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William D. Penny
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Computer Science
UK
2025

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Computer Science 70 1825 1766 103 102 188 32946

William D. Penny publications per year

The chart shows the history of publications by William D. Penny between 1990 and 2021, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. William D. Penny published across 32 years, from 1990 to 2021, averaging 6.3 papers a year. Output peaked at 18 publications in 2007. 7 of the 200 publications appeared in the last two years.

No. of publications
5 10 15
Bar chart. Horizontal axis: year, 1990 to 2021. Vertical axis: number of publications, 0 to 18. Peak 18 publications in 2007. 1990: 1 publication 1991: 1 publication 1992: 1 publication 1993: 1 publication 1994: 0 publications 1995: 1 publication 1996: 3 publications 1997: 4 publications 1998: 3 publications 1999: 7 publications 2000: 9 publications 2001: 6 publications 2002: 10 publications 2003: 9 publications 2004: 9 publications 2005: 7 publications 2006: 5 publications 2007: 18 publications 2008: 8 publications 2009: 7 publications 2010: 6 publications 2011: 6 publications 2012: 8 publications 2013: 14 publications 2014: 12 publications 2015: 7 publications 2016: 7 publications 2017: 7 publications 2018: 8 publications 2019: 8 publications 2020: 1 publication 2021: 6 publications
1990 2021

200 publications in total across all disciplines

View publications per year as a table
William D. Penny: publications per year, 1990 to 2021
Year Publications
1990 1
1991 1
1992 1
1993 1
1994 0
1995 1
1996 3
1997 4
1998 3
1999 7
2000 9
2001 6
2002 10
2003 9
2004 9
2005 7
2006 5
2007 18
2008 8
2009 7
2010 6
2011 6
2012 8
2013 14
2014 12
2015 7
2016 7
2017 7
2018 8
2019 8
2020 1
2021 6
Total 200
Download as CSV

William D. Penny 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 William D. Penny sits on this spectrum.

No. of scientists
200 400 600
Bar chart with 97 bars. Horizontal axis: publications, 32–41 to 991+. Vertical axis: number of scientists, 0 to 609. Most scientists, 609, have 142–151 publications. The last bar groups every scientist with 991 publications or more. The highlighted bar, 182–191 publications, is where this scientist sits. 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
32–41 publications 991+

This scientist: 188 publications — 42nd percentile

42% of scientists in this discipline score the same or lower.

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

View publications distribution as a table
Number of Computer Science scientists by publication count, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
Publications Scientists This scientist
32–41 7
42–51 22
52–61 82
62–71 134
72–81 249
82–91 324
92–101 421
102–111 420
112–121 497
122–131 544
132–141 555
142–151 609
152–161 559
162–171 534
172–181 556
182–191 583 188
192–201 519
202–211 508
212–221 490
222–231 437
232–241 423
242–251 408
252–261 377
262–271 301
272–281 335
282–291 320
292–301 293
302–311 250
312–321 238
322–331 206
332–341 209
342–351 208
352–361 162
362–371 176
372–381 127
382–391 158
392–401 128
402–411 104
412–421 94
422–431 99
432–441 83
442–451 108
452–461 73
462–471 77
472–481 69
482–491 84
492–501 62
502–511 54
512–521 57
522–531 51
532–541 51
542–551 32
552–561 38
562–571 28
572–581 43
582–591 33
592–601 41
602–611 32
612–621 28
622–631 25
632–641 27
642–651 17
652–661 20
662–671 17
672–681 15
682–691 14
692–701 21
702–711 13
712–721 12
722–731 19
732–741 14
742–751 12
752–761 10
762–771 10
772–781 11
782–791 10
792–801 11
802–811 8
812–821 8
822–831 7
832–841 11
842–851 10
852–861 5
862–871 9
872–881 4
882–891 6
892–901 3
902–911 6
912–921 3
922–931 2
932–941 2
942–951 2
952–961 3
962–971 3
972–981 3
982–990 5
991+ 100
Download as CSV

William D. Penny 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 William D. Penny sits on this spectrum.

No. of scientists
200 400 600 800
Bar chart with 52 bars. Horizontal axis: D-Index, 30–31 to 131+. Vertical axis: number of scientists, 0 to 990. Most scientists, 990, have 36–37 D-Index. The last bar groups every scientist with 131 D-Index or more. The highlighted bar, 70–71 D-Index, is where this scientist sits. 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
30–31 D-Index 131+

This scientist: 70 D-Index — 87th percentile

87% of scientists in this discipline score the same or lower.

