World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
7922
World Ranking
7230
National Ranking
3157

Yuandong Tian 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 Yuandong Tian 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: 126 publications — 17th percentile

17% of scientists in this discipline score the same or lower.

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

Yuandong Tian 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 Yuandong Tian 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Yuandong Tian is affiliated with Facebook in the United States. Their primary field of study is Computer Science, with a focus on several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Electrical and Electronic Engineering, and Computer Networks and Communications.

Their research covers multiple main topics such as Advanced Neural Network Applications, Topic Modeling, Neural Networks and Applications, Natural Language Processing Techniques, Domain Adaptation and Few-Shot Learning, Recommender Systems and Techniques, and Multi-Agent Systems and Negotiation.

Yuandong Tian has contributed to various scholarly papers. Notable recent publications include:

  • A Cookbook of Self-Supervised Learning, 2023, arXiv (Cornell University)
  • Understanding self-supervised Learning Dynamics without Contrastive Pairs, 2021, arXiv (Cornell University)
  • Neural Architecture Search Using Deep Neural Networks and Monte Carlo Tree Search, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Sample-Efficient Neural Architecture Search by Learning Actions for Monte Carlo Tree Search, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Real-world Video Adaptation with Reinforcement Learning, 2020, arXiv (Cornell University)

Frequent coauthors collaborating with Tian include:

  • Beidi Chen
  • Sainbayar Sukhbaatar
  • Zechun Liu
  • Daochen Zha
  • Louis Feng

Publication venues where Tian most often publishes work are:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • IEEE Micro

Best Publications

  • FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

    Bichen Wu;Kurt Keutzer;Xiaoliang Dai;Peizhao Zhang

  • Simple Baseline for Visual Question Answering

    Bolei Zhou;Yuandong Tian;Sainbayar Sukhbaatar;Arthur Szlam

  • Single Image 3D Interpreter Network

    Jiajun Wu;Tianfan Xue;Joseph J. Lim;Joseph J. Lim;Yuandong Tian

  • Building Generalizable Agents with a Realistic and Rich 3D Environment

    Yi Wu;Yuxin Wu;Georgia Gkioxari;Yuandong Tian

  • FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions

    Alvin Wan;Xiaoliang Dai;Peizhao Zhang;Zijian He

  • Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search

    Bichen Wu;Yanghan Wang;Peizhao Zhang;Yuandong Tian

  • EasyAlbum: an interactive photo annotation system based on face clustering and re-ranking

    Jingyu Cui;Fang Wen;Rong Xiao;Yuandong Tian

  • Exploring the spatial hierarchy of mixture models for human pose estimation

    Yuandong Tian;C. Lawrence Zitnick;Srinivasa G. Narasimhan

  • Semantic Amodal Segmentation

    Yan Zhu;Yuandong Tian;Dimitris Metaxas;Piotr Dollar

  • Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning

    Yuxin Wu;Yuandong Tian

  • Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima

    Simon S. Du;Jason D. Lee;Yuandong Tian;Barnabas Poczos

  • One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers

    Ari S. Morcos;Haonan Yu;Michela Paganini;Yuandong Tian

  • An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis

    Yuandong Tian

  • Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees.

    Yuping Luo;Huazhe Xu;Yuanzhi Li;Yuandong Tian

  • Seeing through water: Image restoration using model-based tracking

    Yuandong Tian;Srinivasa G. Narasimhan

  • ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

    Yuandong Tian;Qucheng Gong;Wenling Shang;Yuxin Wu

  • A Face Annotation Framework with Partial Clustering and Interactive Labeling

    Yuandong Tian;Wei Liu;Rong Xiao;Fang Wen

  • Learning to Perform Local Rewriting for Combinatorial Optimization

    Xinyun Chen;Yuandong Tian

  • Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction

    Qingquan Song;Dehua Cheng;Hanning Zhou;Jiyan Yang

  • RiFeGAN: Rich Feature Generation for Text-to-Image Synthesis From Prior Knowledge

    Jun Cheng;Fuxiang Wu;Yanling Tian;Lei Wang

  • Learning to Perform Local Rewriting for Combinatorial Optimization

    Xinyun Chen;Yuandong Tian

  • FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

    Bichen Wu;Xiaoliang Dai;Peizhao Zhang;Yanghan Wang

  • Semantic Amodal Segmentation

    Yan Zhu;Yuandong Tian;Dimitris Mexatas;Piotr Dollár

Frequent Co-Authors

Srinivasa G. Narasimhan
Srinivasa G. Narasimhan Carnegie Mellon University
Rodrigo Fonseca
Rodrigo Fonseca Brown University
Xinlei Chen
Xinlei Chen Facebook (United States)
Joseph E. Gonzalez
Joseph E. Gonzalez University of California, Berkeley
Simon S. Du
Simon S. Du University of Washington
Kurt Keutzer
Kurt Keutzer University of California, Berkeley
C. Lawrence Zitnick
C. Lawrence Zitnick Facebook (United States)
Jason D. Lee
Jason D. Lee Princeton University
Stuart Russell
Stuart Russell University of California, Berkeley

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