U of T ENGINEERING

U of T Engineering News

A person standing with mountains and a lake in the background.

‘These opportunities are precious’: How U of T Engineering students are gaining global experience abroad

liquid injection pattern

Inspired by nature, temperature-responsive building facades could help reduce energy use from heating and cooling

Large Language Models (LLMs) have high electricity and water consumption due to the resource requirements of serving them to millions of users. This footprint can be reduced using methods developed by Professor Samin Aref (MIE) and his team, which produce smaller LLMs through quantizing their parameters. (image generated by ChatGPT)

How ‘slimmed-down’ large language models can reduce AI’s environmental and energy footprint

Keep up on the latest Engineering News

Subscribe to our Skulematters newsletter on Linkedin

Latest news

Professor Aimy Bazylak is this year’s winner of the McLean Award from the Connaught Fund and the McLean endowment. (Photo: Roberta Baker)

McLean Award recipient Aimy Bazylak is creating new technologies for sustainable energy

August 21, 2020

A steel-tethered airship, known as an aerostat, designed by Solar Ship, Inc. The company is one of several clients whose projects are facilitated by U of T Engineering’s International Virtual Engineering Student Teams (InVEST) initiative. (Photo: Solar Ship, Inc.)

How to work effectively when your team is both global and virtual

August 20, 2020

A precision flight-control test in wind with a hexacopter drone from Professor Steven Waslander‘s (UTIAS) lab. Waslander will use the funding to acquire the latest in motion-capture technology in order to develop next-generation drones. (Photo courtesy of Steven Waslander)

Five U of T Engineering projects receive funding boost for state-of-the-art research tools

August 19, 2020

In this simulation, atoms of five different chemical elements within nanoparticle are represented by different coloured spheres. A computer algorithm developed at U of T Engineering analyzes thousands of possible geometric configurations of these elements in order to predict which ones will have the best performance as industrial catalysts. (Image courtesy Zhuole Lu)

U of T Engineering researchers use machine learning to design smarter industrial catalysts

August 18, 2020

Load more stories