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Raquel Urtasun

Raquel is the Chief Scientist for Uber ATG and the Head of Uber ATG R&D. She is also a professor at the University of Toronto, a Canada Research Chair in Machine Learning and Computer Vision, and a co-founder of the Vector Institute for AI. Raquel received her PhD from the École Polytechnique Fédérale de Lausanne (EPFL) in 2006 and did her postdoc at MIT and UC Berkeley. She is a recipient of an NSERC EWR Steacie Award; an NVIDIA Pioneers of AI Award; a Ministry of Education, Innovation, and Research Early Researcher Award; 3 Google Faculty Research Awards; an Amazon Research Award; a Connaught New Researcher Award; a Fallona Family Research Award; and 2 Best Paper Runner-Up Prizes awarded at CVPR in 2013 and 2017. Raquel was also named a Chatelaine 2018 Woman of the Year, and a 2018 top influencers in Toronto by Adweek.

Recent publications

Physically Realizable Adversarial Examples for LiDAR Detection

James Tu, Mengye Ren, Siva Manivasagam, Bin Yang, Ming Liang, Richard Du, Frank Cheng, Raquel Urtasun (CVPR 2020)

PolyTransform: Deep Polygon Transformer for Instance Segmentation

Justin Liang, Namdar Homayounfar, Wei-Chiu Ma, Yuwen Xiong, Rui Hu, Raquel Urtasun (CVPR 2020)

OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression

Lila Huang, Shenlong Wang, Kelvin Wong, Jerry Liu, Raquel Urtasun (CVPR 2020, oral)

SpAGNN: Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data

Sergio Casas, Cole Gulino, Renjie Liao, Raquel Urtasun
(ICRA 2020, oral)

PnPNet: End-to-End Perception and Prediction with Tracking in the Loop

Ming Liang*, Bin Yang*, Wenyuan Zeng, Yun Chen, Rui Hu, Sergio Casas, Raquel Urtasun (CVPR 2020)

Efficient Graph Generation with Graph Recurrent Attention Networks

Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel (NeurIPS 2019)

Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles

Abbas Sadat*, Mengye Ren*, Andrei Pokrovsky, Yen-Chen Lin, Ersin Yumer, Raquel Urtasun (IROS 2019, oral)

Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

Wei-Chiu Ma* Ignacio Tartavull* Ioan Andrei Bârsan* Shenlong Wang* Min Bai, Gellért Máttyus, Namdar Homayounfar, Shrinidhi Kowshika Lakshmikanth, Andrei Pokrovsky, Raquel Urtasun (IROS 2019)

Identifying Unknown Instances for Autonomous Driving

Kelvin Wong, Shenlong Wang, Mengye Ren, Ming Liang, Raquel Urtasun (CoRL 2019)

Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction

Ajay Jain*, Sergio Casas*, Renjie Liao*, Yuwen Xiong*, Song Feng, Sean Segal, Raquel Urtasun (CoRL 2019)

DSIC: Deep Stereo Image Compression

Jerry Liu, Shenlong Wang, Raquel Urtasun (ICCV 2019, oral)

Learning Joint 2D-3D Representations for Depth Completion

Yun Chen*, Bin Yang*, Ming Liang, Raquel Urtasun (ICCV 2019)

DAGMapper: Learning to Map by Discovering Lane Topology

Namdar Homayounfar, Wei-Chiu Ma*, Justin Liang*, Xinyu Wu, Jack Fan, Raquel Urtasun (ICCV 2019)

DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation

Xiaohui Zeng, Renjie Liao, Li Gu, Yuwen Xiong, Sanja Fidler, Raquel Urtasun (ICCV 2019)

DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch

Shivam Duggal, Shenlong Wang, Wei-Chiu Ma, Rui Hu, Raquel Urtasun (ICCV 2019)

End-to-End Interpretable Neural Motion Planner

Wenyuan Zeng*, Wenjie Luo*, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, Raquel Urtasun (CVPR 2019, oral)

UPSNet: A Unified Panoptic Segmentation Network

Yuwen Xiong*, Renjie Liao*, Hengshuang Zhao*, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun (CVPR 2019, oral)

