U of T ENGINEERING

U of T Engineering News

SymbI uses its self-driving lab to identify the best combination of microorganisms for breaking down organic waste and producing the ingredients required for a palm kernel oil alternative. (photo courtesy of SymBL Innovations)

AC-U of T startup converts organic waste into high-value bioproducts

Left to right: Alumnus Nan Ge (MechE 1T3, MIE MASc 1T6, PhD 1T9) and Professor Aimy Bazylak (MIE) are the co-founders of Cardinal Volta. (photo by Paola Varhen Pacheco)

New commercialization funding will help this U of T Engineering startup turn waste heat into power

"If nobody outside a company can see what these systems are doing, it’s very hard to tell whether they’re safe," says Professor Nicolas Papernot (ECE). (photo by Nick Iwanyshyn)

U of T researcher calls for independent testing of powerful AI models

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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

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