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

Requirements for emulating inertia with voltage-source converters

The research in this proposal examines the growing concern of frequency swings in modern power systems. With the increasing penetration of generation from renewable resources, the share of conventional modes of generation will be diluted and as a result the system’s natural ability to maintain its frequency is diminished. Advanced converter systems may be able to help; however, their ability to do so is limited by several factors such as converter topology and device ratings, among other things. The objective of this research is to quantify these limitations using mathematical modeling and detailed computer simulations, and prepare for development of advanced mitigating solutions.

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Faculty Supervisor:

Shaahin Filizadeh

Student:

Theja Thilekha

Partner:

TransGrid Solutions

Discipline:

Engineering - computer / electrical

Sector:

University:

Program:

Accelerate

Flow Weaver Virtual Reality Research Project

This project is intended to further the videogame industry’s understanding of user presence and immersive game design, where the player feels that they exist in a virtual space and can control a virtual environment with their physical body. Rather than trying to achieve immersion in a virtual world by escaping from the body, this project engages the player’s body on the other side of the screen through motions and mechanics enacted through virtual body ownership. The goal is to make the player feel that they actually exist in the game world and can affect it with actions that correspond to their on-screen virtual appendages. The Interns involved in this project will foreground their research by conducting tests and creating variations using the game Flow Weaver. TO BE CONT’D

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Faculty Supervisor:

Neil Randall

Student:

Judy Ehrentraut

Partner:

Stitch Media

Discipline:

Languages and linguistics

Sector:

University:

Program:

Accelerate

VR-based testing station for impairment screening

In this project, a VR-based testing station for impairment screening will be implemented. The station includes a Virtual Reality (VR) goggle (to be updated to Augmented Reality, AR, later), biophysiological measurement sensors, and an integration algorithm to integrate the result of measurement with scene construction of the VR system to implement dynamic scene rendering. The project will undergo several steps including basic scene construction and depth creation to model simple scenarios for the user and to research the implementation (and effect) of different related tests on level of impairment; sophisticated rendering algorithm creation to automatically design scenes based on the application; and dynamic scene construction to receive feedback from other measurement sensors and to implement a dynamic algorithm to update the scenes based on user’s reaction.

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Faculty Supervisor:

Jiannan Wang

Student:

Rahul Moorthy

Partner:

CannSight Technologies Inc

Discipline:

Computer science

Sector:

Manufacturing

University:

Program:

Accelerate

Readmission AI: a predictive tool to assess patient risk of hospital readmission

Logibec Readmission AI is a predictive intelligence tool that accurately identifies the patient’s likelihood of being readmitted within 45 days of discharge. Rigorously and ethically developed with machine learning techniques in partnership with three large healthcare organizations in Québec, the predictive model uses reliable, accessible and timely clinical-administrative and sociodemographic data to provide clinically relevant stratification of readmission risk. The benefits of accurately identifying high risk readmission patients include improve population health, care experience and cost.

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Faculty Supervisor:

Yoshua Bengio

Student:

Eric Girard

Partner:

Logibec

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Time-series forecasting

The Internet of things is a global infrastructure that enables advanced services by interconnecting physical and virtual things like smartphones, sensors, computers, machines or buildings. These devices typically create enormous amounts of data that can be used to create business value. Mnubo offers a software dubbed Smart Objects that is a comprehensive, full stack IoT data platform, which allows enterprises to transform IoT data into critical insights that can be used for maximizing their profit (e.g. In agriculture, one could optimize the irrigation rate to maximize crop production). In this context, small gains in the accuracy of their forecast results in big rewards for their clients, therefore, in order to stay relevant, Mnubo has invested heavily in time series prediction models. In this project our goal is to improve upon the forecasting models that Mnubo has already implemented.

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Faculty Supervisor:

Yoshua Bengio

Student:

Charles Ashby-Léporé

Partner:

Mnubo Inc

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Université de Montréal

Program:

Accelerate

Speaker Diarization

Machine-transcription of speech into text is very helpful in many scenarios. Consider the case of machine-transcription of conversation between a Doctor and a patient. If we are able partition and identify the segments of patient’s speech from those of doctor’s, then the transcribed text is more structured and can be more helpful for further use. The process of partitioni a given input audio stream into homogeneous segments according to speaker identity is called Speaker Diarization. In this project, we want to implement and improve the state-of-art method for speaker-diarization.