Convolutional Recurrent Network for Road Boundary Extraction

Justin Liang*, Namdar Homayounfar*, Wei-Chiu Ma, Shenlong Wang, Raquel Urtasun (CVPR 2019)

Learning to Localize Through Compressed Binary Maps

Xinkai Wei*, Ioan Andrei Bârsan*, Shenlong Wang*, Julieta Martinez, Raquel Urtasun (CVPR 2019)

Multi-Task Multi-Sensor Fusion for 3D Object Detection

Ming Liang*, Bin Yang*, Yun Chen, Rui Hu, Raquel Urtasun (CVPR 2019)

DARNet: Deep Active Ray Network for Building Segmentation

Dominic Cheng, Renjie Liao, Sanja Fidler, Raquel Urtasun (CVPR 2019)

Deep Rigid Instance Scene Flow

Wei-Chiu Ma, Shenlong Wang, Rui Hu, Yuwen Xiong, Raquel Urtasun (CVPR 2019)

DeepSignals: Predicting Intent of Drivers Through Visual Signals

Davi Frossard, Eric Kee, Raquel Urtasun (ICRA 2019)

LanczosNet: Multi-Scale Deep Graph Convolutional Networks

Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel (ICLR 2019)

Graph HyperNetworks for Neural Architecture Search

Chris Zhang, Mengye Ren, Raquel Urtasun (ICLR 2019)

Dimensionality Reduction for Representing the Knowledge of Probabilistic Models

Marc T. Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S. Zemel (ICLR 2019)

Neural Guided Constraint Logic Programming for Program Synthesis

Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Raquel Urtasun, Richard Zemel (NeurIPS 2018)

Deep Multi-Sensor Lane Detection

Min Bai*, Gellért Máttyus*, Namdar Homayounfar, Shenlong Wang, Shrinidhi Kowshika Lakshmikanth, Raquel Urtasun (IROS 2018, oral)

IntentNet: Learning to Predict Intention from Raw Sensor Data

Sergio Casas, Wenjie Luo, Raquel Urtasun (CORL 2018, spotlight)

HDNET: Exploiting HD Maps for 3D Object Detection

Bin Yang, Ming Liang, Raquel Urtasun (CORL 2018, spotlight)

Learning to Localize Using a LiDAR Intensity Map

Ioan Andrei Bârsan*, Shenlong Wang*, Andrei Pokrovsky, Raquel Urtasun (CORL 2018, spotlight)

Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds

Chris Zhang, Wenjie Luo, Raquel Urtasun (3DV 2018, spotlight)

Single Image Intrinsic Decomposition Without a Single Intrinsic Image

Wei-Chiu Ma, Hang Chu, Bolei Zhou, Raquel Urtasun, Antonio Torralba (ECCV 2018)

Deep Continuous Fusion for Multi-Sensor 3D Object Detection

Ming Liang, Bin Yang, Shenlong Wang, Raquel Urtasun (ECCV 2018)

End-to-End Deep Structured Models for Drawing Crosswalks

Justin Liang, Raquel Urtasun (ECCV 2018)

PIXOR: Real-Time 3D Object Detection from Point Clouds

Bin Yang, Wenjie Luo, Raquel Urtasun (CVPR 2018)

Learning Deep Structured Active Contours End-to-End

Diego Marcos, Devis Tuia, Benjamin Kellenberger, Lisa Zhang, Min Bai, Renjie Liao, Raquel Urtasun (CVPR 2018)

Matching Adversarial Networks

Gellért Máttyus, Raquel Urtasun (CVPR 2018)

SurfConv: Bridging 3D and 2D Convolution for RGBD Images

Hang Chu, Wei-Chiu Ma, Kaustav Kundu, Raquel Urtasun, Sanja Fidler (CVPR 2018)

SBNet: Sparse Blocks Network for Fast Inference

Mengye Ren*, Andrei Pokrovsky*, Bin Yang*, Raquel Urtasun (CVPR 2018, spotlight)

Hierarchical Recurrent Attention Networks for Structured Online Maps

Namdar Homayounfar, Wei-Chiu Ma, Shrinidhi Kowshika Lakshmikanth, Raquel Urtasun (CVPR 2018)

