Security Analysis of the Rainbow-eta Signature Scheme

Digital signatures are used as electronic alternatives to handwritten signatures, and are built upon mathematical problems. Such problems are computationally unfeasible to solve with classical computers, and give the security basis for signature schemes. With the possible advent of quantum computers, conventional signatures created by currently used schemes cannot be considered secure. Thus, the need […]

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Behavioral Clustering in Big Data with Application in Super Customer Networks

Analyzing customer behavioral patterns and attrition aids organizations in understanding its core customers and improve its decision-making processes in regards to customer attrition and targeted marketing. In this research project, we will develop behavioural analytics super network models and algorithms for behavioural customer segmentation and attrition prediction in the presence of big data. Our models […]

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Supersingular Isogeny-Based Cryptography

In the near future the way that we encrypt and authenticate information online may not be safe. For this reason, we need to create new tools that will enable secure communication for many coming years. The proposed research is to create such tools from a certain algebraic object called isogenies. These are functions that take […]

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Bond Pricing AI Improvement

The fixed-income market consists of government and corporate bonds and other debt instruments which are used to finance operations and capital investments. The bond market remains heavily reliant on exchanges of information between counterparties and as a result information on prices is decentralized and market participants operate with different levels of information. The objective of […]

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Variational methods for pipeline safety and data analysis

This project will explore the non-invasive ways to find potential leaks in buried gas distribution pipelines using sound propagation. When there is a sound source at one point of the pipeline, the nature of the sound coming to another point of the pipeline will depend on the properties of the surrounding soil, properties of the […]

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Modelisation et tarification des couts en assurances dommages

La modelisation des couts par assure en assurances dommages constitue la base du processus de tarification. Les couts peuvent provenir des trois couvertures d’un contrat d’assurance automobile dommages materiels au vehicule,dommages corporels subits par I’assure et dommages corporels subits par les tierces parties. Bien qu’un accidentpeut engendrer des couts pour ces trois couvertures, la modelisation […]

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Development and Application of Marine Mammal Density Estimation Methods for Directional and Omnidirectional Hydrophones

Estimates of the population density of marine mammals in an area and the change in population over space and time are critical inputs for managing the interactions of human activity and mammal populations. Visual surveys from boats, shore stations, and aircraft have served as the basis for most population estimates currently used by managers. However, […]

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Modeling Default Risk for a Small Lender using Machine Learning

Individuals with limited or poor credit history are often unable to access credit from traditional sources such as banks. While some alternative lenders will provide credit to such individuals, these lenders typically lack reliable sources of information which can be used to accurately assess the risk that the loans they make will not be repaid, […]

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L’intelligence artificielle au service de l’industrie de l’assurance

L’évolution technologique révolutionne les processus administratifs dans les banques et les assurances, mais entraîne également de nouveaux défis, en particulier au niveau de la reconnaissance de l’écriture manuscrite et imprimée dans les documents qui doivent être numérisés pour extraire automatiquement l’information pertinente. Les systèmes de dialogues automatisés connaissent également un succès grandissant, grâce à l’utilisation […]

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Equivariant Siamese Neural Networks

The world we live in is ripe with symmetry. From the bilateral symmetry we see in humans to the symmetries which are used to describe fundamental particles in physics. Most modern machine learning methods however do not have an inherent modeling of symmetry in them. By developing algorithms which do have an explicit modeling of […]

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Uncertainty Quantification for Deep Neural Networks

Deep neural networks are effective at image classification and other types of predictive tasks, achieving higher accuracy than conventional machine learning methods. However, unlike these other methods, the predictions are less interpretable. While accuracy may be enough for applications where errors are not costly, for real world applications, we want to also know when the […]

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Predicting the Behavior of Loyalty Programs Customers Using Interpretable Patterns Based on Logical Analysis of Data

Aeroplan Inc. (“Aeroplan”) , aims to redesign and optimize its loyalty program Aeroplan via a collaboration with Polytechnique Montréal. Customers affiliated with Aeroplan’s program earn miles through their purchases and can exchange these miles for various gifts. It is essential for Aeroplan to predict customers behavior, to define the causes of certain behaviors and to […]

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