Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

30156 projets achevés

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812
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673
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842
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Projets par catégorie

HomeGuard: A Smart Gateway to Protect Smart Home Networks

In this project, the intern student is expected to develop develop a data collection tool that can collect data traffic from IoT devices and label them accordingly. The intern student will then use the tool to collect and label traffic from over 100 IoT devices.

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Superviseur du corps professoral :

Carol Fung

Étudiant :

Partenaire :

Universidade Federal do Rio Grande do Sul

Discipline :

Computer science

Secteur :

Technology

Université :

Concordia University

Programme :

Globalink Research Award

Modèle de mélange avec noyaux pour la classification des données de grande dimension

Les données qu’on rencontre aujourd’hui sont souvent de grande dimension. Avec les données génétiques, les signaux et les images, des méthodes d’analyse qui tiennent compte de la taille des données est plus que jamais nécessaire. Chez Hydro-Québec, une nouvelle méthode de surveillance des équipements électriques a été développée, qui fait appel à la théorie de la communication. Cette méthode a mis en évidence l’utilité de tenir compte du comportement des données aléatoires dans un espace de grande dimension, bien connu en théorie des communications. En adaptant des méthodes de classification existantes pour tenir compte de ce comportement, on pourrait les améliorer. Les méthodes que nous voulons adapter sont basées sur des modèles de mélange. Ils sont très flexible dans le sens qu’ils accommodent des données complexes comme celles provenant de l’expression génique, et elles sont compétitives d’un point de vue du temps de calcul, ce qui est important lorsqu’on traite avec des données volumineuses de grande dimension.

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Superviseur du corps professoral :

Alejandro Murua

Étudiant :

Partenaire :

Institut de Recherche Hydro-Québec

Discipline :

Mathematics

Secteur :

Professional, scientific and technical services; Utilities

Université :

Université de Montréal

Programme :

Accelerate

Phase 2 (From Research to Industrialization): Application of Transformer Models to Raw Credit Bureau Files for Improved Credit Risk Modelling Performance

We are developing a new system to help banks and financial institutions better assess credit risk—the chance that a borrower might not repay a loan. Traditional methods use standard data like credit scores, but they often miss valuable information hidden in detailed credit reports. Our project aims to use advanced artificial intelligence, specifically transformer models, to analyze raw credit bureau data more effectively. By doing this, we can create a more accurate and efficient way to evaluate credit risk. This will benefit our partner organization by improving their lending decisions, reducing financial risk, and increasing overall efficiency in their operations.

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Superviseur du corps professoral :

Vahab Khoshdel

Étudiant :

Partenaire :

Wealthsimple Technologies

Discipline :

Computer science

Secteur :

Information and cultural industries; Mining

Université :

University of Manitoba

Programme :

Accelerate

Functional correlates of central disorders of hypersomnia: a global multi-cohort analysis

The proposed project aims to identify functional brain differences between people with different types of central disorders of hypersomnolence (narcolepsy type 1, narcolepsy type 2, and idiopathic hypersomnia) By combining data from over 1,800 participants from research sites across 21 countries, the project will reveal key brain differences that enhance our understanding of the underlying mechanisms of these disorders. With current diagnostic criteria falling short, this research addresses the urgent need for novel, non-invasive biomarkers that could improve diagnosis and support the development of targeted treatments. The intern will bring valuable expertise in setting up a large-scale MRI analysis to Concordia. She will refine the analysis method and will develop a user-friendly manual that will be used by researchers worldwide. Building on the existing partnership between Concordia and Amsterdam UMC, the project will foster closer collaboration and is expected to lead to high-impact joint publications. Ultimately, it will help position Canada at the forefront of innovative, multi-site neuroimaging research, while strengthening international ties with leading research teams worldwide.