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

View D-Index distribution as a table
Number of Computer Science scientists by D-index, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
D-Index Scientists This scientist
30–31 879
32–33 983
34–35 918
36–37 990
38–39 968
40–41 907
42–43 821
44–45 763
46–47 689
48–49 543
50–51 543
52–53 518
54–55 500
56–57 458
58–59 400
60–61 337
62–63 308
64–65 292
66–67 249
68–69 213
70–71 192 70
72–73 189
74–75 165
76–77 139
78–79 119
80–81 121
82–83 113
84–85 88
86–87 87
88–89 75
90–91 69
92–93 57
94–95 46
96–97 38
98–99 34
100–101 36
102–103 27
104–105 37
106–107 18
108–109 31
110–111 19
112–113 16
114–115 12
116–117 20
118–119 15
120–121 5
122–123 20
124–125 8
126–127 5
128–129 7
130 3
131+ 98
Download as CSV

Research.com Recognitions

  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

William D. Penny mainly investigates Artificial intelligence, Bayes' theorem, Bayesian probability, Bayesian inference and Machine learning. His Artificial intelligence study integrates concerns from other disciplines, such as Causal model and Pattern recognition. His Causal model research is multidisciplinary, relying on both Cognition, Mathematical model and Dynamic causal modelling.

William D. Penny regularly ties together related areas like Linear model in his Bayes' theorem studies. His studies examine the connections between Bayesian inference and genetics, as well as such issues in Hyperparameter, with regards to Restricted maximum likelihood, Laplace's method, Mathematical optimization, Covariance and Gibbs sampling. His biological study spans a wide range of topics, including Information theory, Cognitive psychology, Functional magnetic resonance imaging and Functional integration.

His most cited work include:

  • Dynamic causal modelling. (3336 citations)
  • Statistical Parametric Mapping: The Analysis of Functional Brain Images (2078 citations)
  • Bayesian model selection for group studies. (1053 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Artificial intelligence, Bayesian inference, Machine learning, Bayesian probability and Pattern recognition. His study in Algorithm extends to Artificial intelligence with its themes. His Machine learning research also works with subjects such as

  • Hidden Markov model that connect with fields like Bounded rationality,
  • Dynamic causal modelling which connect with Functional neuroimaging.

He interconnects Generalized linear model, Data mining and Linear model in the investigation of issues within Bayesian probability. His research in Pattern recognition focuses on subjects like Electroencephalography, which are connected to Neuroimaging and Elementary cognitive task. The concepts of his Bayes' theorem study are interwoven with issues in Overfitting and Causal model.

He most often published in these fields:

  • Artificial intelligence (56.19%)
  • Bayesian inference (29.38%)
  • Machine learning (26.29%)

What were the highlights of his more recent work (between 2014-2021)?

  • Artificial intelligence (56.19%)
  • Neuroscience (18.56%)
  • Bayesian inference (29.38%)

In recent papers he was focusing on the following fields of study:

William D. Penny mainly focuses on Artificial intelligence, Neuroscience, Bayesian inference, Inference and Set. The Artificial intelligence study combines topics in areas such as Machine learning, Empirical research and Pattern recognition. His work in the fields of Artificial neural network and Transfer of learning overlaps with other areas such as Field.

His study on Neuroimaging, Electroencephalography, Motor cortex and Prefrontal cortex is often connected to Variable as part of broader study in Neuroscience. His work is dedicated to discovering how Bayesian inference, Bayes' theorem are connected with Algorithm, Dissociation and Impulsivity and other disciplines. William D. Penny has researched Inference in several fields, including Stimulus and Multivariate analysis.

Between 2014 and 2021, his most popular works were:

  • Behavioral modeling of human choices reveals dissociable effects of physical effort and temporal delay on reward devaluation. (62 citations)
  • The Neural Representation of Prospective Choice during Spatial Planning and Decisions. (49 citations)
  • Causal evidence that intrinsic beta-frequency is relevant for enhanced signal propagation in the motor system as shown through rhythmic TMS. (49 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Artificial intelligence
  • Machine learning

Neuroscience, Bayes' theorem, Motor cortex, Brain mapping and Markov chain Monte Carlo are his primary areas of study. William D. Penny combines subjects such as Pattern recognition and Bayesian inference with his study of Bayes' theorem. His studies in Pattern recognition integrate themes in fields like Bayes estimator and Machine learning.

His research integrates issues of Beta Rhythm, Motor system, Dynamic causal modelling and Pyramidal tracts in his study of Motor cortex. His Brain mapping research integrates issues from Frontal lobe, Functional magnetic resonance imaging and Prefrontal cortex. Many of his research projects under Artificial intelligence are closely connected to Detector and Group-level effects with Detector and Group-level effects, tying the diverse disciplines of science together.

Best Publications

  • Dynamic causal modelling.

    Karl J. Friston;Lee M. Harrison;William D. Penny

  • Statistical Parametric Mapping: The Analysis of Functional Brain Images

    W Penny;K Friston;J Ashburner;S Kiebel

  • Bayesian model selection for group studies.

