World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
122900
World Ranking
3507
National Ranking
1687

Alex Graves 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 Alex Graves 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 86 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

Alex Graves 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 Alex Graves sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 58 D-Index — 75th percentile

75% of scientists in this discipline score the same or lower.

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

Overview

Alex Graves is affiliated with Google in the United States and has contributed research across multiple scientific disciplines, focusing primarily on biochemistry, genetics, molecular biology, and computer science. Their work intersects medicine, particularly in areas related to stroke rehabilitation and recovery.

Graves's research spans several interconnected topics, including:

  • Bioinformatics and Genomic Networks
  • Stroke Rehabilitation and Recovery
  • Protein Structure and Dynamics
  • Machine Learning in Bioinformatics
  • Biomedical Text Mining and Ontologies
  • Gene expression and cancer classification
  • Acute Ischemic Stroke Management

Their frequent publication venues demonstrate a consistent interest in both computational and medical sciences, with papers appearing in:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Stroke
  • arXiv (Cornell University)
  • Nature Communications

Graves has collaborated extensively with several coauthors, including Timothy Atkinson, Thomas D. Barrett, Bora Guloglu, Liviu Copoiu, and Alexandre Laterre.

Recent papers authored or coauthored by Graves include:

  • Protein sequence modelling with Bayesian flow networks, 2025, Nature Communications
  • Bayesian Flow Networks, 2023, arXiv (Cornell University)
  • A Practical Sparse Approximation for Real Time Recurrent Learning, 2020, arXiv (Cornell University)
  • Protein Sequence Modelling with Bayesian Flow Networks, 2024, bioRxiv (Cold Spring Harbor Laboratory)
  • Abstract P848: A Better Way to NIHSS, 2021, Stroke

The combination of biochemistry and machine learning found in Graves's work highlights an interdisciplinary approach to understanding protein dynamics and computational methods for biological data analysis. Their investigation into stroke rehabilitation adds a clinical dimension, reflecting research that spans from molecular biology to medical applications.

Best Publications

  • Human-level control through deep reinforcement learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Andrei A. Rusu

  • Speech recognition with deep recurrent neural networks

    Alex Graves;Abdel-rahman Mohamed;Geoffrey Hinton

  • Playing Atari with Deep Reinforcement Learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Alex Graves

  • Asynchronous methods for deep reinforcement learning

    Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves

  • Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

    Alex Graves;Santiago Fernández;Faustino Gomez;Jürgen Schmidhuber

  • WaveNet: A Generative Model for Raw Audio

    Aäron van den Oord;Sander Dieleman;Heiga Zen;Karen Simonyan

  • 2005 Special Issue: Framewise phoneme classification with bidirectional LSTM and other neural network architectures

    Alex Graves;Jürgen Schmidhuber

  • Generating Sequences With Recurrent Neural Networks

    Alex Graves

  • Framewise phoneme classification with bidirectional LSTM and other neural network architectures

    Alex Graves;Jürgen Schmidhuber

  • Supervised Sequence Labelling with Recurrent Neural Networks

    Alexander Graves

  • Recurrent Models of Visual Attention

    Volodymyr Mnih;Nicolas Heess;Alex Graves;koray kavukcuoglu

  • Supervised Sequence Labelling

    Alex Graves

  • A Novel Connectionist System for Unconstrained Handwriting Recognition

    A. Graves;M. Liwicki;S. Fernandez;R. Bertolami

  • Towards End-To-End Speech Recognition with Recurrent Neural Networks

    Alex Graves;Navdeep Jaitly

  • Hybrid speech recognition with Deep Bidirectional LSTM

    Alex Graves;Navdeep Jaitly;Abdel-rahman Mohamed

  • Conditional image generation with PixelCNN decoders

    Aäron van den Oord;Nal Kalchbrenner;Oriol Vinyals;Lasse Espeholt

  • Long Short-Term Memory

    Alex Graves

  • Neural Turing Machines

    Alex Graves;Greg Wayne;Ivo Danihelka

  • DRAW: A Recurrent Neural Network For Image Generation

    Karol Gregor;Ivo Danihelka;Alex Graves;Danilo Rezende

  • Hybrid computing using a neural network with dynamic external memory

    Alex Graves;Greg Wayne;Malcolm Reynolds;Tim Harley

  • Sequence Transduction with Recurrent Neural Networks

    Alex Graves

  • Towards End-to-End Speech Recognitionwith Recurrent Neural Networks

    Alex Graves;Navdeep Jaitly

Frequent Co-Authors

Jürgen Schmidhuber
Jürgen Schmidhuber King Abdullah University of Science and Technology
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Aaron van den Oord
Aaron van den Oord Google (United States)
Volodymyr Mnih
Volodymyr Mnih DeepMind (United Kingdom)
Karen Simonyan
Karen Simonyan DeepMind (United Kingdom)
David Silver
David Silver DeepMind (United Kingdom)
Björn Schuller
Björn Schuller Imperial College London
Timothy P. Lillicrap
Timothy P. Lillicrap University College London
Florian Eyben
Florian Eyben Technical University of Munich

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