Raquel Urtasun is a computer scientist specializing in Machine learning and Artificial intelligence, with a focus on computer vision and autonomous driving. She is a professor at the University of Toronto and the founder and CEO of Waabi, an autonomous trucking company. Her research has advanced 3D perception, probabilistic inference, and the application of Deep learning to self-driving vehicles.
Urtasun was born in Spain and earned her undergraduate degree in computer science from the Polytechnic University of Catalonia. She completed her PhD at the Swiss Federal Institute of Technology in Lausanne (EPFL) in 2006, where her dissertation addressed probabilistic models for visual tracking and scene understanding. She subsequently held postdoctoral positions at Carnegie Mellon University and the University of California, Berkeley, before joining the faculty at the University of Toronto in 2011.
Academic Career and Research
At the University of Toronto, Urtasun leads the Machine Learning and Computer Vision lab, where she has supervised numerous graduate students and postdoctoral fellows. Her early work focused on structured prediction and probabilistic graphical models for computer vision, including methods for estimating 3D human pose and vehicle trajectories from monocular images. She contributed to the development of differentiable rendering and neural network architectures for 3D scene reconstruction, which became foundational for modern autonomous perception systems.
Urtasun has published over 200 peer-reviewed papers in venues such as the Conference on Computer Vision and Pattern Recognition (CVPR), the International Conference on Computer Vision (ICCV), and the Conference on Neural Information Processing Systems (NeurIPS). She has received multiple best paper awards and has served as an area chair and senior program committee member for major conferences. Her work has been cited tens of thousands of times, reflecting its influence on both academic research and industrial applications.
Industry Contributions
Before founding Waabi, Urtasun was a principal scientist at Uber Advanced Technologies Group (ATG) from 2017 to 2019, where she led the perception team. At Uber ATG, she oversaw the development of perception algorithms for self-driving cars, including LiDAR-based object detection and sensor fusion. Her team's work contributed to the deployment of autonomous vehicles in Pittsburgh and Toronto, though the program faced regulatory and safety challenges.
In 2021, Urtasun founded Waabi, a company focused on autonomous trucking. Waabi's approach diverges from the data-heavy methods of competitors like Waymo and Cruise by emphasizing a closed-loop simulation platform called Waabi World. This simulator allows the AI system to learn from synthetic scenarios and edge cases, reducing the need for extensive on-road testing. Waabi has raised over $200 million in funding from investors including Khosla Ventures, Uber, and NVIDIA, and has partnered with logistics companies such as Uber Freight and Volvo Autonomous Solutions.
Recognition and Awards
Urtasun has received numerous honors for her contributions to AI. She was named a Canada CIFAR AI Chair in 2018 and a Fellow of the Royal Society of Canada in 2020. In 2021, she was included in the MIT Technology Review's list of 35 Innovators Under 35, and in 2022 she received the Governor General's Innovation Award. She has also been recognized by the Canadian government for her leadership in the field of autonomous vehicles.
Impact and Legacy
Urtasun's work has shaped the trajectory of autonomous driving research, particularly in the areas of 3D perception and simulation. Her emphasis on simulation-based training has influenced industry practices, with many companies adopting similar closed-loop testing methodologies. As a professor, she has mentored a generation of researchers who now work at major AI labs and companies, including Google DeepMind, OpenAI, and Tesla.
Her advocacy for open research and collaboration has led to the release of several datasets and open-source tools, such as the TorontoCity dataset and the PointNet-based models for LiDAR processing. These resources have become standard benchmarks in the field. Urtasun continues to teach and advise at the University of Toronto while leading Waabi, balancing academic rigor with entrepreneurial innovation.
Current Work
As of 2025, Waabi is scaling its autonomous trucking operations, with plans to deploy driverless trucks on major freight corridors in North America. Urtasun remains active in the research community, publishing papers on topics such as uncertainty estimation and safety-critical decision-making. She is also a vocal proponent of responsible AI development, emphasizing the importance of safety validation in autonomous systems.