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2025

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Rising Stars

D-Index
34
Citations
15266
World Ranking
862
National Ranking
140

Computer Science

D-Index
34
Citations
14129
World Ranking
11873
National Ranking
4841

Shixiang Gu 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 Shixiang Gu 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: 57 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.

Shixiang Gu 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 Shixiang Gu 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Shixiang Gu is a researcher affiliated with Google in the United States. Their work primarily spans the fields of Computer Science and Engineering, with a particular focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, and Cognitive Neuroscience.

The main topics addressed in their research include Reinforcement Learning in Robotics, Robot Manipulation and Learning, Topic Modeling, Domain Adaptation and Few-Shot Learning, Multimodal Machine Learning Applications, and Modular Robots and Swarm Intelligence. These topics reflect a strong emphasis on the intersection of machine learning techniques and robotic systems.

Shixiang Gu has published extensively, contributing to 34 papers at arXiv (Cornell University), alongside works in Transactions of the Japanese Society for Artificial Intelligence, Advanced Robotics, IEEE Robotics and Automation Letters, and Journal of the Robotics Society of Japan.

Recent representative papers include:

  • Scaling Instruction-Finetuned Language Models, 2022, arXiv (Cornell University)
  • Large Language Models are Zero-Shot Reasoners, 2022, arXiv (Cornell University)
  • A Minimalist Approach to Offline Reinforcement Learning, 2021, arXiv (Cornell University)
  • Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization, 2020, arXiv (Cornell University)
  • Aligning Text-to-Image Models using Human Feedback, 2023, arXiv (Cornell University)

Their frequent co-authors include Yutaka Matsuo, Tatsuya Matsushima, Hiroki Furuta, and Yusuke Iwasawa, reflecting collaborative efforts in advancing research in AI and robotics.

Best Publications

  • Categorical Reparameterization with Gumbel-Softmax

    Eric Jang;Shixiang Gu;Ben Poole

  • Scaling Instruction-Finetuned Language Models

    Unknown

  • Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates

    Shixiang Gu;Ethan Holly;Timothy Lillicrap;Sergey Levine

  • Continuous deep Q-learning with model-based acceleration

    Shixiang Gu;Timothy Lillicrap;Ilya Sutskever;Sergey Levine

  • Towards Deep Neural Network Architectures Robust to Adversarial Examples

    Shixiang Gu;Luca Rigazio

  • Data-Efficient Hierarchical Reinforcement Learning

    Ofir Nachum;Shixiang Gu;Honglak Lee;Sergey Levine

  • Large Language Models Can Self-Improve

    Unknown

  • Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog.

    Natasha Jaques;Asma Ghandeharioun;Judy Hanwen Shen;Craig Ferguson

  • Q-PrOP: Sample-efficient policy gradient with an off-policy critic

    Shixiang Gu;Timothy Lillicrap;Zoubin Ghahramani;Richard Eric Turner

  • Temporal Difference Models: Model-Free Deep RL for Model-Based Control

    Vitchyr Pong;Shixiang Gu;Murtaza Dalal;Sergey Levine

  • MuProp: Unbiased Backpropagation for Stochastic Neural Networks

    Shixiang Gu;Shixiang Gu;Sergey Levine;Ilya Sutskever;Andriy Mnih

  • Dynamics-Aware Unsupervised Discovery of Skills

    Archit Sharma;Shixiang Gu;Sergey Levine;Vikash Kumar

  • Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

    Shixiang Gu;Timothy P. Lillicrap;Zoubin Ghahramani;Richard E. Turner

  • Sequence tutor: conservative fine-tuning of sequence generation models with KL-control

    Natasha Jaques;Shixiang Gu;Dzmitry Bahdanau;José Miguel Hernández-Lobato

  • A Divergence Minimization Perspective on Imitation Learning Methods

    Seyed Kamyar Seyed Ghasemipour;Richard S. Zemel;Shixiang Gu

  • Tuning Recurrent Neural Networks with Reinforcement Learning

    Natasha Jaques;Shixiang Gu;Richard E. Turner;Douglas Eck

  • Neural adaptive sequential Monte Carlo

    Shixiang Gu;Zoubin Ghahramani;Richard E. Turner

  • Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

    Ofir Nachum;Shixiang Gu;Honglak Lee;Sergey Levine

  • Aligning Text-to-Image Models using Human Feedback

    Unknown

  • Language as an Abstraction for Hierarchical Deep Reinforcement Learning

    YiDing Jiang;Shixiang Gu;Kevin P. Murphy;Chelsea Finn

  • Categorical Reparametrization with Gumble-Softmax

    Eric Jang;Shixiang Gu;Ben Poole

  • The Mirage of Action-Dependent Baselines in Reinforcement Learning.

    George Tucker;Surya Bhupatiraju;Shixiang Gu;Richard E. Turner

  • Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog

    Natasha Jaques;Asma Ghandeharioun;Judy Hanwen Shen;Craig Ferguson

Frequent Co-Authors

Sergey Levine
Sergey Levine University of California, Berkeley
Richard E. Turner
Richard E. Turner University of Cambridge
Timothy P. Lillicrap
Timothy P. Lillicrap University College London
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Vikash Kumar
Vikash Kumar University of Washington
Honglak Lee
Honglak Lee University of Michigan–Ann Arbor
George Tucker
George Tucker Google (United States)
Yutaka Matsuo
Yutaka Matsuo University of Tokyo
Douglas Eck
Douglas Eck Google (United States)

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