Projets novateurs réalisés

Conservation et retauration d’artéfacts archéologiques en Grèce

Le projet sera complété en trois parties. D’abord, j’irai étudier la conservation et la restauration d’artéfacts composés de divers matériaux en Grèce, dont le verre, à l’Institut Canadien en Grèce. Cet institut à Athènes me donnera accès à de nombreux ouvrages inaccessibles ailleurs et à plusieurs musées et sites archéologiques. Ces sites et musées me donneront des exemples concrets de bonnes et mauvaises pratiques de conservation et de restauration d’artéfacts. Ces recherches me prépareront pour un stage de six semaines à Argilos, une fouille archéologique dans le nord de la Grèce. Ici, je ferai trois semaines de conservation de terrain et trois semaines de conservation en laboratoire avec une conservatrice du musée d’Amphipolis. Finalement, de retour à Montréal, je profiterai de la bibliothèque de l’Université de Montréal pour étudier d’autres points de vue de conservation et de restauration. Je pourrai également inclure mes apprentissages sur la conservation et la restauration du verre dans un chapitre de mon mémoire, qui traite du verre mosaïqué de l’époque hellénistique. Les connaissances que j’aurai acquises sur la conservation et la restauration en général m’aideront à me préparer pour des études en conservation et en restauration d’artéfacts que je compte entreprendre en septembre 2027.

RISC-V validation internship

This project focuses on improving the trustworthiness of a RISC-V processor design intended for use in aircraft systems by carefully checking that it works correctly and securely. Modern aircraft depend heavily on software and digital hardware, so even small design errors or hidden security weaknesses can create safety risks. The project will verify a security-enhanced RISC-V core using a mix of testing, formal checks, and FPGA prototyping to confirm that it behaves as expected and that the added security features do not interfere with normal operation. By following practices aligned with avionics assurance standards, the work will produce clear verification evidence, coverage results, and reusable tools. Participating institutions benefit by gaining a validated processor prototype, hands-on experience with high-assurance verification methods, and documented results that can support future research, teaching, and certification-oriented development in safety-critical and secure computing systems.

Advancing Multimodal Hate Speech Detection in Internet Memes with Large Vision-Language Models

Online hate is increasingly spread through memes, where harmful meaning comes from the combination of images and text, making it harder to detect than text-only abuse. Through a 12-week research visit to the University of Hildesheim, I will develop and evaluate efficient AI models that can better identify hateful memes across multiple datasets while remaining practical to run at scale. Building on my prior work with Prof. Thomas Mandl’s group and my graduate training at McGill, I will systematically compare leading open-source multimodal models, test lightweight improvements that help capture subtle and context-dependent hate, and document common failure cases to guide model improvements, and will also help refine evaluation practices so results are consistent and reusable for follow-on research. The project will deliver reproducible experiments and publishable findings, strengthening international collaboration between McGill University and the University of Hildesheim and supporting both institutions’ research in trustworthy AI and online safety.

Analyse de l’accident d’avion SWA1380 avec la méthode Causal Analysis based on System Theory (CAST)

Ce projet vise à mieux comprendre comment les équipes et les systèmes parviennent à gérer des situations critiques en aviation. Il s’appuie sur l’accident du vol Southwest Airlines 1380, survenu en 2018, lorsqu’une défaillance moteur a entraîné une dépressurisation rapide de la cabine. Malgré la gravité de la situation, l’avion a pu atterrir en toute sécurité grâce à la coordination entre l’équipage, le contrôle aérien et les systèmes de l’avion. Le projet analysera cet événement en adoptant une approche globale qui considère l’ensemble du système, plutôt que de se concentrer sur des erreurs individuelles. Les résultats aideront à mieux comprendre comment la sécurité est maintenue dans des conditions extrêmes et pourront contribuer à améliorer la formation, les procédures et la gestion de la sécurité dans l’aviation.