Deep Parametric Continuous Convolutional Neural Networks

Shenlong Wang*, Simon Suo*, Wei-Chiu Ma, Andrei Pokrovsky, Raquel Urtasun (CVPR 2018, spotlight)

Fast and Furious: Real-Time End-to-End 3D Detection, Tracking, and Motion Forecasting with a Single Convolutional Net

Wenjie Luo, Bin Yang, Raquel Urtasun (CVPR 2018, oral)

GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation

Xiaojuan Qi, Renjie Liao, Zhengzhe Liu, Raquel Urtasun, Jiaya Jia (CVPR 2018)

MultiNet: Real-Time Joint Semantic Reasoning for Autonomous Driving

Marvin Teichmann, Michael Weber, Marius Zöllner, Roberto Cipolla, Raquel Urtasun (IV 2018)

End-to-End Learning of Multi-Sensor 3D Tracking by Detection

Davi Frossard, Raquel Urtasun (ICRA 2018)

Learning to Reweight Examples for Robust Deep Learning

Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun (ICML 2018, spotlight)

Reviving and Improving Recurrent Back-Propagation

Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel (ICML 2018, oral)

Inference in Probabilistic Graphical Models by Graph Neural Networks

KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow (ICLR 2018, workshop)

Leveraging Constraint Logic Programming for Neural Guided Program Synthesis

Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Raquel Urtasun, Richard Zemel (ICLR 2018)

Graph Partition Neural Networks for Semi-Supervised Classification

Renjie Liao*, Marc Brockschmidt, Daniel Tarlow*, Alexander L. Gaunt, Raquel Urtasun, Richard Zemel (ICLR 2018, workshop)

The Reversible Residual Network: Backpropagation Without Storing Activations

Aidan N. Gomez*, Mengye Ren*, Raquel Urtasun, Roger B. Grosse (NeurIPS 2017)

Few-Shot Learning Through an Information Retrieval Lens

Eleni Triantafillou, Richard Zemel, Raquel Urtasun (NeurIPS 2017)

Situation Recognition with Graph Neural Networks

Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler (ICCV 2017)

TorontoCity: Seeing the World with a Million Eyes

Shenlong Wang, Min Bai, Gellért Máttyus, Hang Chu, Wenjie Luo, Bin Yang, Justin Liang, Joel Cheverie, Sanja Fidler, Raquel Urtasun (ICCV 2017, spotlight)

Be Your Own Prada: Fashion Synthesis with Structural Coherence

Shizhan Zhu, Sanja Fidler, Raquel Urtasun, Dahua Lin, Chen Change Loy (ICCV 2017)

SGN: Sequential Grouping Networks for Instance Segmentation

Shu Liu, Jiaya Jia, Sanja Fidler, Raquel Urtasun (ICCV 2017)

3D Graph Neural Networks for RGBD Semantic Segmentation

Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun (ICCV 2017, oral)

Towards Diverse and Natural Image Descriptions via a Conditional GAN

Bo Dai, Sanja Fidler, Raquel Urtasun, Dahua Lin (ICCV 2017, oral)

DeepRoadMapper: Extracting Road Topology from Aerial Images

Gellért Máttyus, Wenjie Luo, Raquel Urtasun (ICCV 2017)

Efficient Multiple Instance Metric Learning Using Weakly Supervised Data

Marc T. Law, Yaoliang Yu, Raquel Urtasun, Richard S. Zemel, Eric P. Xing (CVPR 2017)

Annotating Object Instances with a Polygon-RNN

Lluís Castrejón, Kaustav Kundu, Raquel Urtasun, Sanja Fidler (CVPR 2017, oral)

Deep Watershed Transform for Instance Segmentation

Min Bai, Raquel Urtasun (CVPR 2017)

Sports Field Localization via Deep Structured Models

Namdar Homayounfar, Sanja Fidler, Raquel Urtasun (CVPR 2017)

Deep Spectral Clustering Learning

Marc T. Law, Raquel Urtasun, Richard S. Zemel (ICML 2017, oral)

Find Your Way by Observing the Sun and Other Semantic Cues

Wei-Chiu Ma, Shenlong Wang, Marcus A. Brubaker, Sanja Fidler, Raquel Urtasun (ICRA 2017)

Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes

Mengye Ren*, Renjie Liao*, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel (ICLR 2017)