David R. Cheriton School of Computer Science

Professor Martin Karsten and his former graduate student Peter Cai (MMath ’23) uncovered a subtle but powerful way to make data centres more efficient. Their research could reduce energy consumption by as much as 30 per cent by rethinking how network traffic is processed.

Valentio Iverson, Alice Moayyedi and Beihao Zhou are recipients of the Computing Research Association’s 2026 Outstanding Undergraduate Researcher Awards, an annual program that recognizes exceptional undergraduate researchers from universities across North America.

All three students received honourable mentions in this year’s competition, placing them among an outstanding group of research-focused undergraduates whose work demonstrates technical depth, originality and intellectual creativity.

Drug development is an arduous process that costs billions of dollars and can last for years or even decades. Whether scientists are trying to understand the potential interactions of two drugs or develop new applications for an existing medication, pharmaceutical research features frequent wrong turns and dead ends.

“Often, when we use machine learning to train neural networks, we’re starting from scratch,” says Bing Hu, a PhD student at the Cheriton School of Computer Science. “But by drawing on the enormous amount of domain specific knowledge coming from biology and medicine, we’re able to build more efficient, more accurate models whose predictions consistently match-up with existing data from the real world.”