Projets novateurs réalisés

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

31 914 projets complétés

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5256
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878
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703
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913
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9488
ON
9949
QC
98
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629
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1215
NS

Projets par catégorie

Robust Multi-Source Retrieval and Verification for Multi-Agent QA Systems

This project will strengthen an AI question-answering system by improving how it finds and checks information. It focuses on building a retrieval pipeline that can search across multiple document collections and then filter the results to keep only the most relevant pieces. She will also design a “verification agent” that double-checks whether each answer is really supported by the retrieved evidence and flags possible mistakes. The project adds a robust information layer to an existing multi-agent AI framework, deepening expertise in trustworthy and explainable AI. For the home institution, it offers concrete experience with end-to-end AI system engineering, from data pipelines to interfaces, and creates a basis for future joint projects, student exchanges, and shared publications.

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Superviseur du corps professoral :

Yassine Yaakoubi

Étudiant :

Partenaire :

École nationale des sciences de l'informatique

Discipline :

Engineering

Secteur :

Education

Université :

Concordia University

Programme :

Globalink Research Award

Uncertainty-Driven Adaptive Orchestration in Multi-Agent Retrieval-Augmented QA Systems

This project will build a new kind of AI question-answering system where several specialized “agents” work together under an adaptive controller. Instead of a single model answering a question in one shot, Omar’s work designs a coordinator that plans the steps, decides when more information is needed, and measures how confident the system should be before giving an answer. By using uncertainty signals and feedback from evidence, the controller can trigger extra retrieval or reasoning only when necessary, making answers both more reliable and more efficient. It also provides the student hands-on experience with advanced AI methods, supports thesis work, and strengthens long-term partnerships and joint publications between both institutions.

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Superviseur du corps professoral :

Yassine Yaakoubi

Étudiant :

Partenaire :

Tunisia Polytechnic School, University of Carthage

Discipline :

Computer science

Secteur :

Artificial Intelligence; Information and Communications Technology (ICT); Technology

Université :

Concordia University

Programme :

Globalink Research Award

Waste Characterization Study

The Textile Transformation Nova Scotia (TTNS) Phase II project will study how Nova Scotians donate, reuse, and discard textiles to reduce waste and build a circular economy. Led by EnsembleCo Consulting Services and the National Association for Charitable Textile Recycling, this project includes a province-wide textile waste audit, donation behaviour study, and market analysis for recycled textile feedstock. The work will generate the first quantitative data on what types of textiles are being discarded in Nova Scotia and why, helping Divert NS, Environment and Climate Change Canada, and other partners design policies to keep clothing in use longer and support future recycling infrastructure. A graduate student from the University of Alberta will gain hands-on experience in circular economy research, sorting samples, analyzing fibres, and studying donation behaviours, while supporting Nova Scotia’s leadership in sustainable resource management and Extended Producer Responsibility (EPR) policy development.

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Superviseur du corps professoral :

Rachel McQueen

Étudiant :

Partenaire :

National Association for Charitable Textile Recycling

Discipline :

Earth science

Secteur :

Retail trade

Université :

University of Alberta

Programme :

Business Strategy Internship

SMART-Admin NBCGSS

NBCGSS will collaborate with UNBC’s Computer Science department to integrate artificial intelligence solutions into administrative operations, addressing critical organizational efficiency challenges. This project aims to automate time-consuming manual processes, reduce dependency on individual staff members, and create sustainable operational systems for the 600+ graduate student community.
The project will focus on AI integration into documentation systems, communication workflows, and database management. Two implementation approaches will be evaluated: developing custom AI systems versus integrating existing AI tools into current operations. The chosen approach will optimize administrative task automation, improve filing systems, and streamline communication processes.
NBCGSS operates with a $300,000+ annual budget and faces significant administrative pressure on leadership roles. Current challenges include manual data entry, semi paper-based expense tracking, delayed reporting, and limited real-time operational visibility. The AI implementation will directly address these pain points while creating transferable solutions for other student organizations.
The intern will gain practical experience in applied AI development, working with real organizational data and operational constraints. This hands-on exposure to AI implementation in non-profit settings provides valuable industry-relevant skills, evidenced by previous success stories where similar projects led directly to employment opportunities in healthcare and other sectors.
Expected outcomes include measurable reductions in administrative overhead, improved accuracy in routine tasks, enhanced real-time reporting capabilities, and development of scalable AI solutions. The project will serve as a model for AI integration in student organizations across Canada, contributing to broader innovation in non-profit operational efficiency.
This collaboration strengthens the partnership between NBCGSS and UNBC while providing meaningful employment and professional development opportunities for graduate students, directly supporting the Northern BC graduate student community’s career advancement and practical skill development.

