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Raquel Urtasun

Raquel Urtasun

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Best Female Scientists
2025
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Computer Science
Canada
2026

D-Index & Metrics

Best Female Scientists

D-Index
117
Citations
70645
World Ranking
630
National Ranking
19

Computer Science

D-Index
115
Citations
73033
World Ranking
185
National Ranking
5

Research.com Recognitions

  • 2026 - Research.com Computer Science in Canada Leader Award
  • 2025 - Research.com Best Female Scientists Award
  • 2025 - Research.com Computer Science in Canada Leader Award
  • 2023 - Research.com Computer Science in Canada Leader Award
  • 2022 - Research.com Computer Science in Canada Leader Award

Overview

Raquel Urtasun is affiliated with the University of Toronto in Canada. Their research focuses on various aspects of computer science and engineering, with significant contributions to fields including computer vision and pattern recognition, artificial intelligence, automotive engineering, aerospace engineering, and computational mechanics.

The scientist's work centers on key topics such as autonomous vehicle technology and safety, advanced neural network applications, video surveillance and tracking methods, advanced vision and imaging, human pose and action recognition, robotics and sensor-based localization, and anomaly detection techniques and applications.

Among their recent papers are the following:

  • Deep Continuous Fusion for Multi-Sensor 3D Object Detection, 2020, arXiv (Cornell University)
  • Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net, 2020, arXiv (Cornell University)
  • IntentNet: Learning to Predict Intention from Raw Sensor Data, 2021, arXiv (Cornell University)
  • HDNET: Exploiting HD Maps for 3D Object Detection, 2020, arXiv (Cornell University)
  • LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting, 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Raquel Urtasun has frequently published in venues such as:

  • arXiv (Cornell University)
  • 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • Lecture Notes in Computer Science
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Their collaboration network includes frequent co-authors:

  • Sergio Casas
  • Sivabalan Manivasagam
  • Ming Liang
  • Wei-Chiu Ma
  • Mengye Ren

Raquel Urtasun's publications prominently span the disciplines of computer science, with 204 publications, and engineering, with 103 publications. Subfields of study include extensive work in computer vision and pattern recognition with 141 publications, alongside contributions to artificial intelligence, automotive engineering, aerospace engineering, and computational mechanics.

Best Publications

  • Are we ready for autonomous driving? The KITTI vision benchmark suite

    Andreas Geiger;Philip Lenz;Raquel Urtasun

  • Vision meets robotics: The KITTI dataset

    A Geiger;P Lenz;C Stiller;R Urtasun

  • Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books

    Yukun Zhu;Ryan Kiros;Rich Zemel;Ruslan Salakhutdinov

  • Skip-thought vectors

    Ryan Kiros;Yukun Zhu;Ruslan Salakhutdinov;Richard S. Zemel

  • Understanding the effective receptive field in deep convolutional neural networks

    Wenjie Luo;Yujia Li;Raquel Urtasun;Richard S. Zemel

  • The Role of Context for Object Detection and Semantic Segmentation in the Wild

    Roozbeh Mottaghi;Xianjie Chen;Xiaobai Liu;Nam-Gyu Cho

  • PIXOR: Real-time 3D Object Detection from Point Clouds

    Bin Yang;Wenjie Luo;Raquel Urtasun

  • Monocular 3D Object Detection for Autonomous Driving

    Xiaozhi Chen;Kaustav Kundu;Ziyu Zhang;Huimin Ma

  • Efficient large-scale stereo matching

    Andreas Geiger;Martin Roser;Raquel Urtasun

  • Deep Continuous Fusion for Multi-Sensor 3D Object Detection

    Ming Liang;Bin Yang;Shenlong Wang;Raquel Urtasun

  • Learning to Reweight Examples for Robust Deep Learning

    Mengye Ren;Wenyuan Zeng;Bin Yang;Raquel Urtasun

  • Efficient Deep Learning for Stereo Matching

    Wenjie Luo;Alexander G. Schwing;Raquel Urtasun

  • 3D object proposals for accurate object class detection

    Xiaozhi Chen;Kaustav Kundu;Yukun Zhu;Andrew Berneshawi

  • MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

    Marvin Teichmann;Michael Weber;Marius Zollner;Roberto Cipolla

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

    Ming Liang;Bin Yang;Yun Chen;Rui Hu

  • 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

  • MovieQA: Understanding Stories in Movies through Question-Answering

    Makarand Tapaswi;Yukun Zhu;Rainer Stiefelhagen;Antonio Torralba

  • Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts

    Xianjie Chen;Roozbeh Mottaghi;Xiaobai Liu;Sanja Fidler

  • Learning Lane Graph Representations for Motion Forecasting

    Ming Liang;Bin Yang;Rui Hu;Yun Chen

  • Deep Watershed Transform for Instance Segmentation

    Min Bai;Raquel Urtasun

  • 3D People Tracking with Gaussian Process Dynamical Models

    R. Urtasun;D.J. Fleet;P. Fua

  • MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

    Marvin Teichmann;Michael Weber;Marius Zoellner;Roberto Cipolla

Frequent Co-Authors

Sanja Fidler
Sanja Fidler University of Toronto
Renjie Liao
Renjie Liao University of British Columbia
Richard S. Zemel
Richard S. Zemel University of Toronto
Alexander G. Schwing
Alexander G. Schwing University of Illinois at Urbana-Champaign
Trevor Darrell
Trevor Darrell University of California, Berkeley
Pascal Fua
Pascal Fua École Polytechnique Fédérale de Lausanne
Mathieu Salzmann
Mathieu Salzmann École Polytechnique Fédérale de Lausanne
Andreas Geiger
Andreas Geiger University of Tübingen
Alan L. Yuille
Alan L. Yuille Johns Hopkins University
Dahua Lin
Dahua Lin Chinese University of Hong Kong

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