World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
67
Citations
16640
World Ranking
2206
National Ranking
125

Edwin R. Hancock 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 Edwin R. Hancock 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: 899 publications — 99th percentile

99% of scientists in this discipline score the same or lower.

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

Edwin R. Hancock 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 Edwin R. Hancock 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: 67 D-Index — 85th percentile

85% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2022 - Fellow of the Asia-Pacific Artificial Inteliegence Association
  • 2021 - Fellow of the Royal Academy of Engineering (UK)
  • 2018 - IAPR Pierre Devijver Award, International Association for Pattern Recognition
  • 2016 - IEEE Fellow For contributions to pattern recognition and computer vision
  • 2016 - Distinguished Fellow of the British Machine Vision Association (BMVA)
  • 2008 - Fellow of the Institution of Engineering and Technology (IET), UK
  • 2006 - IAPR P. Zamperoni Award A Reimannian Weighted Filter for Edge-sensitive Image Smoothing
  • 2000 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to structural and statistical pattern recognition, and to computer vision

Overview

Edwin R. Hancock is affiliated with the University of York in the United Kingdom. Their research primarily focuses on the field of Computer Science, with extensive work spanning artificial intelligence, computer vision, and pattern recognition among other subfields.

The main fields of study covered by Hancock include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Statistical and Nonlinear Physics
  • Materials Chemistry
  • Signal Processing

They have contributed to a variety of topics, notably in:

  • Advanced Graph Neural Networks
  • Complex Network Analysis Techniques
  • Graph Theory and Algorithms
  • Quantum Computing Algorithms and Architecture
  • Advanced Vision and Imaging
  • Machine Learning in Materials Science
  • Mental Health Research Topics

Edwin R. Hancock has published in frequent venues such as:

  • arXiv (Cornell University)
  • Pattern Recognition
  • IEEE Transactions on Knowledge and Data Engineering
  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Recent notable papers include:

  • "Uncertainty estimation for stereo matching based on evidential deep learning" (2021), published in Pattern Recognition
  • "Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification" (2020), published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification" (2024), published in IEEE Transactions on Knowledge and Data Engineering
  • "Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective" (2022), published in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Fused lasso for feature selection using structural information" (2021), published in Pattern Recognition

Frequent co-authors in their research work include:

  • Lixin Cui
  • Lu Bai
  • Zhihong Zhang
  • Yue Wang
  • Xiao Bai

Hancock also has book publications with Springer Science+Business Media, including titles such as Image Analysis and Processing - ICIAP 2023 (2023).

The scientist has received several awards and honors, including:

  • Fellow of the Asia-Pacific Artificial Intelligence Association (2022)
  • Fellow of the Royal Academy of Engineering (UK) (2021)
  • IAPR Pierre Devijver Award, International Association for Pattern Recognition (2018)
  • Distinguished Fellow of the British Machine Vision Association (BMVA) (2016)
  • IEEE Fellow for contributions to pattern recognition and computer vision (2016)
  • Fellow of the Institution of Engineering and Technology (IET), UK (2008)
  • IAPR P. Zamperoni Award for work on edge-sensitive image smoothing (2006)
  • Fellow of the International Association for Pattern Recognition (IAPR) for contributions to structural and statistical pattern recognition and computer vision (2000)

Best Publications

  • Structural, syntactic, and statistical pattern recognition

    Edwin R. Hancock;Richard C. Wilson;Terry Windeatt;Ilkay Ulusoy

  • Structural graph matching using the EM algorithm and singular value decomposition

    Bin Luo;E.R. Hancock

  • Structural matching by discrete relaxation

    R.C. Wilson;E.R. Hancock

  • Spectral embedding of graphs

    Bin Luo;Bin Luo;Richard C. Wilson;Edwin R. Hancock

  • Graph matching with a dual-step EM algorithm

    A.D.J. Cross;E.R. Hancock

  • Clustering and Embedding Using Commute Times

    Huaijun Qiu;E.R. Hancock

  • Recovery of surface orientation from diffuse polarization

    G.A. Atkinson;E.R. Hancock

  • Pattern vectors from algebraic graph theory

    R.C. Wilson;E.R. Hancock;Bin Luo

  • New constraints on data-closeness and needle map consistency for shape-from-shading

    P.L. Worthington;E.R. Hancock

  • Graph edit distance from spectral seriation

    A. Robles-Kelly;E.R. Hancock

  • Spectral correspondence for point pattern matching

    Marco Carcassoni;Edwin R. Hancock

  • Bayesian graph edit distance

    R. Myers;R.C. Wison;E.R. Hancock

  • Graph spectral image smoothing using the heat kernel

    Fan Zhang;Edwin R. Hancock

  • Inexact graph matching using genetic search

    Andrew D.J. Cross;Richard C. Wilson;Edwin R. Hancock

  • Edge-labeling using dictionary-based relaxation

    E.R. Hancock;J. Kittler

  • COMBINING EVIDENCE IN PROBABILISTIC RELAXATION

    Josef Kittler;Edwin R. Hancock

  • Graph characteristics from the heat kernel trace

    Bai Xiao;Edwin R. Hancock;Richard C. Wilson

  • Discrete relaxation

    E. R. Hancock;J. Kittler

  • Recovering Facial Shape Using a Statistical Model of Surface Normal Direction

    W.A.P. Smith;E.R. Hancock

  • Graph matching and clustering using spectral partitions

    Huaijun Qiu;Edwin R. Hancock

  • Bayesian graph edit distance

    R. Myers;R.C. Wilson;E.R. Hancock

  • Structural, syntactic, and statistical pattern recognition : joint IAPR international workshop, SSPR & SPR 2010 : Cesme, Izmir, Turkey, August 18 - 20, 2010 : proceedings

    Edwin R Hancock;Richard C. Wilson;Terry Windeatt;Ilkay Ulusoy

Frequent Co-Authors

Richard Wilson
Richard Wilson Harvard University
William A. P. Smith
William A. P. Smith University of York
Andrea Torsello
Andrea Torsello Ca Foscari University of Venice
Bin Luo
Bin Luo Anhui University
Marcello Pelillo
Marcello Pelillo Ca Foscari University of Venice
Josef Kittler
Josef Kittler University of Surrey
Simone Severini
Simone Severini University College London
Jun Zhou
Jun Zhou Griffith University
Luciano da Fontoura Costa
Luciano da Fontoura Costa Universidade de São Paulo

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online degrees in computer science opens up a range of flexible career options tailored to different needs and goals. For those seeking a fast track, the quickest degree to get online can lead to well-paying jobs without spending several years in school. These programs are ideal for career changers or those wanting to boost their qualifications quickly.

If your interests lean toward innovative fields, consider the best online ai degree programs. These specialized degrees focus on in-demand AI and machine learning skills that are highly sought after by employers worldwide.

Choosing the right area of study is crucial. To maximize employability and future prospects, explore the best college degrees that align with industry needs and personal interests.

For advancing your education without major hurdles, the easiest master degree to get can provide a streamlined path to higher qualifications, leadership roles, or a shift into new tech domains.

Best Scientists Citing Edwin R. Hancock

Trending Scientists

Recently Published Articles