World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
11527
World Ranking
13337
National Ranking
5335

George Tucker 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 George Tucker 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: 60 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

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

George Tucker 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 George Tucker 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: 31 D-Index — 6th percentile

6% of scientists in this discipline score the same or lower.

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

Overview

George Tucker is a researcher affiliated with Google in the United States. Their work predominantly focuses on the field of Computer Science, with a strong specialization in Artificial Intelligence.

Their research spans multiple subfields, including:

  • Artificial Intelligence
  • Management Science and Operations Research
  • Computer Vision and Pattern Recognition
  • Automotive Engineering
  • Industrial and Manufacturing Engineering

George Tucker's main topics of research encompass:

  • Reinforcement Learning in Robotics
  • Advanced Bandit Algorithms Research
  • Gaussian Processes and Bayesian Inference
  • Autonomous Vehicle Technology and Safety
  • Machine Learning and Algorithms
  • Machine Learning and Data Classification
  • Generative Adversarial Networks and Image Synthesis

Most of Tucker's scholarly output is published through the venue arXiv (Cornell University), with a total of 20 publications in this repository.

Recent papers by George Tucker include:

  • Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems (2020), published in arXiv (Cornell University)
  • Gemini: A Family of Highly Capable Multimodal Models (2023), published in arXiv (Cornell University)
  • Conservative Q-Learning for Offline Reinforcement Learning (2020), published in arXiv (Cornell University)
  • D4RL: Datasets for Deep Data-Driven Reinforcement Learning (2020), published in arXiv (Cornell University)
  • Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024), published in arXiv (Cornell University)

Frequent collaborators include:

  • Ofir Nachum
  • Sergey Levine
  • Aviral Kumar
  • Justin Fu
  • Cosmin Păduraru

Best Publications

  • Soft Actor-Critic Algorithms and Applications

    Tuomas Haarnoja;Aurick Zhou;Kristian Hartikainen;George Tucker

  • Efficient Bayesian mixed-model analysis increases association power in large cohorts

    Po-Ru Loh;George Tucker;Brendan K Bulik-Sullivan;Bjarni J Vilhjálmsson;Bjarni J Vilhjálmsson

  • Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

    Sergey Levine;Aviral Kumar;George Tucker;Justin Fu

  • Regularizing Neural Networks by Penalizing Confident Output Distributions

    Gabriel Pereyra;George Tucker;Jan Chorowski;Łukasz Kaiser

  • Conservative Q-Learning for Offline Reinforcement Learning

    Aviral Kumar;Aurick Zhou;George Tucker;Sergey Levine

  • Model-Based Reinforcement Learning for Atari

    Lukasz Kaiser;Mohammad Babaeizadeh;Piotr Milos;Blazej Osinski

  • Learning to Walk via Deep Reinforcement Learning

    Tuomas Haarnoja;Sehoon Ha;Aurick Zhou;Jie Tan

  • On Variational Bounds of Mutual Information

    Ben Poole;Sherjil Ozair;Aaron van den Oord;Alexander A. Alemi

  • D4RL: Datasets for Deep Data-Driven Reinforcement Learning

    Justin Fu;Aviral Kumar;Ofir Nachum;George Tucker

  • Model Based Reinforcement Learning for Atari

    Łukasz Kaiser;Mohammad Babaeizadeh;Piotr Miłos;Błażej Osiński

  • Behavior Regularized Offline Reinforcement Learning

    Yifan Wu;George Tucker;Ofir Nachum

  • Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

    Aviral Kumar;Justin Fu;Matthew Soh;George Tucker

  • REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models

    George Tucker;Andriy Mnih;Chris J. Maddison;Dieterich Lawson

  • Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

    Jacob Buckman;Danijar Hafner;George Tucker;Eugene Brevdo

  • Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

    Aviral Kumar;Justin Fu;George Tucker;Sergey Levine

  • Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

    Carlos Riquelme;George Tucker;Jasper Roland Snoek

  • Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting

    Ming Sun;Anirudh Raju;George Tucker;Sankaran Panchapagesan

  • Filtering Variational Objectives

    Chris J Maddison;Dieterich Lawson;George Tucker;Nicolas Heess

  • Methods and devices for ignoring similar audio being received by a system

    Alexander David Rosen;Michael James Rodehorst;George Jay Tucker;Aaron Lee Mathers Challenner

  • Meta-Learning without Memorization

    Mingzhang Yin;George Tucker;Mingyuan Zhou;Sergey Levine

  • Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse

    James Lucas;George Tucker;Roger B. Grosse;Mohammad Norouzi

  • Widespread Macromolecular Interaction Perturbations in Human Genetic Disorders

    Jian Peng;George Jay Tucker;Alexander T. Leighton;Bonnie Berger Leighton

Frequent Co-Authors

Sergey Levine
Sergey Levine University of California, Berkeley
Mohammad Norouzi
Mohammad Norouzi Google (United States)
Po-Ru Loh
Po-Ru Loh Harvard Medical School
Alkes L. Price
Alkes L. Price Harvard University
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Jascha Sohl-Dickstein
Jascha Sohl-Dickstein Google (United States)
Shixiang Gu
Shixiang Gu Google (United States)
Benjamin M. Neale
Benjamin M. Neale Harvard University

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