Innovative Projects Realized

Collaborative Multi-Environment Approach for Systems Integration in Conceptual Aircraft Design

The aviation sector faces the critical challenge of reducing its environmental impact while meeting the growing global demand for air travel. Incremental improvements to conventional propulsion systems will not be sufficient to achieve the industry’s carbon neutrality targets, prompting researchers and aircraft manufacturers to explore novel propulsion concepts designed to significantly reduce aircraft emissions. Among these concepts, hydrogen-powered aircraft represent a promising solution for commercial flight because they can substantially reduce emissions. This project aims to address key challenges associated with the hydrogen storage in the aft fuselage and the resulting dry wing system integration, including system architecture definition and system design. This will be supported by the development of a collaborative analysis environment consisting of a framework for overall systems design from the Hamburg University of Technology and a framework for component placement optimization from Concordia University. This will enable the definition of system architectures for hydrogen-powered aircraft through a model-based systems engineering approach, while assessing integration feasibility by considering safety, maintenance, thermal, and intercomponent routing aspects. The project will enable both institutions to enhance their capabilities for advancing low-emission technologies, thereby contributing to the development of next-generation sustainable aircraft through the exchange of complementary expertise.

Investigating Large Language Models as Collaborative Teammates for Human-AI Aerial Teaming Scenarios

This project investigates the use of large language models (LLMs) as autonomous teammates to perform aircraft control, strategic reasoning, and team communication in complex aviation missions. We propose to design and evaluate an LLM-based “wingman” agent for a simulated collaborative aerial firefighting scenario, in which a human and AI fly their own aircraft to detect and extinguish wildfires. Unlike prior work that employs narrow AI systems to fulfill isolated teamwork functions, this research explores whether a single LLM can integrate real-time reasoning, mission planning, and natural language communication to enable effective human–AI teaming. This work represents the first systematic exploration of LLMs for both autonomous flight control and human collaboration, advancing the integration of language-based intelligence into mission-critical domains. Our research questions focus on (1) the feasibility of LLMs serving as general-purpose teammates in fast-paced, safety-critical aviation missions, and (2) the design choices (such as system prompting, context management, and retrieval-augmented grounding) that most influence performance, adherence to mission rules, and human trust.

ML surrogates for location problems

Electric vehicle charging infrastructure has become a central component of transportation planning as adoption of electric vehicles accelerates worldwide. Modern research models how drivers choose a station using discrete choice models, especially multinomial logit, which capture realistic preferences such as distance, queues, and charging speed. These models are computationally expensive, making large-scale location planning challenging. In this project we aim to address this complexity by approximating the logit model via surrogate models, such as neural networks, in order to both realistically model the driver preferences uncertainty and to allow the model to scale to larger instances.

Define–Assess–Learn: Development of an AI Chatbot and Pilot Evaluation of Its Effects on Student Learning

This project will develop and pilot-test a dual-function Generative AI chatbot to support student learning in an undergraduate course. The system will integrate: (1) a definition-retrieval component that provides course-aligned explanations drawn from the textbook, lecture slides, and instructor-approved materials, and (2) an assessment component that evaluates students’ written explanations of key concepts by comparing them to official course definitions and offering targeted feedback on accuracy and completeness. Together, these functions are designed to help students clarify concepts, check their understanding, and identify gaps during self-directed study.

After development, a pilot study will be conducted to evaluate usability, feasibility, and initial learning effects. The pilot will inform a future large-scale experimental study. Students will be assigned to one of three groups: (a) a traditional classroom group with no AI support, (b) a definition-only AI group that can use the chatbot for concept lookup, and (c) a definition-plus-assessment AI group that can both retrieve definitions and assess understanding. Pre/post-tests, usage logs, and short surveys will be used to determine whether AI-supported learning improves conceptual mastery and whether the assessment function offers additional benefits beyond simple definition retrieval.

Nouvelle classe de complexes hétéroleptiques d’Ir(III) pour la photoproduction d’hydrogène.

Ce projet vise à utiliser la lumière du soleil pour produire de l’hydrogène, un carburant propre capable de remplacer les énergies fossiles. Pour cela, nous développons des molécules contenant du métal iridium qu’on appelle des organométalliques, capables de capter efficacement la lumière visible et de déclencher des réactions chimiques qui nous permettent de produire de l’hydrogène vert et de consommer du CO2 dans l’air qui est considéré comme un gaz responsable du réchauffement climatique.
L’objectif est de créer une nouvelle génération de matériaux plus stables, plus durables et plus performants que ceux utilisés aujourd’hui. Ces molécules seront fabriquées en laboratoire, puis étudiées en détail pour comprendre comment leurs structures influencent leurs efficacités et leurs résistances lorsqu’elles sont exposées à la lumière.
En améliorant ces systèmes, ce projet contribuera à faire avancer les technologies permettant de transformer l’eau ou le CO2 en carburants propres comme l’hydrogène. À long terme, ces avancées pourraient aider à développer des solutions énergétiques plus durables, utiles autant pour la recherche que pour l’industrie et la transition énergétique

Experimental Investigation of RuCl3 Electronic and Magnetic Properties Toward Spintronic and Quantum Computing Applications

Since their discovery in 2017, two-dimensional (2D) magnetic materials have emergedas a powerful platform for probing magnetism at reduced dimensionality, where thermal fluctuations challenge long-range order. Among them, RuCl3 is particularly compelling due to its unusual electronic and magnetic properties and its capacity to form heterostructures with potential ferroelectric effects, positioning it as a candidate for spintronic and quantum technologies. This project proposes a research internship to investigate how doping, external fields, and heterostructure engineering shape the properties of RuCl3, with the goal of identifying new pathways toward applications in quantum electronics and spintronics.

