Statistical and harmonic analysis of 3-dimensional motions of objects or humans are instrumental to establish how these motions differ, depending on various influences. When such motions involve no tearing, they may be described by elements of the Euclidean Group. Algorithms developed in the author’s Ph.D. dissertation exploit the unique properties of Conformal Geometric Algebra to […]

This project aims to automate the early detection of pandemics by constructing knowledge graphs from open-source data, by providing direct enhancement to an important R&D project, the Health Emergency Monitoring (HEM) tool, of the partner organization. The research, when completed, will provide advanced AI capabilities (more specifically the incorporation of state-of-art LLM), and reduce the […]

The internship focuses on the intersection of mathematics, machine learning, and ethical AI, specifically within the domains of conformal prediction, fairness, and calibration. Conformal prediction is a statistical framework that provides mathematically rigorous confidence measures for machine learning predictions, ensuring that the uncertainty quantification is valid under minimal assumptions. In this project, the goal is […]

Les entreprises de services sont confrontées à des enjeux logistiques majeurs liés à la conception des routes pour servir les clients. Le design de ces routes a un impact important sur les coûts de l’entreprise mais également sur la qualité du service offert. Déterminer les routes les plus efficaces pour servir un ensemble de clients […]

Polynomial factorization is a core problem in Computer Algebra, with significant applications across fields such as coding theory, cryptography, number theory, solving systems of polynomials, and algebraic geometry. This project aims to develop an efficient algorithm for factoring multivariate polynomials over algebraic number fields with multiple extensions, addressing a key computational challenge in modern algebraic […]

This project aims to develop a robust foundation model designed explicitly for financial time series representation learning. The core of this approach is an autoencoder framework capable of capturing multi-modal relationships in financial data. Once trained, the encoder will be deployed as a general-purpose model for various downstream financial tasks, including predictive analytics and asset […]

This project will investigate new methods for improving decision-making tools that help organizations make better choices when faced with uncertainty. Specifically, we will study how to more accurately estimate weights in the Analytic Hierarchy Process (AHP), a common tool used for Multi-Criteria Decision Making (MCDM). By focusing on interval and fuzzy weight estimation, the project […]

(1) The Desjardins Quantitative Strategies department manages a set of internally developed systematic investment strategies. There are two main products; global equity strategies (developed and emerging countries), as well as alternative strategies using futures on global stock indices, resources, interest rates and currencies. The team owns a proprietary technology platform that has been developed over […]

This proposal aims to integrate mathematical analysis with numerical simulations using Physics-Informed Neural Network (PINN) schemes to investigate the primitive equations, a fundamental model for atmospheric and geophysical flows. The primary focus is on achieving a precise characterization of singularity formation in these models.

HIV-1 remains a global health challenge, with over 35 million people infected. The high rates of turnover and evolutionary adaptability exhibited by HIV-1 pose a particular challenge to the use of antiretrovirals, as well as the development of a vaccine. Our focus is to understand the dynamics of two of the most commonly tracked clinical […]

This research project focuses on using advanced machine learning techniques to better predict stock prices, specifically targeting stocks from the S&P 500. By combining powerful deep learning methods—such as LSTM and GRU networks—with adaptive attention mechanisms inspired by Transformer models, the project aims to create forecasting systems that can dynamically adapt to changing market conditions, […]

Awesense is a clean tech company on a mission to accelerate the transition to clean energy by simplifying the creation of data-driven applications for a decarbonized, decentralized grid. To enable the complex planning and operational decisions required by distributed energy resources (e.g., solar, wind, batteries, EVs), Awesense developed its Digital Energy Platform. This platform allows […]