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Founding faculty member at the Vector Institute for AI and a pioneer in teaching machines to learn
Maybe it was intuition or maybe just indecision. But at 22, David Duvenaud knew it was time to switch tracks. He’d finished his computer science degree at the University of Manitoba, but failed to win a co-op placement or get into grad school.
So while his friends headed off for higher learning, he stayed home in suburban Winnipeg, where he killed time building a giant tower out of Lego and ploughing through War and Peace. He considered planting trees, but having spent his youth doing chores on his family’s farm, he knew he loved fresh air but not the backbreaking physical labour. Instead, he joined the Army Reserve.
Fast forward 14 years, and Duvenaud finds himself looking back not on a military career but on an academic track that has led to one of the most vibrant research hubs in Canada. A founding faculty member of the Vector Institute for Artificial Intelligence in Toronto, he has gained international attention for his work in an area known as approximate inference. “You could say we’re trying to automate the process of building intuition,” says Duvenaud, now 35.
In December, he won a “best paper” award at an annual showcase for the latest in machine learning. The project, which began as an effort to predict patient health, turned into a new way to equip computer systems to deal with information that comes at unspecified intervals, rather than on a set schedule. “David combines very strong mathematical and technical knowledge with a love of working on code, so he knows his own work and the field inside and out,” says Richard Zemel, the Vector Institute’s research director.