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Superviseur du corps professoral :

Thien Thanh Dang-Vu

Étudiant :

Partenaire :

Amsterdam UMC

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Life Sciences (not health)

Université :

Concordia University

Programme :

Globalink Research Award

Graph Kolmogorov-Arnold Networks for Anomaly Detection

The proposed project aims to develop a new method for finding unusual patterns in data represented as graphs, such as social networks, communication networks, or financial transactions. We proposed a novel method Graph Kolmogorov-Arnold Networks(G-KANs) to improve how we detect anomalies, which are important for identifying security threats or fraudulent activities. This project will help our participating institutions collaborate and share knowledge in artificial intelligence and its applications. The knowledge gained will benefit both academic research and industry practices, leading to better tools for analyzing data and solving real-world problems..

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Superviseur du corps professoral :

Abdessamad Ben Hamza

Étudiant :

Partenaire :

Université Abdelmalek Essaadi

Discipline :

Computer science

Secteur :

Education

Université :

Concordia University

Programme :

Globalink Research Award

Terramechanics based Traction Prediction for Lunar Rover by High-Speed Granular Scaling Law

The development of lunar rovers requires research in terramechanics to understand the interactions between wheels and loose terrain. While extensive terramechanics research has been conducted on Earth, few studies have focused on adapting these findings to the Moon’s different gravity environment. This project will focus on the Granular Scaling Law (GSL), a recently utilized mechanical similarity law, and discuss its application to terramechanics. In particular, considering both rover speed and gravity is essential for this application. The host laboratory has world-leading expertise in low-gravity experimental techniques, while the home laboratory is at the forefront of developing experimental facilities for high-speed traversal on lunar terrain. By combining these complementary technologies, this project aims to address the previously unexplored application of the GSL in terramechanics and conduct precise performance predictions for lunar surfaces. If successful, this project is expected to yield significant theoretical insights for both laboratories and contribute greatly to the future development and control of rovers in both countries.

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Superviseur du corps professoral :

Krzysztof Skonieczny

Étudiant :

Partenaire :

Tohoku University

Discipline :

Engineering

Secteur :

Aerospace

Université :

Concordia University

Programme :

Globalink Research Award

Declining and Stigmatized: An Analysis of French and Canadian Left-Behind Places

The world’s economy is changing at a rapid rate. Resources and population growth are becoming concentrated in a small number of larger cities, while other cities have become “left behind”. The dissatisfaction of living in poorer economic conditions and the resentment toward economically successful cities, has prompted resident political discontent. Left-behind places are also prone to spatial stigmatization, which refers to the way people are devalued and poorly treated due to the places they are associated with, and can affect the way residents and local decision-makers view their surrounding environment. The objectives of this study are to determine the geography of left-behind places in Canada and France, analyze policy interventions in left-behind places to identify how they address left-behindness, and examine the visual transformation of downtown cores in Canadian and French left-behind places. The proposed project will benefit the participating institutions by creating short-term and long-term collaboration opportunities between researchers, as well as opportunities for research dissemination in peer-reviewed journals and international conferences. Furthermore, this study will assess the similarities and differences between left-behind places in Canada and France, informing recommendations which contribute to a better understanding of these places in local, regional, and national level policies.

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Superviseur du corps professoral :

Maxwell Hartt

Étudiant :

Partenaire :

Centre National de la Recherche Scientifique (CNRS)

Discipline :

Sociology

Secteur :

Public Service, Policy, and Governance

Université :

Queen's University

Programme :

Globalink Research Award

Does Positive Feedback Spur Innovation? Insights into Firms’ Explorative Technology Development

Since prior research on performance feedback has primarily focused on the effects of underperformance, our understanding of the mechanisms through which positive performance feedback affects firm innovation is relatively limited. To address this gap, we will explore why, how, and when positive performance feedback spurs firm innovation and technological development. Drawing on previous studies on organizational learning and performance feedback, we plan to develop our hypotheses and test them in the context of U.S. listed firms in high-tech manufacturing industries and their patenting activities over the past 20 years. Through this international collaboration, our team aims to make important theoretical contributions to the fields of organizational learning and technological innovation. Our findings will also offer practical insights for corporate executives and innovation managers, particularly in understanding the effects of performance feedback on strategic decision-making and innovation management.

