Crossings Minds provides machine learning-based recommendation systems that allow companies to integrate their data and easily obtain on-demand personalized recommendations for their users. Currently, onboarding a new customer requires significant work on the behalf of Crossing Minds engineers to create and polish a machine learning model for that specific customer. The research project will explore […]

SOTI has accumulated a large number of devices in the past ten years, and how to diagnose and predict the status of these devices has become a very important topic. Through this research we hope to find various parameters and patterns contributing to particular negative impacts. Additionally we would like to be able to Create […]

The existing document understanding systems use machine learning methods, natural language understanding and text analysis, to validate structured trade contracts for language and economic term correctness. The system is now being expanded to allow general document understanding across a wide variety of financial documents, beginning with a focus on customer provided reference material. The proposed […]

Procedural content (text and video) is abundant on the internet, and is regularly used in our daily lives, e.g., when we follow a cooking recipe to make a dish, or watch an instructional furniture assembly video. Automatically understanding such content allows for the development of various types of AI assistants, including those that can provide […]

Traditionally, software for home products could not be changed once they were shipped. Furthermore, they could not run complex machine learning (ML) models because their computing and storage capacities are limited due to budget constraints. Recent advancements of cloud infrastructure, however, may allow such products to collect a large amount of data and continuously update […]

The goal of cross-modal recipe retrieval is to design systems that are able to find a digital recipe, given the user’s image of the food, or find its image, given its ingredients or cooking instructions. For such a cross- modal retrieval task, a common image-text representation space is needed to embed the semantic information of […]

Observable Market Data is critical for effective valuation of trades for risk management purposes within the investment bank. The valuation process requires the existence of good quality data day by day, and dating back into the mid-2000’s. Not all assets have highly liquid data available either historically or at present, and there is significant interest […]

The core value of Nureva is to provide reliable and easy-to-use audio-conferencing products that offer a good user experience and maximize productivity. A very important factor of that is to output clean audio which oftentimes in its raw form is contaminated with environmental noise, reverberation, and echo. These problems have been researched using traditional signal […]

In this Mitacs project, the BSI intern will work with the University of Toronto’s Urban Climate Action Project (UCAP) and with Social Innovation Canada to distill and build on insights gleaned from jurisdictional scans, environmental reports, and other outputs produced by the University of Toronto’s Sustainability in a Living Lab course cohort and previous ones. […]

Road safety affects everyone, not just Geotab customers. With several years of driving and environmental data from over 2 million vehicles, we have an opportunity to make our customers safer, as well as our communities and cities. To reduce accidents, we need to understand both the driving behavioral patterns that are predictive of accidents and […]

In eukaryotic cells, genetic material is stored in a specialized compartment — the nucleus — while other cellular material is mostly found in the surrounding cytoplasm. The selective exchange of materials between the nucleus and cytoplasm, known as nucleocytoplasmic transport (NCT), is vital to many important cellular processes including gene regulation and protein synthesis. NCT […]

Reinforcement learning has caught significant attention in the past decade with landmark successes including deep Q-net applied to video games and AlphaGo for the game of go. However, the gap is still wide when it comes to application to real world problems mainly due to high training cost. We propose to develop a digital model […]