The project aims to equip Hydro-Québec’s current and future fleet of inspection robots with autonomous inspection capabilities. The three main objectives of the project are: 1. Leverage breakthroughs in artificial intelligence to enable robotic vehicles to realize real-time automated visual inspection of the company’s infrastructure. 2. Facilitate and accelerate deep neural network (DNNs) machine learning […]

State-of-the-art forecasting: Demand planning is a critical part of a business’ operations. Traditional approaches to forecasting use statistical methods to predict future demand from past transactions, but do not take into account contextual data. However, there are good reasons to believe that contextual data – such as weather, events, product descriptions, sentiment analysis (from reviews, […]

Le projet consiste à optimiser les pratiques de gestion de la publicité sur les médias sociaux (SMM) pour plusieurs clients de l’agence de Marketing Numérique Click & Mortar. Le SMM inclut plusieurs pratiques d’optimisation de la présence sur les réseaux sociaux tels que Facebook ou Instagram. Les algorithmes sophistiqués utilisés pour filtrer le contenu publié […]

The proposed research will enable customers to see personalized recommendations based on multiple factors such as their order history, their preferences and contextual information such as the meteo and the day of the week. The main expected benefit is to increase the average bill by showing personalized recommendation

Le FerroSilicium (FeSi) est produit dans un four à arc à l’aide de plusieurs matières premières qui sont le quartz, le charbon, le bois et la ferraille d’acier. C’est un procédé complexe à opérer puisque les matières premières sont réparties inégalement dans le four, il est difficile de mesurer des données à l’intérieur du four […]

Modern machine learning is powered by deep neural networks composed of many interconnected layers of artificial neurons, whose tunable connections learn from data to solve important problems. While this approach has achieved incredible successes in many domains, in practice neural networks act as “black boxes” whose high-level insights are hard to access. Our project will […]

This project is addressing the problem of portfolio optimization by using reinforcement learning, an area of machine learning that has recently attracted many researchers. Its advantages compared to the conventional models of portfolio optimization are coming from its ability in incorporating many features of the assets into the asset allocation problem without relying on the […]

The BI (business intelligence) department at Reitmans is currently going through many changes. The team is growing and a new reporting/dashboarding tool, Birst, has been acquired. The BI team aims to migrate existing reports from its current BI tool, Microstrategy, to Birst. In order for end users to adopt this new tool, they need to […]

The intent of this project is to address the analog and silicon photonic modelling portion of a silicon photonic transceiver solution that will explore new and innovative metro reach terabit optical modems. In total there are five projects that combine to create the solution. These five project areas are silicon photonic design, high-speed electronic design, […]

Le projet vise à développer de nouvelles composantes intelligentes pour un logiciel financier. Ainsi, différentes problématiques devront être résolues à l’aide de techniques statistiques et d’apprentissage machine et profond. Les données disponibles étant principalement du texte, le projet nécessite une étape de transformation afin de rendre les données utilisables dans les modèles appropriés. Des modèles […]

The project aims to facilitate the research and development of new drugs by exploring deep learning methods to process molecules and to generate new molecules. The deep learning models that will be experimented include few shot learning, generative adversarial network, and variational autoencoder. We would like to improve these methods specifically for pharmacological datasets, which […]

This project aims at evaluating whether recent results in deep learning models, trained to exploit weak labels can serve to extract meaningful lesion localizations from image-level labels, either from individual scans or given a (longitudinal) sequence thereof. To this end, we will scale up existing models that have been shown to work on 2D images […]