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Faculty Supervisor:

Yoshua Bengio

Student:

Vicki Anand

Partner:

Lyrebird AI

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Université de Montréal

Program:

Accelerate

Super resolution for MRI scans

Brain MRI scans are a critical component in the diagnosis of neurodegenerative disorders. However, there is a wide diversity in terms of the image quality and resolution obtained from different MRI scanner. In particular, it is common to find coarse resolution MRI scans (e.g. every axial slice is 3-5 mm thick), which limit the type of anatomical analysis that can be performed. The goal of this project is to develop and validate the performance of state-of-the-art super-resolution methods in 3D MRI scans, which generate high resolution MRI scans from low resolution scans.

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Faculty Supervisor:

Yoshua Bengio

Student:

Ishaan Kumar

Partner:

Arctic Fox AI

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Low data drug modeling

The project aims to facilitate the research and development of new drugs by exploring Machine Learning methodology useful for both the generation of new molecules and the prediction of molecule properties. Doing so will involve training deep learning models on a large number of small, heterogeneous datasets, with the objective of transferring learned representations quickly when faced with a new drug-discovery or drug optimization objectives. The trained models will be used for the purposes of predicting molecular properties of new drugs and generating novel molecules with high likelihood of satisfying certain properties. The multi-objective nature of designing new molecules satisfying competing objectives will be approached using techniques from Reinforcement Learning.

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Faculty Supervisor:

Yoshua Bengio

Student:

Basile Dura

Partner:

InVivo AI

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Real-time object recognition on wearable devices

The goal of the project is to implement real-time state of the art object recognition models on wearable devices. These devices aim to help people living with a visual disability by providing a description of their outdoor environment and offer navigation guidance. This would improve the experience of the users by allowing them to perform usual day-to-day tasks with much more ease and safety.

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Faculty Supervisor:

Yoshua Bengio

Student:

Rémi Lussier St-Laurent

Partner:

HumanWare

Discipline:

Computer science

Sector:

Manufacturing

University:

Université de Montréal

Program:

Accelerate

Electrical Load Forecasting

Load forecasting is an essential activity for a company like Hydro-Québec. It is necessary for objectives as varied as the management of production or the management and maintenance of the electricity network. Any significant forecasting error can result in reliability issues, loss of opportunity, or additional costs to the business. On the other hand, a good prediction would allow Hydro-Québec to generate additional sales in neighboring markets. With the deployment of its Advanced Measurement Infrastructure (AMI), Hydro-Québec now has a significant amount of new consumption data. This data can be used to improve demand forecasting, increasing reliability, decreasing expenses, and potentially generating new revenue.

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Faculty Supervisor:

Yoshua Bengio

Student:

Aditya Joshi

Partner:

Hydro-Québec

Discipline:

Computer science

Sector:

Energy

University:

Université de Montréal

Program:

Accelerate

Simplification of long sentences

The task of sentence simplification can present itself in multiple forms. It could consist in correcting the punctuation of a sentence like so:
Avant : J’ai acheté un bateau je l’aime beaucoup.
Après : J’ai acheté un bateau. Je l’aime beaucoup.
However, a sentence can be both long and written correctly. In this case, it would require a reformulation in multiple sentences like so:
Avant: J’ai acheté un grand bateau à la foire nautique qui a eu lieu à Montréal plus tôt cette année et j’ai pu l’essayer cet été dans les eaux du lac Massawippi lors d’un récent voyage dans les Cantons de l’Est.
After: J’ai acheté un grand bateau à la foire nautique qui a eu lieu à Montréal plus tôt cette année. J’ai pu l’essayer cet été dans les eaux du lac Massawippi lors d’un récent voyage dans les Cantons de l’Est.
The task should be accomplished using deep learning and has to work both in french and in english.

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Faculty Supervisor:

Yoshua Bengio

Student:

Philippe Marcotte

Partner:

Druide Informatique

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Université de Montréal

Program:

Accelerate

Link predicting in court

The company Lexum is an undisputed leader in the development of information retrieval tools for the law – statutes, regulations and decisions of courts and tribunals. The project is to improve a new tool offer by the company. The tool is used to retrieve a list of legal subjects from a factual description. With that list extract, the tool provides a list of potential related document.

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Faculty Supervisor:

Yoshua Bengio

Student:

Fanny Salvail-Bérard

Partner:

Lexum informatique juridique inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

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