World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
7301
World Ranking
10119
National Ranking
629

Peter Tino 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 Peter Tino 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: 249 publications — 62nd percentile

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

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

Peter Tino 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 Peter Tino 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

Peter Tino is affiliated with the University of Birmingham in the United Kingdom. Their research is concentrated in the field of Computer Science, with a considerable focus on Artificial Intelligence. They have contributed to various subfields including Computer Vision and Pattern Recognition, Cognitive Neuroscience, Astronomy and Astrophysics, and Molecular Biology.

The main research themes in Peter Tino's body of work encompass Dementia and Cognitive Impairment Research, Neural Networks and Applications, Machine Learning in Healthcare, Neural Dynamics and Brain Function, Alzheimer's Disease Research and Treatments, Galaxies: Formation, Evolution, Phenomena, and Topological and Geometric Data Analysis.

Peter Tino has published extensively, with frequent appearances in the following venues:

  • arXiv (Cornell University)
  • Nature Communications
  • Astronomy and Astrophysics
  • SSRN Electronic Journal
  • Neurocomputing

Notable recent papers include:

  • Emerging opportunities and challenges for the future of reservoir computing, 2024, Nature Communications
  • Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies, 2020, European Journal of Epidemiology
  • A predictable conserved DNA base composition signature defines human core DNA replication origins, 2020, Nature Communications
  • Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease, 2020, NeuroImage Clinical
  • Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD Matrices, 2020, IEEE Transactions on Neural Networks and Learning Systems

Peter Tino has collaborated frequently with several co-authors, including Kerstin Bunte, Marco Canducci, R. F. Peletier, Robert Simon Fong, and Zoe Kourtzi.

In addition to articles, they have contributed to book publications with Springer Nature and Springer Science+Business Media. Titles include Population-Based Optimization on Riemannian Manifolds (2022) and Intelligent Data Engineering and Automated Learning - IDEAL 2021 (2021).

Best Publications

  • Learning long-term dependencies in NARX recurrent neural networks

    Tsungnan Lin;B.G. Horne;P. Tino;C.L. Giles

  • Minimum Complexity Echo State Network

    A Rodan;P Tino

  • A Survey on Neural Network Interpretability

    Yu Zhang;Peter Tino;Ales Leonardis;Ke Tang

  • Markovian architectural bias of recurrent neural networks

    P. Tino;M. Cernansky;L. Benuskova

  • Financial volatility trading using recurrent neural networks

    P. Tino;C. Schittenkopf;G. Dorffner

  • Probabilistic Classification Vector Machines

    Huanhuan Chen;P. Tino;Xin Yao

  • Learning in the Model Space for Cognitive Fault Diagnosis

    Huanhuan Chen;Peter Tino;Ali Rodan;Xin Yao

  • Model-based kernel for efficient time series analysis

    Huanhuan Chen;Fengzhen Tang;Peter Tino;Xin Yao

  • Hierarchical GTM: constructing localized nonlinear projection manifolds in a principled way

    P. Tino;I. Nabney

  • Predictive Ensemble Pruning by Expectation Propagation

    Huanhuan Chen;P. Tiho;Xin Yao

  • Scaling Up Estimation of Distribution Algorithms for Continuous Optimization

    Weishan Dong;Tianshi Chen;Peter Tino;Xin Yao

  • Finite state machines and recurrent neural networks—automata and dynamical systems approaches

    Peter Tiňo;Bill G. Horne;C. Lee Giles

  • Concept drift detection for online class imbalance learning

    Shuo Wang;Leandro L. Minku;Davide Ghezzi;Daniele Caltabiano

  • Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies

    Krishna Margadhamane Gokhale;Joht Singh Chandan;Konstantinos Toulis;Georgios Gkoutos

  • Indefinite proximity learning: A review

    Frank-Michael Schleif;Peter Tino

  • Predicting the Future of Discrete Sequences from Fractal Representations of the Past

    Peter Tino;Georg Dorffner

  • Learning predictive statistics from temporal sequences: Dynamics and strategies.

    Rui Wang;Rui Wang;Yuan Shen;Yuan Shen;Peter Tino;Andrew E Welchman

  • Spatial representation of symbolic sequences through iterative function systems

    P. Tino

  • Incorporating Privileged Information Through Metric Learning

    S. Fouad;P. Tino;S. Raychaudhury;P. Schneider

  • Recurrent neural networks with small weights implement definite memory machines

    Barbara Hammer;Peter Tiňo

  • Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space

    Huanhuan Chen;Peter Tiňo;Xin Yao

Frequent Co-Authors

Xin Yao
Xin Yao Lingnan University
Zoe Kourtzi
Zoe Kourtzi University of Cambridge
Barbara Hammer
Barbara Hammer Bielefeld University
Mirco Musolesi
Mirco Musolesi University College London
Wiebke Arlt
Wiebke Arlt University of Birmingham
Rami Bahsoon
Rami Bahsoon University of Birmingham
William J. Jagust
William J. Jagust University of California, Berkeley
Graham Kendall
Graham Kendall MILA University
Susan M. Landau
Susan M. Landau University of California, Berkeley
Pedro Antonio Gutiérrez
Pedro Antonio Gutiérrez University of Córdoba

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