Voir la description complète du projet
Superviseur du corps professoral :

Terry Fan Jiang

Étudiant :

Partenaire :

Northern BC Student Graduate Society

Discipline :

Computer science

Secteur :

Administrative and support, waste management and remediation services

Université :

University of Northern British Columbia

Programme :

Business Strategy Internship

Prédiction des événements medicaux à partir des signaux physiologiques

Les signaux physiologiques (ECG, EEG, EMG, CGM, etc.) constituent des indicateurs essentiels de l’état de santé et permettent de détecter ou anticiper des événements critiques tels que les crises épileptiques, l’hypoglycémie ou certaines anomalies cardiaques. Cependant, leur variabilité inter-individuelle, leur bruit et leur forte complexité temporelle rendent leur modélisation difficile pour les approches traditionnelles.
Ce projet propose une approche hybride combinant modèles profonds, méthodes d’IA interprétable et calcul quantique pour améliorer la prédiction automatique d’événements médicaux à partir de séries temporelles physiologiques. Le premier volet développe des modèles interprétables appliqués aux signaux CGM afin d’identifier les motifs annonçant une hypoglycémie imminente, un enjeu clé pour renforcer la confiance clinique et soutenir la prise de décision.
Le second volet explore l’apport du calcul quantique, notamment les Quantum Transformers et réseaux récurrents quantiques, pour détecter les crises épileptiques à partir du signal ECG. Ces modèles pourraient mieux capturer des motifs subtils, non linéaires ou de longue dépendance, difficiles à représenter avec les architectures classiques.
Les expérimentations s’appuieront sur des bases de données publiques afin de garantir transparence, reproductibilité et contribution durable à la santé numérique.

Voir la description complète du projet
Superviseur du corps professoral :

Khadidja Henni

Étudiant :

Partenaire :

École Supérieure en Informatique Sidi Bel Abbès

Discipline :

Computer science

Secteur :

Education

Université :

Université TÉLUQ

Programme :

Globalink Research Award

Algorithms the Dynamic Hoist Scheduling Problem with Multiple Hoists

The project develops new optimization methods for a complex industrial scheduling problem involving multiple hoists moving parts through electroplating tanks under strict timing and safety constraints. Unlike existing models, the project focuses on a dynamic setting where new tasks can appear during production and where multiple hoists share tracks and must coordinate movements. The work will produce an ILP formulation and lightweight heuristic algorithms capable of generating fast, near-optimal schedules that can scale to real factory environments. The collaboration between Politecnico di Milano and Concordia University strengthens shared expertise in discrete optimization and industrial scheduling, supports ongoing joint research, and brings advanced modeling techniques, practical algorithms, and industrial datasets to both institutions.

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Superviseur du corps professoral :

Tommaso Schettini

Étudiant :

Partenaire :

Politecnico di Milano

Discipline :

Computer science

Secteur :

Education

Université :

Concordia University

Programme :

Globalink Research Award

Graph Kolmogorov-Arnold Networks for 3D point cloud data analysis, with applications in tree species recognition and brain connectivity analysis

This project aims to develop advanced deep learning models that combine graph-based neural networks with Kolmogorov-Arnold Networks (KAN) to improve the analysis of 3D point cloud data. These models will be applied to real-world problems such as identifying tree species from laser scanning data and studying brain connectivity from medical imaging. The collaboration will benefit Concordia University by advancing research in AI and 3D data processing, strengthening its expertise in environmental and medical applications. ENSIAS, Mohammed V University, will gain hands-on experience with novel neural network architectures and practical deployment strategies, enhancing its capacity for innovation in machine learning and data science. Together, the institutions will foster knowledge exchange and create solutions with strong academic and societal impact.

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Superviseur du corps professoral :

Abdessamad Ben Hamza

Étudiant :

Partenaire :

Université Mohammed V de Rabat

Discipline :

Computer science

Secteur :

Artificial Intelligence

Université :

Concordia University

Programme :

Globalink Research Award

Agentic AI Frameworks for Multi-Domain Intelligent Assistants

This project aims to help ALS improve how employees access and use information by creating an advanced AI assistant that can understand questions, search internal documents, and provide accurate answers across HR, training, and technical departments. ALS manages large amounts of complex material—such as lab procedures, policies, and technical reports—which can be difficult and time-consuming for staff to navigate. The proposed system will use modern AI and retrieval methods to unify these knowledge sources, reduce repetitive questions, and support faster decision-making. By developing this tool, ALS will benefit from improved efficiency, less reliance on subject-matter experts, and a more streamlined way for employees to find the information they need to work effectively.

