SOTI Snap Analytics

Currently, data analytics services are not only expensive, but also often require days of delay for the data analytics report to be generated. The project focuses on making use of a user’s voice query as an input to create a data analytics response by aggregating data from a database. The successful implementation of the project […]

Read More
Diverging from the GPUs: The case for alternative architectures for training ML algorithms

Machine learning has ushered many breakthroughs in areas such as computer vision, natural language processing, speech recognition, and recommendation systems. The models used in these applications contain many parameters that need to be learned and training often requires massive amounts of computation. As such in recent years graphics processor units (GPUs) have seen wide adoption […]

Read More
Federated Learning for Language Models

The existing approach to building Machine Learning models includes gathering all the data from customers in one place and then running the training procedure using it. However, there are no guarantees personal data won’t be leaked by the one training the model. So, the customers face a difficult decision of either allowing companies to gather […]

Read More
Managing Shared State for Video Games in a Networked Multi-core Environment

Video Games require a vast array of different computations to present the desired experience. These computations must be completed consistently to make the software responsive to the user. The industry trend towards many separate processors (multi-core) in the same physical device and the emergence of network based ‘cloud’ computing have created many opportunities, but also […]

Read More
Application of Data Mining Methods for Detecting Irregularities in Securities Market

Current procedure for monitoring securities market in Canada relies heavily on tips from outside and hard coded rules and thresholds. This approach leaves many suspicious cases out of investigation (i.e. false negatives). We introduce a data mining approach to identify irregularities in securities market. These irregularities are known as suspicious cases that might be associated […]

Read More
Personalized relevance content recommendation

PC Optimum was originally built as a rewards platform. As we are moving in the path to engage more meaningfully with our customers by way of personalized & interactive experiences (content), we are building a targeting engine as a solution to surface personalized content tailored for each individual customer. The problem we are solving then […]

Read More
AI-driven detection of anomalous supplier profiles at web scale

Information gathered from internet sources has high variance and different types of noise. This causes out-of-distribution problems with downstream ML modules such as category classification and keyword extraction. The extremely large size of internet-scale datasets requires a solution that is efficient and scalable. The objective of this project is to develop a start-of-the-art anomaly detection […]

Read More
LeddarTech: Détection et suivi d’objets pour la conduite autonome à partir de données Lidar et caméra fusionnées

LeddarTech, acteur de premier plan des solutions de détection environnementale pour les véhicules autonomes et les systèmes avancés d’aide à la conduite, propose l’utilisation d’un capteur Lidar (i.e., Pixell) conjointement avec d’autres capteurs (caméras optique, Radar, IMU, etc.). Le but de ce stage serait donc de vérifier (1) l’intérêt de l’utilisation du capteur Lidar pour […]

Read More
Paradocs : Recommandations pour la clientèle des stations de ski en temps réel

Dans un monde de plus en plus connecté, les stations de ski en Amérique du Nord et à travers le monde désirent s’impliquer davantage auprès de leur clientèle pendant qu’elle est sur les pentes où dans leurs infrastructures. Favoriser l’expérience de ski et améliorer les revenus connexes sont deux motivations importantes dans le contexte actuel […]

Read More
Coveo : Algorithmes de recommandation

Ce projet s’inscrit dans la grande famille des projets de systèmes de recommandation, où un algorithme doit recommander aux utilisateurs des produits ou des documents qui seraient intéressants pour eux, basé sur ce que nous connaissons de l’utilisateur et sur sa session de recherche actuelle. Coveo a déjà des modèles de recommandation en place, dans […]

Read More
One-Shot Learning Based 3D Object Picking

Automation of processes is becoming more popular within logistics and material handling industries. As the amount of transferred objects grows there is a continuously increasing need for high efficiency, reduce cost, and of course reliability. With this project, we are utilizing the latest machine learning technology to improve the automation rate to a new level. […]

Read More