U of T Technology & Startup Explorer

Machine-learning based methods to improve traffic flow through adaptive signal control.

Toronto Annotation Suite (TORAS) is a web-based AI-powered annotation platform.

A software platform that leverages statistical inference and machine learning to make more informed decisions.

Monte Carlo Lights is a novel ATSC approach that combines state-of-the-art reinforcement learning and Monte Carlo tree search to optimize traffic flow.

This opportunity is a portfolio of hardware and software techniques that seamlessly accelerate AI/ML applications, while saving on-chip and off-chip memory, bandwidth, and processing power. These compute and compression methods have been shown to deliver a 3x boost in processing speed with 2x-4x increase in storage capabilities (up to 10x for natural language processing).

AutoScribe uses hands-free speech recognition and Artificial Intelligence to help reduce time consuming manual clinical documentation, freeing clinicians to place their focus where it should be – on their patients.

A method to extract, assess, and visualize information from electroencephalography (EEG) readings.

The two inventions captured in this technology allow for the representation, learning, and interactive editing of color palettes through a novel interface for creating color themes.