Participatory Design of a Dual-Data Decision Support Tool for Child Welfare

The Children’s Aid Society of Toronto (CAST) is responsible for delivering high-quality child welfare services and supporting frontline workers who make complex, time-sensitive decisions affecting child safety and family wellbeing. While CAST has invested heavily in improving documentation practices and data systems, frontline staff still lack practical, accessible tools that help synthesize narrative case notes and administrative information into clear, actionable insights. As service needs grow and cases become more complex, CAST is exploring responsible, trauma-informed ways to integrate decision-support technologies that complement professional judgment.
This project supports that priority by designing and prototyping an early-stage AI-supported decision-support tool tailored for ongoing case management. Building on CAST’s existing data infrastructure, the project focuses on translating CAST-identified needs into concrete interface features, usability requirements, and prototype components that can be tested in a controlled environment. By emphasizing participatory design and iterative feedback, the project ensures alignment with frontline workflows and organizational values.
The anticipated benefits include improved decision consistency, reduced information overload for workers, clearer visibility into case trajectories, and foundational evidence needed for future, larger-scale evaluation studies. This project advances CAST’s strategic goal of developing responsible, evidence-informed innovations that enhance service quality and support better outcomes for children and families.

The Ripple Effect: An Animated Exploration of Climate Change, Ocean Health, and Global Well-Being

This project aims to develop an animated educational video on the effects of changing ocean conditions on global disease vulnerability and displacement, with an emphasis on visualizing not only the growing burden of disease due to the climate crisis but also how this burden is inequitably distributed through mechanisms of displacement and risk exposure. Aligned with the Ocean Decade Blue Catalyst (bLYSs), GRACE for NATURE, and ECO_CARE networks, this project emphasizes accessible knowledge translation and ocean literacy as a way of fostering relationships with the planet rooted in solidarity and reciprocity. This project will benefit both McMaster University, UiT The Arctic University of Norway, and ??Universidade Federal do Amazonas, by producing a publicly accessible educational resource on the complex interaction of ocean health, disease burden, and displacement that fosters deeper understanding of planetary health across diverse audiences, thus contributing to interdisciplinary, innovative approaches of thinking about climate change and our oceans.

Investigating mineral ellipsoid stucture and composition in healthy and prostate cancer bone metastases vertebral bone samples

Prostate cancer is the most diagnosed cancer in men, and the most common complication of this cancer is metastasis to bone, particularly, to the vertebrae. Mineral ellipsoids, a recently identified structural component of bone, are largely uncharacterized at both the structural and chemical level. Although these structures have been identified in mouse and human bone, their chemical composition in the vertebrae remains a fundamental question. This research aims to further our knowledge of bone structure and composition and to establish a baseline structure and chemical composition of mineral ellipsoids in healthy bone samples. By establishing this baseline on healthy tissue, the knowledge acquired could then be applied to diseased bone tissue in the future. Dr. Micheletti, an Assistant Professor at Chalmers University of Technology, is an expert on mineral ellipsoids. Her expertise in spectroscopy and her position at Chalmers will provide me with additional ways to characterize and understand bone composition. I can then return to my home institution with an increased breadth of knowledge on bone structure and characterization. The Chalmers Materials Analysis Laboratory is a leading hub for microscopy and spectroscopy in Europe, rendering this an excellent location to advance my materials characterization skills.

Sequential optimal experiment design for an industrial polymerization process

This project aims to develop an accurate model of an industrial polymerization process using a minimal number of experiments. The approach combines basic physical understanding of the process with data-driven modeling techniques to efficiently capture process behavior. An adaptive experimental design strategy will be used, where each experiment is chosen based on results from previous ones to maximize learning while minimizing experimental effort. The resulting model will support process simulation, design, and optimization, helping improve efficiency in polymer production.