Precision assessment of standard laboratory methods for corrosion inhibitor evaluation.

Precision assessment of standard laboratory methods for corrosion inhibitor evaluation.

Étude la valorisation de l’érable rouge au Québec

L’érable rouge (Acer rubrum), espèce en expansion au Québec sous l’effet des changements climatiques, demeure sous-valorisé faute de données techniques intégrées reliant ses propriétés à des usages industriels. Le projet vise à caractériser scientifiquement ce bois pour identifier ses applications optimales en produits biosourcés.

Not All Backports Are Equal: Analyzing the Risk/Value Trade-offs in Backport Acceptance Decisions

Ensuring the long-term stability and security of widely-used software is a critical operational challenge. A key part of this is “backporting,” the high-stakes process of deciding which updates to apply to stable product versions. Currently, developers often make these crucial risk-versus-value judgments without formal, data-driven guidance. This project will develop an intelligent decision-support framework to solve this problem. By applying state-of-the-art machine learning to historical project data, our system will learn to automatically classify backport proposals, profile their risk and value, and provide actionable recommendations. For the participating institutions, this project pioneers new techniques in AI-driven software engineering, culminating in a tangible decision-support tool. The ultimate benefit is a direct contribution to enhancing software reliability and developer productivity, strengthening the innovation capacity of the wider technology sector.

Exploring the social learning potential of community-based participatory sustainable fashion and textiles activities framed by speculation.

This internship supports the creation of an open-access publication on sustainable fashion, intergenerational learning, and craft practices. It investigates how participatory speculative design through Fashion Fictions workshops enables community textile groups to collaboratively imagine alternative fashion futures, fostering collective imagination, social learning, and agency. The project is the first Mitacs Globalink partnership between Nottingham Trent University’s Sustainable Transitions Research Group (STRG) and Concordia University’s Geography, Planning, and Environment community, bridging sustainable fashion/design and decolonial geography. The research environment provides hands-on experience in workshops, intergenerational sessions, and co-design exercises, with access to STRG networks, seminars, and interdisciplinary collaborations. The internship will support the intern’s development, contribute to an open-access publication, strengthen cross-institutional collaboration, and advance Canada’s leadership in socially grounded sustainability research.

Analytical Assessment of Bond Resistance and Failure Modes of Post-Installed Bundled GFRP bars in Concrete

Glass Fiber Reinforced Polymer (GFRP) bars have become increasingly important in civil engineering due to their exceptional resistance to corrosion, high strength-to-weight ratio, and durability when compared to traditional steel reinforcement. This has led to widespread adoption, particularly for the rehabilitation and strengthening of existing structures. Previous research has explored the mechanical performance and bond behavior of single, cast-in, and post-installed GFRP bars, providing valuable insights into how factors such as embedment length, bar diameter, and adhesive formulation affect anchorage capacity and structural integrity.
Much of the literature, however, remains focused on single-bar scenarios or post-installed steel bars; studies involving bundled post-installed GFRP bars are rare, despite these systems being increasingly applied in real retrofit projects where space or design requirements necessitate bar bundling.
The main goal of this research is to improve the analytical understanding of bond resistance and failure modes in post-installed bundled GFRP (Glass Fiber Reinforced Polymer) bars for concrete retrofit applications. The work is the central focus of my B.Eng. thesis and aims to connect experimental findings with finite element (FE) simulations to help develop practical recommendations for future design and safer construction practices.

Neural Oscillations During Sleep: Mathematical Modeling and Data Analysis

When we sleep, the brain produces rhythmic patterns of activity that help us learn, remember, and maintain healthy function. Some of these patterns travel across the brain like waves. Scientists believe these traveling waves are important for how the brain processes information, but we still do not fully understand how they form or what they do.

This project brings together researchers in Brazil and Canada to study these neural oscillations. Our team will use computer models and mathematical techniques to simulate how brain areas interact to generate wave-like patterns. We will also analyze empirical recordings of brain activity to detect when and where these oscillations occur. By combining these two approaches, the project aims to uncover the basic principles that shape neural oscillations during sleep.

Understanding these patterns is significant because disruptions in sleep rhythms are linked to conditions such as Alzheimer’s disease, epilepsy, and mood disorders. Insights from this research may help guide future studies on brain health and inspire new ways to understand — or potentially correct — abnormal brain activity.