlled through a combination of artificial intelligence and human-in-the-loop teleoperation. The proposed project involves applying techniques from artificial intelligence (specifically machine learning and reinforcement learning) to improve the ratio of automatic control to human control. The core hypothesis of the project is that historical data collected from human teleoperation of the robots performing object-grasping tasks can be used to train the robots to pick up items automatically. This task is a challenging research problem at the cutting edge of robotic control and AI, and it will be tackled with a combination of state-of-the-art academic research and internally-developed algorithms.

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Faculty Supervisor:

Sven Dickinson

Student:

Ryan Dick

Partner:

Kindred Systems Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Exploration of Methods and Models to Achieve Multi-Document Comprehension in the Legal Domain

The project attempts to tackle an important challenge in Artificial Intelligence (AI), to give a machine an ability to comprehend multiple documents like humans do. These can do the redundant or preliminary reading-based research performed in many domains. The project aims to create a system which can read, understand, and answer queries and/or summarize multiple legal documents in a single shot. The project aligns with ROSS’s roadmap and vision – to supplement and enhance the quality and capacity of research tools, available at an average lawyer’s disposal and boost the time spent with their clients. Such a powerful system requires the capacity to understand queries made in natural language such as English. Hence the system will be developed using applicable novel state-of-the-art AI based Natural Language Processing (NLP) techniques. TO BE CONT’D

View Full Project Description
Faculty Supervisor:

Frank Rudzicz

Student:

Manasa Bharadwaj

Partner:

ROSS Intelligence Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

Identifying vehicle accidents and high risk drivers using Machine Learning

The primary objective of the project is to approach the problem of understanding true causality of vehicle accidents and scientifically determining which vehicles and drivers are at highest risk of an accident from a machine learning perspective. Geotab has a number of identified collisions in X, Y and Z planes, and much more. The research would be aimed at using both Geotab’s data in addition to external data such as weather and topography to develop a predictive model that can identify those drivers at highest risk of an accident. This may be based solely on current driving behavior and/or the driving history.
The results of this project are important in helping our over 20,000 commercial fleet customers understand the true safety risks that exist in their fleet leveraging a novel machine learning approach that goes beyond a generic score. TO BE CONT’D

View Full Project Description
Faculty Supervisor:

Roger Grosse

Student:

Meng Zhang

Partner:

Geotab

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

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