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Superviseur du corps professoral :

Young-Chul Jeong

Étudiant :

Partenaire :

Yonsei University

Discipline :

Business

Secteur :

Education

Université :

Concordia University

Programme :

Globalink Research Award

Development of AI algorithms to support oncology drug development

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

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Superviseur du corps professoral :

Jacques Corbeil

Étudiant :

Partenaire :

9Bio Thérapeutiques

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Artificial Intelligence

Université :

Université Laval

Programme :

Accelerate

Metalic organic frameworks for use in DAC

Research of metalic organic frameworks (MOFs) in direct air capture application.
MOFs are a class of porous materials, often crystalline in nature, that are made of inorganic nodes bridged by organic linkers. MOFs are highly tunable porous materials, and the metal nodes can be metal ions, chains, or clusters. Given that MOFs can be constructed from nearly any metal on the periodic table and that the library of organic linkers is also vast, the structural possibilities are nearly endless. MOFs have been studied extensively for CO2 gas capture, towards applications in direct air capture, and post-combustion capture. Due to their impressive adsorption capacities, and amenability to scale-up, large-scale carbon capture units containing MOFs are being pilot tested by companies worldwide. Herein, we will test the CO2 adsorption capacity of a series of new MOFs recently developed in the Howarth lab. Specifically, these MOFs will be tested using conditions relevant for direct air capture (DAC) applications such as gas with 298K containing 424ppm CO2. Success in this project could lead to more efficient carbon capture technologies, benefiting the participating institutions by advancing their leadership in environmental research and innovation.

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Superviseur du corps professoral :

Ashlee Howarth

Étudiant :

Partenaire :

Technische Universität München (Garching)

Discipline :

Earth science

Secteur :

Education

Université :

Concordia University

Programme :

Globalink Research Award

Levels and Patterns of Physical Activity and Sleep in people with COMISA Versus Age- and Sex-Matched Insomniacs and Good Sleepers

Obstructive sleep apnea (OSA) and chronic insomnia have each been associated with decreased levels of physical activity (PA). In turn, insufficient PA can lead to poorer sleep. For people with both OSA and insomnia – a combination called COMISA – guidelines recommend continuous positive airway pressure (CPAP) for OSA and cognitive behavioural therapy for insomnia (CBTi). However, these treatments have certain limitations and no documented effects on PA levels. Thus, adjunct therapies that can increase PA levels in COMISA would be useful. Exercise training has shown promising effects in people with OSA. The goal of the proposed project is to: i) assess PA levels and patterns in people with COMISA and compare them to those of age- and sex-matched insomniacs (INS) and good sleepers (GS); ii) evaluate PA levels in the COMISA group before and after an exercise-training intervention. This project will document, for the first time, PA levels in people with COMISA and assess their response to an exercise program. Results may reveal a new adjunct treatment in the clinical management of COMISA. This is the first research collaboration between 2 teams that have very compatible expertise: Dr. Maldonado’s team at UPV/EHU and Dr. Pepin’s team at Concordia.

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Superviseur du corps professoral :

Veronique Pepin

Étudiant :

Partenaire :

University of the Basque Country

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Other

Université :

Concordia University

Programme :

Globalink Research Award

Enhancing Productivity and Accessibility in Multilingual Book Writing Through Generative AI

This project leverages AI to streamline multilingual book creation. It supports speech-to-text, bilingual content in English and Persian, automated editing, AI audiobook narration, and version tracking. This tool aims to boost productivity, ensure consistency, and enhance author accessibility.

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Superviseur du corps professoral :

Marjan Alavi

Étudiant :

Partenaire :

North Star Success Inc.

Discipline :

Engineering

Secteur :

Education; Information and cultural industries

Université :

McMaster University

Programme :

Business Strategy Internship

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