World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
6278
World Ranking
9781
National Ranking
611

Ingmar Posner publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Ingmar Posner sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 133 publications — 20th percentile

20% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Ingmar Posner D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Ingmar Posner sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Ingmar Posner is affiliated with the University of Oxford in the United Kingdom. Their research primarily spans the fields of Computer Science and Engineering, with a significant focus on subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Biomedical Engineering, and Aerospace Engineering.

Posner's work covers several main topics, including:

  • Robot Manipulation and Learning
  • Human Pose and Action Recognition
  • Robotics and Sensor-Based Localization
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Robotic Locomotion and Control
  • Reinforcement Learning in Robotics

Frequent collaborators include Ioannis Havoutis, Ōiwi Parker Jones, Martin Engelcke, Alex Mitchell, and Jun Yamada. These recurring partnerships reflect a sustained engagement with research communities involved in robotics and machine learning.

Posner has published extensively, with notable papers including:

  • "From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence," 2021, arXiv (Cornell University)
  • "Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement," 2022, 2022 International Conference on Robotics and Automation (ICRA)
  • "Fast-MbyM: Leveraging Translational Invariance of the Fourier Transform for Efficient and Accurate Radar Odometry," 2022, 2022 International Conference on Robotics and Automation (ICRA)
  • "RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces," 2020, arXiv (Cornell University)
  • "GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement," 2021, arXiv (Cornell University)

The most frequent publication venues for their work are arXiv (Cornell University), IEEE Robotics and Automation Letters, the 2022 International Conference on Robotics and Automation (ICRA), IEEE Access, and IEEE Transactions on Robotics.

Best Publications

  • Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks

    Martin Engelcke;Dushyant Rao;Dominic Zeng Wang;Chi Hay Tong

  • The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset

    Dan Barnes;Matthew Gadd;Paul Murcutt;Paul Newman

  • Maximum Entropy Deep Inverse Reinforcement Learning

    Markus Wulfmeier;Peter Ondruska;Ingmar Posner

  • Voting for Voting in Online Point Cloud Object Detection

    Dominic Zeng Wang;Ingmar Posner

  • Deep tracking: seeing beyond seeing using recurrent neural networks

    Peter Ondrúška;Ingmar Posner

  • Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers

    Paul Newman;Gabe Sibley;Mike Smith;Mark Cummins

  • Large-scale cost function learning for path planning using deep inverse reinforcement learning

    Markus Wulfmeier;Dushyant Rao;Dominic Zeng Wang;Peter Ondruska

  • Toward automated driving in cities using close-to-market sensors: An overview of the V-Charge Project

    Paul Furgale;Ulrich Schwesinger;Martin Rufli;Wojciech Derendarz

  • Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects

    Adam R. Kosiorek;Hyunjik Kim;Yee Whye Teh;Ingmar Posner

  • What could move? Finding cars, pedestrians and bicyclists in 3D laser data

    Dominic Zeng Wang;Ingmar Posner;Paul Newman

  • Watch this: Scalable cost-function learning for path planning in urban environments

    Markus Wulfmeier;Dominic Zeng Wang;Ingmar Posner

  • Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects.

    Adam R. Kosiorek;Hyunjik Kim;Ingmar Posner;Yee Whye Teh

  • Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy

    Dan Barnes;Will Maddern;Ingmar Posner

  • Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar

    Dan Barnes;Ingmar Posner

  • Deep tracking in the wild: End-to-end tracking using recurrent neural networks:

    Julie Dequaire;Peter Ondruska;Dushyant Rao;Dominic Zeng Wang

  • Incremental Adversarial Domain Adaptation for Continually Changing Environments

    Markus Wulfmeier;Alex Bewley;Ingmar Posner

  • Model-free detection and tracking of dynamic objects with 2D lidar

    Dominic Zeng Wang;Ingmar Posner;Paul Newman

  • On the Limitations of Representing Functions on Sets

    Edward Wagstaff;Fabian B. Fuchs;Martin Engelcke;Ingmar Posner

  • A generative framework for fast urban labeling using spatial and temporal context

    Ingmar Posner;Mark Cummins;Paul Newman

  • Automated valet parking and charging for e-mobility

    Ulrich Schwesinger;Mathias Burki;Julian Timpner;Stephan Rottmann

  • GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations

    Martin Engelcke;Adam R. Kosiorek;Oiwi Parker Jones;Ingmar Posner

  • ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking

    Oliver Groth;Fabian B. Fuchs;Ingmar Posner;Andrea Vedaldi

Frequent Co-Authors

Paul Newman
Paul Newman University of Oxford
Andrea Vedaldi
Andrea Vedaldi University of Oxford
Marta Kwiatkowska
Marta Kwiatkowska University of Oxford
Raia Hadsell
Raia Hadsell DeepMind (United Kingdom)
Max Welling
Max Welling University of Amsterdam
Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Lars Wolf
Lars Wolf Technische Universität Braunschweig
Peter Corke
Peter Corke Queensland University of Technology

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