World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
11547
World Ranking
5560
National Ranking
169

Eduardo Nebot 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 Eduardo Nebot 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: 220 publications — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Eduardo Nebot 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 Eduardo Nebot 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: 50 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Overview

Eduardo Nebot is affiliated with the University of Sydney in Australia. Their research spans multiple areas within engineering and computer science, focusing on subfields such as computer vision and pattern recognition, automotive engineering, and artificial intelligence. Other subfields include aerospace engineering and safety, risk, reliability, and quality.

The primary topics of their work cover autonomous vehicle technology and safety, advanced neural network applications, and video surveillance and tracking methods. Their research also explores robotics and sensor-based localization, domain adaptation and few-shot learning, remote sensing and LiDAR applications, as well as safety systems engineering in autonomy.

Eduardo Nebot has collaborated frequently with several co-authors, including Stewart Worrall, Mao Shan, Julie Stephany Berrío, Dhanoop Karunakaran, and Karan Narula.

Publications by Eduardo Nebot have appeared in various venues, including:

  • arXiv (Cornell University)
  • 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
  • Microscopy and Microanalysis
  • IEEE Transactions on Intelligent Transportation Systems
  • Sensors

Recent papers include:

  • Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations, 2020, Sensors
  • A Comparison of a Direct Electron Detector and a High-Speed Video Camera for a Scanning Precession Electron Diffraction Phase and Orientation Mapping, 2020, Microscopy and Microanalysis
  • Developing and Testing Robust Autonomy: The University of Sydney Campus Data Set, 2020, IEEE Intelligent Transportation Systems Magazine
  • See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation, 2022, IEEE Robotics and Automation Letters
  • A Novel Probabilistic V2X Data Fusion Framework for Cooperative Perception, 2022, 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

Best Publications

  • Optimization of the simultaneous localization and map-building algorithm for real-time implementation

    J.E. Guivant;E.M. Nebot

  • Consistency of the EKF-SLAM Algorithm

    T. Bailey;J. Nieto;J. Guivant;M. Stevens

  • A high integrity IMU/GPS navigation loop for autonomous land vehicle applications

    S. Sukkarieh;E.M. Nebot;H.F. Durrant-Whyte

  • The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications

    G. Dissanayake;S. Sukkarieh;E. Nebot;H. Durrant-Whyte

  • Localization and map building using laser range sensors in outdoor applications

    José E. Guivant;Eduardo Mario Nebot;Stephan Baiker

  • An evidential approach to map-building for autonomous vehicles

    D. Pagac;E.M. Nebot;H. Durrant-Whyte

  • Consistency of the FastSLAM algorithm

    T. Bailey;J. Nieto;E. Nebot

  • Approximate Inference in State-Space Models With Heavy-Tailed Noise

    G. Agamennoni;J. I. Nieto;E. M. Nebot

  • An experiment in autonomous navigation of an underground mining vehicle

    S. Scheding;G. Dissanayake;E.M. Nebot;H. Durrant-Whyte

  • Initial calibration and alignment of low‐cost inertial navigation units for land vehicle applications

    Eduardo Mario Nebot;Hugh F. Durrant-Whyte

  • Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks

    Alex Zyner;Stewart Worrall;Eduardo Nebot

  • A Recurrent Neural Network Solution for Predicting Driver Intention at Unsignalized Intersections

    Alex Zyner;Stewart Worrall;Eduardo M. Nebot

  • Data association for mobile robot navigation: a graph theoretic approach

    T. Bailey;E.M. Nebot;J.K. Rosenblatt;H.F. Durrant-Whyte

  • Real time data association for FastSLAM

    J. Nieto;J. Guivant;E. Nebot;S. Thrun

  • Autonomous Navigation and Map building Using Laser Range Sensors in Outdoor Applications

    Jose Guivant;Eduardo Nebot;Stephan Baiker

  • Simultaneous localization and map building using natural features and absolute information

    José E. Guivant;Favio R. Masson;Eduardo Mario Nebot

  • Development of a nonlinear psi-angle model for large misalignment errors and its application in INS alignment and calibration

    Xiaoying Kong;E.M. Nebot;H. Durrant-Whyte

  • Artificial neural network implementation of chemistry with pdf simulation of H2/CO2 flames

    Farid Christo;AR Masri;EM Nebot

  • An outlier-robust Kalman filter

    Gabriel Agamennoni;Juan I. Nieto;Eduardo M. Nebot

  • Recursive scan-matching SLAM

    Juan Nieto;Tim Bailey;Eduardo Nebot

  • Localization of Autonomous Guided Vehicles

    Hugh Durrant-Whyte;David Rye;Eduardo Nebot

  • FastSLAM: An Efficient Solution to the Simultaneous Localization And Mapping Problem with Unknown Data

    Sebastian Thrun;Michael Montemerlo;Daphne Koller;Ben Wegbreit

Frequent Co-Authors

Juan Nieto
Juan Nieto Microsoft (United States)
Hugh Durrant-Whyte
Hugh Durrant-Whyte University of Sydney
Salah Sukkarieh
Salah Sukkarieh University of Sydney
Gamini Dissanayake
Gamini Dissanayake University of Technology Sydney
Miguel Angel Sotelo
Miguel Angel Sotelo University of Alcalá
Sebastian Thrun
Sebastian Thrun Stanford University
Martin Tomitsch
Martin Tomitsch University of Technology Sydney
Peter Corke
Peter Corke Queensland University of Technology
Andreas G. Andreou
Andreas G. Andreou Johns Hopkins University
Daphne Koller
Daphne Koller insitro Inc.

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