World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
5380
World Ranking
12574
National Ranking
5098

Anelia Angelova 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 Anelia Angelova 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: 128 publications — 18th percentile

18% of scientists in this discipline score the same or lower.

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

Anelia Angelova 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 Anelia Angelova 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Anelia Angelova is affiliated with Google in the United States and has a research portfolio concentrated in the field of Computer Science, with a substantial focus on Computer Vision and Pattern Recognition. Their work falls within several key subfields, including Artificial Intelligence, Aerospace Engineering, Geology, and Computational Mechanics.

The scientist's research topics include multimodal machine learning applications, domain adaptation and few-shot learning, human pose and action recognition, advanced image and video retrieval techniques, advanced neural network applications, robotics and sensor-based localization, as well as 3D surveying and cultural heritage.

Anelia Angelova's publication record includes over 124 works primarily situated in the intersections of Computer Science and its sub-disciplines. The frequent publication venues for their work consist of:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Lecture Notes in Computer Science
  • IEEE Robotics and Automation Letters
  • Applied AI Letters

Highlighted recent papers illustrate a focus on vision and language models, object detection, and depth learning in dynamic scenes. These include:

  • PaLI: A Jointly-Scaled Multilingual Language-Image Model (2022), published in arXiv (Cornell University)
  • Learning Open-World Object Proposals Without Learning to Classify (2022), IEEE Robotics and Automation Letters
  • Unsupervised Monocular Depth Learning in Dynamic Scenes (2020), arXiv (Cornell University)
  • PaLI-X: On Scaling up a Multilingual Vision and Language Model (2023), arXiv (Cornell University)
  • F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models (2022), arXiv (Cornell University)

Collaboration has been an important aspect of their research, with frequent co-authors including AJ Piergiovanni, Weicheng Kuo, Michael S. Ryoo, Dahun Kim, and Tsung-Yi Lin. These collaborations span diverse areas within computer vision and machine learning.

Best Publications

  • Real-time grasp detection using convolutional neural networks

    Joseph Redmon;Anelia Angelova

  • Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints

    Reza Mahjourian;Martin Wicke;Anelia Angelova

  • PaLI: A Jointly-Scaled Multilingual Language-Image Model

    Unknown

  • Depth Prediction without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

    Vincent Michael Casser;Soeren Pirk;Reza Mahjourian;Anelia Angelova

  • Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras

    Ariel Gordon;Hanhan Li;Rico Jonschkowski;Anelia Angelova

  • Real-Time Pedestrian Detection With Deep Network Cascades

    Anelia Angelova;Alex Krizhevsky;Vincent Vanhoucke;Abhijit S. Ogale

  • Computer Vision on Mars

    Larry Matthies;Mark Maimone;Andrew Johnson;Yang Cheng

  • Efficient Object Detection and Segmentation for Fine-Grained Recognition

    Anelia Angelova;Shenghuo Zhu

  • Learning and prediction of slip from visual information

    Anelia Angelova;Larry H. Matthies;Daniel M. Helmick;Pietro Perona

  • Pruning training sets for learning of object categories

    A. Angelova;Y. Abu-Mostafam;P. Perona

  • What Matters in Unsupervised Optical Flow

    Rico Jonschkowski;Austin Stone;Jonathan T. Barron;Ariel Gordon

  • Evolving Losses for Unsupervised Video Representation Learning

    AJ Piergiovanni;Anelia Angelova;Michael S. Ryoo

  • ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors

    Weicheng Kuo;Anelia Angelova;Jitendra Malik;Tsung-Yi Lin

  • Pedestrian detection with a Large-Field-Of-View deep network

    Anelia Angelova;Alex Krizhevsky;Vincent Vanhoucke

  • KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects

    Xingyu Liu;Rico Jonschkowski;Anelia Angelova;Kurt Konolige

  • Terrain Adaptive Navigation for planetary rovers

    Daniel Helmick;Anelia Angelova;Larry Matthies

  • Fast Terrain Classification Using Variable-Length Representation for Autonomous Navigation

    A. Angelova;L. Matthies;D. Helmick;P. Perona

  • Probabilistic Object Detection: Definition and Evaluation

    David Hall;Feras Dayoub;John Skinner;Haoyang Zhang

  • Learning Open-World Object Proposals without Learning to Classify

    Dahun Kim;Tsung-Yi Lin;Anelia Angelova;In So Kweon

  • Learning to predict slip for ground robots

    A. Angelova;L. Matthies;D. Helmick;G. Sibley

  • PaLI-X: On Scaling up a Multilingual Vision and Language Model

    Unknown

  • Towards learned traversability for robot navigation: From underfoot to the far field

    Andrew Howard;Michael J. Turmon;Larry H. Matthies;Benyang Tang

  • Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints

    Reza Mahjourian;Martin Wicke;Anelia Angelova

  • Improved generator objectives for GANs

    Ben Poole;Alexander A. Alemi;Jascha Sohl-Dickstein;Anelia Angelova

  • Slip prediction using visual information

    Anelia Angelova;Larry H. Matthies;Daniel M. Helmick;Pietro Perona

  • Image segmentation for large-scale subcategory flower recognition

    A. Angelova;Shenghuo Zhu;Yuanqing Lin

  • TokenLearner: Adaptive Space-Time Tokenization for Videos

    Michael Ryoo;AJ Piergiovanni;Anurag Arnab;Mostafa Dehghani

  • Learning and prediction of slip from visual information: Research Articles

    Anelia Angelova;Larry Matthies;Daniel Helmick;Pietro Perona

Frequent Co-Authors

Michael S. Ryoo
Michael S. Ryoo Stony Brook University
Larry Matthies
Larry Matthies Jet Propulsion Lab
Tsung-Yi Lin
Tsung-Yi Lin Nvidia (United States)
Pietro Perona
Pietro Perona California Institute of Technology
Alexander Toshev
Alexander Toshev Apple (United States)
Vincent Vanhoucke
Vincent Vanhoucke Google (United States)
Irfan Essa
Irfan Essa Georgia Institute of Technology
Kurt Konolige
Kurt Konolige Google (United States)
Angela Dai
Angela Dai Technical University of Munich
Andrew W. Howard
Andrew W. Howard California Institute of Technology

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