Medial Lateral Separation and Age Differences on Collision Avoidance Behaviours

Avoiding collisions with other pedestrians is a daily necessity for individuals across all age groups. Unfortunately, age-related declines in older adults’ motor and sensory systems can negatively impact their ability to safely navigate the environment. For instance, decrements in muscular strength, balance, and cognitive function can impair one’s ability to adapt locomotion to environmental constraints. These age-related declines in sensory-motor processing appear to be particularly prominent when navigating dynamic environments, where timely and accurate responses are critical to safe locomotion. The objective of the proposed study is to determine whether age-related changes to sensory-motor processing affects collision avoidance behaviours. It is believed that older adults’ avoidance behaviours (onset of avoidance and clearance at the time of crossing) will be more variable, suggesting a decrease in accurate sensory-motor processing. The proposed study will help identifying age-related differences in decision-making, contributing to the broader understanding of sensory-motor processing decline. Ultimately, by addressing the nuanced challenges faced by older adults in navigating real-world urban environments, this project aims to foster safer and more inclusive communities, while advancing our understanding of human behaviours across the lifespan.

Quantum Machine Learning for Adversarially Robust Cybersecurity: Network and Phishing Attack Detection

This project aims to enhance the detection of modern cyberattacks by investigating quantum machine learning (QML) and quantum neural network (QNN) approaches alongside classical machine learning and deep learning models. As cyber threats evolve, attackers increasingly employ stealthy strategies, such as low-and-slow attacks and phishing/spear-phishing, designed to evade traditional detection systems. Moreover, many existing models remain vulnerable to adversarial attacks, where subtle input manipulations can lead to incorrect predictions.

To address these challenges, the project will first establish strong classical baselines using traditional ML algorithms and deep neural networks (DNNs). Subsequently, quantum-based models, including QML and QNNs, will be implemented to assess their potential advantages in feature representation, learning efficiency, and robustness. A comprehensive comparative evaluation will be conducted using standard metrics such as accuracy, precision, recall, F1-score, evasion rate, and robustness under adversarial perturbations. The objective is to determine whether quantum approaches can improve the reliability and resilience of cybersecurity detection systems against advanced and evasive threats.

Causal Deep Learning for Cross-Country Generalization in Multimodal Remote Sensing

This project aims to develop an automated and robust satellite-based pipeline to map hedgerows, which are import for carbon storage, biodiversity and agricultural landscapes. Even though a large amount of satellite data is available, current methods often rely on manual labeling and are sensitive to changes in region or sensors. During this internship, a machine learning model will be trained on annotated data in France and evaluated on manually labeled data in Canada to study cross-country transfer. Causal machine learning methods will be added to the model to reduce sensitivity to spurious correlations and improve generalization. The expected final result is a semi-automated scalable and reliable pipeline for detecting hedgerows across region while reducing labeling effort using foundation models.

Conductive and printable hydrogel with wet-adhesive properties

Electrical stimulation therapy has shown significant potential for activating neural precursor cells, which can undergo activation, differentiation, proliferation, and cathodal migration under an applied electrical field. Currently, this relies on rigid metal electrodes or micro-fabricated soft electrodes, both of which require transportation to the treatment site and surgical implantation. These approaches can lead to complications such as inflammation and surgical errors, highlighting the need for advances in in situ additive manufacturing of conformable surface electrodes. This requires electrode materials that are electrically conductive, printable, mechanically compliant, and capable of strong adhesion to tissue surfaces under wet conditions due to the presence of cerebrospinal fluid (CSF). Hydrogels have emerged as well-suited materials, however, most reported formulations rely on external triggers such as temperature changes or UV light for crosslinking and typically satisfy only one or two of the three key functional requirements: printability, electrical conductivity, and wet adhesion. This project aims to address this gap by developing a biocompatible hydrogel that integrates all three functionalities for direct in situ deposition. The outcomes of this work will introduce a new material platform and manufacturing strategy to the host lab, while expanding hydrogel materials development and additive manufacturing innovation within the TSMART lab.