    Klaas Enno Stephan;Will D. Penny;Jean Daunizeau;Rosalyn J. Moran

  • Comparing dynamic causal models

    William D. Penny;Klaas E. Stephan;Andrea Mechelli;Karl J. Friston

  • Variational free energy and the Laplace approximation

    Karl J. Friston;Jérémie Mattout;Nelson J. Trujillo-Barreto;John Ashburner

  • Ten simple rules for dynamic causal modeling.

    K.E. Stephan;K.E. Stephan;W.D. Penny;R.J. Moran;H.E.M. den Ouden

  • Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution.

    Darren R. Gitelman;William D. Penny;John Ashburner;Karl J. Friston

  • Comparing families of dynamic causal models.

    Will D. Penny;Klaas E. Stephan;Klaas E. Stephan;Jean Daunizeau;Maria J. Rosa

  • Classical and Bayesian inference in neuroimaging: theory.

    Karl J. Friston;William D. Penny;Christophe Phillips;Stefan J. Kiebel

  • EEG and MEG data analysis in SPM8.

    Vladimir Litvak;Jérémie Mattout;Stefan J. Kiebel;Christophe Phillips

  • Multivariate autoregressive modeling of fMRI time series

    Lee M. Harrison;William D. Penny;Karl J. Friston

  • Modelling functional integration:a comparison of structural equation and dynamic causal models

    W D Penny;K E Stephan;A Mechelli;K J Friston

  • Bayesian approaches to Gaussian mixture modeling

    S.J. Roberts;D. Husmeier;I. Rezek;W. Penny

  • Comparing Dynamic Causal Models using AIC, BIC and Free Energy

    William D. Penny

  • Post hoc Bayesian model selection

    Karl J. Friston;Will D. Penny

  • EEG-based communication: a pattern recognition approach

    W.D. Penny;S.J. Roberts;E.A. Curran;M.J. Stokes

  • Bayesian fMRI time series analysis with spatial priors.

    William D. Penny;Nelson J. Trujillo-Barreto;Karl J. Friston

  • Information theory, novelty and hippocampal responses: unpredicted or unpredictable?

    Bryan A. Strange;Andrew Duggins;William Penny;Raymond J. Dolan

  • Testing for nested oscillation

    W.D. Penny;E. Duzel;K.J. Miller;J.G. Ojemann

  • Posterior probability maps and SPMs.

    Karl J. Friston;William D. Penny

  • Dynamic causal models of neural system dynamics: current state and future extensions

    Klaas E Stephan;Lee M Harrison;Lee M Harrison;Stefan J Kiebel;Stefan J Kiebel;Olivier David

Frequent Co-Authors

Karl J. Friston
Karl J. Friston University College London
Stephen J. Roberts
Stephen J. Roberts University of Oxford
Klaas E. Stephan
Klaas E. Stephan University of Zurich
Emrah Düzel
Emrah Düzel German Center for Neurodegenerative Diseases
Guillaume Flandin
Guillaume Flandin University College London
Gareth R. Barnes
Gareth R. Barnes University College London
Alexander P. Leff
Alexander P. Leff University College London
John Ashburner
John Ashburner University College London
Jean Daunizeau
Jean Daunizeau Grenoble Alpes University
Cathy J. Price
Cathy J. Price University College London

External Links

Personal website of William D. Penny Google Scholar page

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Best Scientists Citing William D. Penny

Karl J. Friston

Karl J. Friston

University College London

Publications: 442

Klaas E. Stephan

Klaas E. Stephan

University of Zurich

Publications: 253

Raymond J. Dolan

Raymond J. Dolan

University College London

Publications: 114

Jean Daunizeau

Jean Daunizeau

Grenoble Alpes University

Publications: 99

Juan Manuel Górriz

Juan Manuel Górriz

University of Granada

Publications: 91

Javier Ramírez

Javier Ramírez

University of Granada

Publications: 87

Vladimir Litvak

Vladimir Litvak

University College London

Publications: 83

Rosalyn J. Moran

Rosalyn J. Moran

King's College London

Publications: 82

Michael Breakspear

Michael Breakspear

University of Newcastle Australia

Publications: 78

Adeel Razi

Adeel Razi

Monash University

Publications: 77

Gareth R. Barnes

Gareth R. Barnes

University College London

Publications: 76

Gustavo Deco

Gustavo Deco

Pompeu Fabra University

Publications: 76

James B. Rowe

James B. Rowe

University of Cambridge

Publications: 75

Simon B. Eickhoff

Simon B. Eickhoff

Heinrich Heine University Düsseldorf

Publications: 74

Marta I. Garrido

Marta I. Garrido

University of Melbourne

Publications: 73

Mark W. Woolrich

Mark W. Woolrich

University of Oxford

Publications: 70

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