Communications of the ACM
Triple Threat: of T Computer Scientist Combines Machine Learning, Robotics, and Computer Vision
University of Toronto computer scientist Raquel Urtasun specializes in creating algorithms to help computers make decisions typically reserved for humans.
Credit: University of Toronto
Raquel Urtasun is specializing in creating algorithms to help computers make decisions previously reserved for humans, after completing postdoctoral fellowships at the Massachusetts Institute of Technology and the University of California, Berkeley.
As an undergraduate, Urtasun was fascinated by machine learning, computer vision, and robotics. "In many applications, you can't disentangle these three things," says Urtasun, who is now a computer scientist at the University of Toronto.
She is especially excited about how machine learning and computer vision could help make self-driving cars an affordable reality by programming inexpensive sensors to be smarter and more robust. Urtasun's work on self-driving cars has been recognized with accolades and honors such as being named Canada research chair in machine learning and computer vision, and receiving two Google faculty awards, an Early Career award, and best paper award at the Computer Vision and Pattern Recognition conference (CVPR 2016).
"Our algorithms reconstruct the environment in [three dimensions] using one or two cameras, which make for much less expensive sensors," Urtasun says. "We can infer the intentions of the car, the cars around it, and note other things on the road like cyclists."
Another example of Urtasun's research is an algorithm that analyzes a person's photograph to determine if their outfit is stylish.
From University of Toronto
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