World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
16244
World Ranking
8150
National Ranking
3492

Lihong Li 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 Lihong Li 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: 97 publications — 8th percentile

8% of scientists in this discipline score the same or lower.

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

Lihong Li 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 Lihong Li 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Lihong Li is a researcher affiliated with Amazon in the United States. Their academic contributions span multiple fields, primarily focused on Computer Science and Engineering. Within these domains, they have emphasized specific subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Management Science and Operations Research, Biomedical Engineering, and Industrial and Manufacturing Engineering.

The scientist's publications cover a range of topics. Their main research interests include Reinforcement Learning in Robotics, Advanced Bandit Algorithms Research, Machine Learning and Algorithms, Photoacoustic and Ultrasonic Imaging, Video Surveillance and Tracking Methods, Advanced Neural Network Applications, and Advanced Image and Video Retrieval Techniques.

Frequent publication venues for their work include:

  • arXiv (Cornell University)
  • Journal of Advanced Computational Intelligence and Intelligent Informatics
  • Sensors
  • The Visual Computer
  • BioMedical Engineering OnLine

Among recent papers, the following are notable examples across different years and venues:

  • Multipath affinage stacked-hourglass networks for human pose estimation, 2020, Frontiers of Computer Science
  • Understanding Domain Randomization for Sim-to-real Transfer, 2021, arXiv (Cornell University)
  • Exploration of the correlation between GPCRs and drugs based on a learning to rank algorithm, 2020, Computers in Biology and Medicine
  • A Conceptual Framework for Collaborative Development of Intelligent Construction and Building Industrialization, 2022, Frontiers in Environmental Science
  • Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning, 2020, arXiv (Cornell University)

Lihong Li has collaborated with several frequent coauthors, including:

  • Pengtao Wang
  • Jiachen Hu
  • Liwei Wang
  • Feiyang Pan
  • Ziwei Zeng

Best Publications

  • A contextual-bandit approach to personalized news article recommendation

    Lihong Li;Wei Chu;John Langford;Robert E. Schapire

  • Parallelized Stochastic Gradient Descent

    Martin Zinkevich;Markus Weimer;Lihong Li;Alex J. Smola

  • An Empirical Evaluation of Thompson Sampling

    Olivier Chapelle;Lihong Li

  • Contextual bandits with linear Payoff functions

    Wei Chu;Lihong Li;Lev Reyzin;Robert E. Schapire

  • Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms

    Lihong Li;Wei Chu;John Langford;Xuanhui Wang

  • Sparse Online Learning via Truncated Gradient

    John Langford;Lihong Li;Tong Zhang

  • PAC model-free reinforcement learning

    Alexander L. Strehl;Lihong Li;Eric Wiewiora;John Langford

  • Doubly Robust Policy Evaluation and Learning

    John Langford;Lihong Li;Miroslav Dud k

  • Doubly Robust Policy Evaluation and Learning

    Miroslav Dudik;John Langford;Lihong Li

  • Doubly robust off-policy value evaluation for reinforcement learning

    Nan Jiang;Lihong Li

  • Towards a Unified Theory of State Abstraction for MDPs.

    Lihong Li;Thomas J. Walsh;Michael L. Littman

  • Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

    Alekh Agarwal;Daniel Hsu;Satyen Kale;John Langford

  • Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access

    Bhuwan Dhingra;Lihong Li;Xiujun Li;Jianfeng Gao

  • Reinforcement Learning in Finite MDPs: PAC Analysis

    Alexander L. Strehl;Lihong Li;Michael L. Littman

  • Knows what it knows: a framework for self-aware learning

    Lihong Li;Michael L. Littman;Thomas J. Walsh

  • Knows what it knows: a framework for self-aware learning

    Lihong Li;Michael L. Littman;Thomas J. Walsh;Alexander L. Strehl

  • Doubly robust policy evaluation and optimization

    Miroslav Dudík;Dumitru Erhan;John Langford;Lihong Li

  • Contextual Bandit Algorithms with Supervised Learning Guarantees

    Alina Beygelzimer;John Langford;Lihong Li;Lev Reyzin

  • An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning

    Ronald Parr;Lihong Li;Gavin Taylor;Christopher Painter-Wakefield

  • BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems

    Unknown

  • Learning from Logged Implicit Exploration Data

    Alex Strehl;John Langford;Lihong Li;Sham M Kakade

  • Neural Approaches to Conversational AI.

    Jianfeng Gao;Michel Galley;Lihong Li

Frequent Co-Authors

Jianfeng Gao
Jianfeng Gao Microsoft (United States)
John Langford
John Langford Microsoft (United States)
Michael L. Littman
Michael L. Littman Brown University
Li Deng
Li Deng Citadel
Wei Chu
Wei Chu Sichuan University
Dale Schuurmans
Dale Schuurmans University of Alberta
Dengyong Zhou
Dengyong Zhou Google (United States)
Emma Brunskill
Emma Brunskill Stanford University
Russell Greiner
Russell Greiner University of Alberta
Zachary C. Lipton
Zachary C. Lipton Carnegie Mellon University

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