This project will focus on the role of regulatory sandboxes in AI governance, exploring how privacy and AI oversight can work together through interviews and fieldwork in the EU and UK at a time when EU regulators are actively working to develop their AI sandboxes under the EU AI Act. The project will bridge the […]

Apex Health is an emerging Canadian digital health company dedicated to advancing equity, security, and innovation in health information management and informatics. Its operations are focused on delivering privacy-first digital health solutions, including health data analysis using machine learning and predictive analytics. Apex Health is committed to helping Canadian healthcare organizations optimize clinical outcomes, enhance […]

This project is focused to democratize enterprise-level information technology solutions for Atlantic Canadian small and medium sized businesses (SMBs) through a revolutionary one-person or small team, agile, artificial intelligence (AI)-powered consultancy model. This project will provide key innovation using cutting-edge AI tools (Claude, agentic AI), which can deliver digital transformations at 1/10th the traditional cost […]

Le projet vise à développer un système d’intelligence artificielle capable de remplacer le rôle traditionnel du scribe en clinique. Actuellement, dans certaines cliniques à Montréal, les médecins se sont regroupés pour créer des formulaires comprenant des questions essentielles à poser à un patient se présentant avec certains symptômes. Par exemple, dans le cas d’un mal […]

NovaAI Solutions will offer a suite of secure and customizable AI services designed for local businesses, including inventory forecasting using sales patterns and seasonal data, email categorization to help businesses prioritize important messages, automated reporting and business analytics summaries, offer admin task automation and document processing, and review and feedback analysis for actionable insights. The […]

The goal of this project is to bridge the fields of neuroscience and nonlinear dynamics by leveraging advanced machine learning algorithms to analyze recurrence patterns in neural wave data. Specifically, the project focuses on studying traveling cortical waves – a fundamental phenomenon in brain activity – using recurrence quantification analysis enhanced by machine learning techniques. […]

Current EEG (electroencephalogram) systems used in clinical and diagnostic settings are often bulky, expensive, and require patients to remain in a controlled environment for extended periods. This limits their accessibility and practicality, especially for patients suffering from neurological conditions such as epilepsy who require continuous, long-term brain monitoring to detect abnormal brain activity and predict […]

For many Canadians with food allergies, knowing what is in their food is important. This project helps by creating an allergen detection app that can process a picture of a product’s ingredient list from the package and then checks for known allergens. The impact of this project is helping tens if not hundreds of thousands […]

Ce projet vise à développer un système intelligent pour aider les médecins à détecter et segmenter avec précision les fractures osseuses atypiques et complexes à partir d’images médicales comme le scanner (CT) ou l’IRM. Ces types de fractures sont particulièrement difficiles à identifier avec les méthodes traditionnelles en raison de leur rareté, de leur diversité […]

This project brings together ÉTS Montréal and the American University of Beirut to design smarter ways for satellites and ground stations to share network resources so that next generation of quantum secure communications can run in a optimized way. Using quantum computer simulations, the intern will test new matching and learning algorithms that decide, in […]

This research investigates whether tensor networks can serve as interpretable surrogates for graph neural networks (GNNs). It explores whether tensor networks can approximate the functional behavior of GNNs while offering a more structured and interpretable internal representation. The project aims to quantify the contribution of nodes, edges, and features to predictions through this surrogate representation, […]

This project will develop novel circuit metrics to predict model performance under realistic noise conditions, offering a practical approach to enhancing QML trainability. The research will investigate optimal parameter resilience across different circuit depths, qubit counts, and problem types, while comparing overparameterized and underparameterized regimes. Additionally, circuit metrics will be developed to predict model performance […]