Keynote Speakers

Raquel Urtasun

Raquel Urtasun Raquel Urtasun the Founder and CEO of Waabi Innovation Inc. She is also a Full Professor in the Department of Computer Science at the University of Toronto and a co-founder of the Vector Institute for AI. From 2017 to 2021 she was the Chief Scientist and Head of R&D at Uber ATG. From 2015-2017 she was a Canada Research Chair in Machine Learning and Computer Vision. She received her Ph.D. from the Ecole Polytechnique Federal de Lausanne (EPFL) in 2006 and did her postdoc at MIT and UC Berkeley. She is a recipient of an NSERC EWR Steacie Award, two NVIDIA Pioneers of AI Award, a Ministry of Education and Innovation Early Researcher Award, three Google Faculty Research Awards, an Amazon Faculty Research Award, a Connaught New Researcher Award, a Fallona Family Research Award and two Best Paper Runner up Prize awarded CVPR in 2013 and 2017. She was also named Chatelaine 2018 Woman of the year, and 2018 Toronto's top influencers by Adweek magazine.

Topic: TBA



Adrien Gaidon

Adrien Gaidon is currently the Senior Manager of Machine Learning Research at Toyota Research Institute (TRI) where he focuses on self-supervised learning, dynamic 3D scene understanding and using simulation data for ML. In 2008 he won the Pascal VOC 2008 challenge on object classification and detection and in 2012, he received his PhD from Microsoft Research - Inria Paris in the area of Structured Models for Action Recognition in Real-world Videos. He was a postdoctoral research scientist at Inria Grenoble and a Research Scientist in the Computer Vision Group at Xerox Research Center Europe (XRCE). His research focuses on video understanding related tasks such as: action detection and recognition, object detection, object tracking and domain adaptation from synthetic worlds to real. He published more than 50 papers at top conferences and received numerous awards for his outstanding reviewing activities.

Topic: Scalable Geometric and Synthetic Supervision for Scene Understanding

Alexander Amini

Alexander Amini is I am a PhD student at the Massachusetts Institute of Technology (MIT), in the Computer Science and Artificial Intelligence Laboratory (CSAIL), with Prof. Daniela Rus. I am a NSF Fellow and completed my Bachelor of Science and Master of Science in Electrical Engineering and Computer Science at MIT, with a minor in Mathematics. My research focuses on building machine learning algorithms for end-to-end control (i.e., perception to actuation) of autonomous systems and formulating guarantees for these algorithms. I have worked on control of autonomous vehicles, formulating confidence of deep neural networks, mathematical modeling of human mobility, as well as building complex inertial refinement systems. In addition to research, I am also a lead organizer and lecturer for MIT 6.S191: Introduction to Deep Learning, MIT's official introductory course on deep learning. In high school I was named the European Union Young Scientist of 2011 with my project entitled: Tennis Sensor Data Analysis: An Automated System for Macro Motion Refinement. I grew up in New York, and then moved to Dublin, Ireland, where I attended Castleknock College, and then returned to the US in 2012.

Topic: Data-driven simulation for multimodal and interactive autonomous driving

Deva Ramanan

Deva Ramanan is a Professor at the Robotics institute at Carnegie-Mellon University and Principal Scientist and Director of the Argo AI Center for Autonomous Vehicle Research. His research interests cover computer vision and machine learning, with a focus on visual recognition. Prior to this, he was an Associate Professor of Computer Science at the University of California at Irvine, and a Research Assistant Professor at the Toyota Technological Institute at Chicago. He received his Ph.D. in Electrical Engineering and Computer Science from UC Berkeley in 2005, and was awarded numerous prizes: David Marr Prize (2009), PASCAL VOC Lifetime Achievement Prize (2010), Longuet-Higgins Prize (2018) and the best paper awards at CVPR 2019 and ECCV 2020.

Topic: Streaming 3D Perception of Dynamic Scenes

Fisher Yu

Fisher Yu is an Assistant Professor at ETH Zürich in Switzerland. He obtained his Ph.D. degree from Princeton University and became a postdoctoral researcher at UC Berkeley. He is now leading the Visual Intelligence and Systems (VIS) group at ETH Zürich. His goal is to build perceptual systems capable of performing complex tasks in complex environments. His research is at the junction of machine learning, computer vision and robotics. He currently works on closing the loop between vision and action. His works on image representation learning and large-scale datasets, especially dilated convolutions and the BDD100K dataset, have become essential parts of computer vision research. More info is available at https://www.yf.io

Topic: Multi-Task Learning on the Road