Voir la description complète du projet
Superviseur du corps professoral :

Jake Doliskani

Étudiant :

Partenaire :

ALS Global

Discipline :

Computer science

Secteur :

Mining

Université :

McMaster University

Programme :

Business Strategy Internship

Towards Explainable and Privacy-Preserving LLM Frameworks for Personalized Healthcare Part 2

The project is the direct continuation of application #IT47869, extending the work initiated in the first 24-week phase toward a more scalable, robust, and deployment-ready Large Language Model framework for healthcare. While the initial unit focused on designing the system architecture, implementing explainability and privacy mechanisms, and establishing preliminary evaluation methods, this second phase will advance the prototype into an optimized, rigorously validated, and audit-ready system suitable for real-world clinical contexts. The research will include performance optimization, cost–efficiency benchmarking, expanded user studies with stakeholders, and fairness-oriented audits to ensure equitable, transparent, and reliable model behaviour across different patient and clinician profiles. Through this work, the student will gain advanced skills in system profiling, scalable evaluation, bias assessment, and privacy-aligned optimization, competencies that are increasingly essential for deploying trustworthy AI in healthcare. This continuation will strengthen the student’s academic development, support high-impact publications, and consolidate their preparation for future leadership roles in human-centred and responsible AI research.

Voir la description complète du projet
Superviseur du corps professoral :

Glaucia Melo dos Santos

Étudiant :

Partenaire :

Universidade Federal do Rio de Janeiro

Discipline :

Computer science

Secteur :

Education

Université :

Toronto Metropolitan University

Programme :

Globalink Research Award

Development of an Automated Simulation Pipeline for Assessing Effectiveness of Far-UV Disinfection of Air

This project aims to help hospitals reduce the spread of airborne infections—such as influenza, COVID-19, and other respiratory illnesses—by improving how a new disinfecting light technology called far-UVC is deployed. Far-UVC light can safely kill viruses and bacteria in the air while people are present in the room, but hospitals currently have no reliable way to predict how many lamps they need or where to place them for maximum protection. Working with UVX Inc., the student researcher will build a “digital twin” tool that uses 3D scans of real rooms to simulate how air moves, how germs spread, and how effectively far-UVC light can inactivate them. This tool will allow infection-control teams to test different lamp layouts virtually before purchasing equipment. The expected benefit to UVX is faster product adoption, lower installation costs for customers, and strong scientific validation of their technology. More broadly, this work will support safer hospitals, reduced energy use compared with ventilation-only approaches, and improved readiness for future pandemics.

Voir la description complète du projet
Superviseur du corps professoral :

Christopher DeGroot

Étudiant :

Partenaire :

UVX

Discipline :

Engineering

Secteur :

Manufacturing

Université :

The University of Western Ontario

Programme :

Accelerate

Can LLMs work in a spectrum of thought

Large Language Models (LLMs) are increasingly used in everyday life, yet they often make decisions based on internal assumptions and tendencies that users cannot see. These hidden patterns, called inductive biases, shape how models interpret concepts, judge situations, and respond to people. Understanding these biases is essential for building safer and more trustworthy AI systems.
To explore this, we introduce a novel game-like activity inspired by the psychological dynamics of the party game Wavelength. In this activity, an AI model must place different concepts along a spectrum of opposites, such as cold ? hot or more subjective dimensions like safe ? dangerous or moral ? immoral. This simple interaction offers a powerful way to reveal how LLMs internally organize meaning and make conceptual judgments.
Building on this idea, the project develops a controlled experimental pipeline grounded in mechanistic interpretability techniques. By comparing multiple LLMs, analyzing layer-wise contributions, and applying targeted causal interventions to internal components, we investigate how these interventions shift the model’s conceptual decisions. This allows us to map where certain biases arise and how stable or fragile they are under perturbation.

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Superviseur du corps professoral :

Leila Kosseim

Étudiant :

Partenaire :

University of Copenhagen

Discipline :

Computer science

Secteur :

Education

Université :

Concordia University

Programme :

Globalink Research Award

Computational Investigation of Shear Failure in Geomaterials

This project focuses on understanding how concrete and similar building materials behave as they age, crack, and deteriorate. Material modeling is a way of using computer simulations to predict how a structure will react to forces such as weight, weather, and damage over time. By improving these simulations, engineers can better evaluate the safety of old bridges, columns, and even historical masonry buildings. The internship at TU Delft, one of the world’s leading universities in concrete modeling, will help adapt and refine Concordia University’s existing material models by comparing them with Delft’s well-established approaches. Through this collaboration, both institutions will gain deeper insight into how damage in small parts of a structure can influence the behaviour of the whole system. The project strengthens research ties between the two universities and contributes to safer and more reliable infrastructure worldwide.

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Superviseur du corps professoral :

Emre Erkmen

Étudiant :

Partenaire :

Delft University of Technology

Discipline :

Engineering

Secteur :

Education

Université :

Concordia University

Programme :

Globalink Research Award

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