News archive - 2017

Monday, December 18, 2017

SYDE Professor and PhD student wins Best Paper Award during NIPS 2017

Systems Design Engineering PhD student Devinder Kumar and Professor Alexander Wong wins the Best Paper Award at the 31st Neural Information Processing Systems (NIPS 2017) Transparent and interpretable Machine Learning in Safety Critical Environments Workshop for their work on explainable and interpretable AI for clinicial decision support: "CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy".

Wednesday, December 6, 2017

SYDE Alumnus Startup feature

Sisun Lee is a 2014 graduate from Systems Design Engineering.  In just three years he has worked at Facebook, Uber, Tesla, and has now created his own startup to cure hangovers, and selling more than $1 million in three months.  He describes his experience in his own words or the high-profile article from Business Insider.

Friday, December 1, 2017

Systems Design Professor featured in CIHR's Celebrating Health Research Storybook

Lifesavers: Diagnosis driven by artificial intelligence (AI)

Powered by AI at a fraction of the cost, new imaging technology can catch subtle signs of disease.

Today, doctors need a lot of judgement in order to detect cancer and heart disease on MRI, CT, and ultrasound scans. My research lab has developed technology that can catch subtle signs of cancer and heart disease from medical images that might otherwise be missed.

Tuesday, November 28, 2017

SYDE Professor featured in Digital Journal article about their groundbreaking AI for making AI more private and portable

New technology invented at the University of Waterloo is paving the way for artificial intelligence to break free of the Internet and cloud computing, offering a new means of portability.

Tuesday, November 28, 2017

Systems Design PhD student featured in Vice article about openining the black box of financial AI

One University of Waterloo (UW) PhD candidate has developed an AI program he says shines a light into that black box—the "white box" method—revealing the inner workings of the AI and allowing us to better understand what the computer is learning, how it is analyzing data, and why decisions are made.[Read more]