World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
5832
World Ranking
11617
National Ranking
729

Giovanni Montana 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 Giovanni Montana 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: 139 publications — 22nd percentile

22% of scientists in this discipline score the same or lower.

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

Giovanni Montana 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 Giovanni Montana 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Giovanni Montana is affiliated with the University of Warwick in the United Kingdom. Their research primarily spans the field of Computer Science, with a focus on several subfields including Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Control and Systems Engineering, and Computational Theory and Mathematics.

The scientist has explored diverse topics in their work, including Reinforcement Learning in Robotics, Robot Manipulation and Learning, Artificial Intelligence in Healthcare and Education, Topological and Geometric Data Analysis, Medical Imaging Techniques and Applications, Advanced Neuroimaging Techniques and Applications, and Global Cancer Incidence and Screening.

Frequent coauthors of Giovanni Montana include:

  • Yue Jin
  • Andrew J. Lawrence
  • Carmine M. Pariante
  • Mitul A. Mehta
  • Ana Rodríguez-Mateos

The scientist's recent papers cover a range of topics and were published in notable venues. These include:

  • "Evaluation of an AI Model to Assess Future Breast Cancer Risk," 2023, Radiology
  • "Mediators and moderators in the relationship between maternal childhood adversity and children's emotional and behavioural development: a systematic review and meta-analysis," 2022, Psychological Medicine
  • "A Persistent Homology-Based Topological Loss for CNN-Based Multiclass Segmentation of CMR," 2022, IEEE Transactions on Medical Imaging
  • "Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study," 2023, The Lancet Digital Health
  • "PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals," 2020, arXiv (Cornell University)

Publications by Giovanni Montana have appeared frequently in the following venues:

  • arXiv (Cornell University)
  • Machine Learning
  • Neuroscience Applied
  • Psychoneuroendocrinology
  • Radiology

Best Publications

  • Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker.

    James H. Cole;Rudra P. K. Poudel;Dimosthenis Tsagkrasoulis;Matthan W. A. Caan

  • Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks

    Adrien Payan;Giovanni Montana

  • Deep neural networks for anatomical brain segmentation

    Alexandre de Brebisson;Giovanni Montana

  • Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks

    Mauro Annarumma;Samuel J. Withey;Robert J. Bakewell;Emanuele Pesce

  • Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation

    Rudra P. K. Poudel;Pablo Lamata;Giovanni Montana

  • Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach.

    Maria Vounou;Thomas E. Nichols;Giovanni Montana

  • Estimating time-varying brain connectivity networks from functional MRI time series.

    Ricardo Pio Monti;Peter Hellyer;David J. Sharp;Robert Leech

  • Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

    Petros Pavlos Ypsilantis;Musib Siddique;Hyon Mok Sohn;Andrew Davies

  • Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease

    Maria Vounou;Eva Janousova;Robin Wolz;Jason L. Stein

  • False positives in neuroimaging genetics using voxel-based morphometry data.

    Matt Silver;Giovanni Montana;Thomas E. Nichols

  • Learning to detect chest radiographs containing pulmonary lesions using visual attention networks

    Emanuele Pesce;Samuel Joseph Withey;Petros-Pavlos Ypsilantis;Robert Bakewell

  • Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression☆

    Matt J Silver;Eva Janoušová;Xue Hua;Paul M Thompson

  • Community Detection in Multiplex Networks using Locally Adaptive Random Walks

    Zhana Kuncheva;Giovanni Montana

  • The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI

    Romy Lorenz;Ricardo Pio Monti;Inês R. Violante;Christoforos Anagnostopoulos

  • HapSim: a simulation tool for generating haplotype data with pre-specified allele frequencies and LD coefficients

    Giovanni Montana

  • Heritability maps of human face morphology through large-scale automated three-dimensional phenotyping

    Dimosthenis Tsagkrasoulis;Pirro Hysi;Tim Spector;Giovanni Montana;Giovanni Montana

  • Subspace clustering of high-dimensional data: a predictive approach

    Brian Mcwilliams;Giovanni Montana

  • Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication

    Emanuele Pesce;Giovanni Montana

  • Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks

    Savelie Cornegruta;Robert Bakewell;Samuel Withey;Giovanni Montana

  • Flexible least squares for temporal data mining and statistical arbitrage

    Giovanni Montana;Kostas Triantafyllopoulos;Theodoros Tsagaris

  • Statistical methods in genetics

    Giovanni Montana

Frequent Co-Authors

Robert Leech
Robert Leech King's College London
Daniel Rueckert
Daniel Rueckert Technical University of Munich
Serena J. Counsell
Serena J. Counsell King's College London
Adam Hampshire
Adam Hampshire Imperial College London
Andrew P. King
Andrew P. King King's College London
David J. Sharp
David J. Sharp Imperial College London
Paul Aljabar
Paul Aljabar King's College London
Michael Levin
Michael Levin Tufts University
Paul M. Matthews
Paul M. Matthews Imperial College London
Tim D. Spector
Tim D. Spector King's College London

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