Innovative Projects Realized

Enhanced Optical Rulers from Nonlinear SNAP Resonators

This project brings together leading Canadian and European researchers to explore a new type of tiny optical device called a SNAP resonator, which can guide and control light with extreme precision. These devices may play an important role in future technologies such as environmental sensing, low-energy optical computing, and advanced communication systems. During a 12-week visit to Concordia University, the researcher will work closely with experts to develop new theoretical models that explain how light behaves inside these resonators, helping to interpret ongoing experiments and guide future ones. The collaboration will strengthen both institutions by combining Concordia’s world-class experimental facilities with the visitor’s specialised expertise in light-based physics, opening new research directions and supporting long-term international partnerships.

Misalignment Detector — Mining and Modelling Misaligned Code Change Descriptions Using Large Language Models

Code review plays a key role in software development by helping teams understand why a change was made and how it fits into the broader project. A major part of this understanding comes from the commit message, which is supposed to accurately describe the intent and scope of the corresponding code changes. However, in many real-world projects, commit messages are incomplete, outdated, or simply do not match what the code actually does. This misalignment creates confusion for reviewers and increases the time needed to understand and validate a change.

To address this challenge, many open-source communities discuss and correct misleading commit messages during code review. These discussions provide valuable examples of how commit messages should be improved and why certain messages fail to reflect the code.

This project builds on that real-world practice by creating a dataset of aligned and misaligned commit messages drawn from open-source code review discussions. We then train language models to automatically detect when a commit message does not accurately reflect the underlying changes and to suggest improvements.

The goal is to support developers and reviewers by making commit messages clearer, reducing review time, and improving overall code quality.

Smart mannequin platform for compression testing

This project is a collaborative research partnership designed to support Tression, a Canadian health-tech apparel company, in enhancing its product development. The primary challenge in the therapeutic apparel industry is the difficulty of accurately and efficiently testing the performance of medical-grade compression garments. The current process can be slow and subjective, making it difficult to innovate rapidly. To solve this challenge, this project will develop, build, and validate an advanced R&D testing device: a “smart mannequin leg.” This innovative prototype will feature a mechatronic system to adjust its physical dimensions, allowing it to simulate a wide variety of body types. It will also be integrated with a sophisticated sensor array to capture real-time, objective data on the exact pressure a garment applies. This new technology will provide Tression with a rapid, reliable, and data-driven process for product validation. The expected outcomes include a significant improvement in R&D productivity, a reduction in material waste from physical prototyping, and a stronger capacity for innovation.

Pure Environmental BSI Internship

This project will help Pure Environmental improve how it collects and maps important environmental and regulatory data. Pure has already built an initial version of a mapping tool in ArcGIS, and the intern will help make this system more reliable, easier to use, and able to pull in new information automatically. By strengthening this platform, Pure will be able to plan waste and water management more efficiently and identify solutions that reduce long-distance trucking and related emissions. The project benefits Pure by creating a smoother, faster digital process for updating key datasets, while supporting the company’s broader goals of environmental sustainability and operational improvement.

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.