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D-Index & Metrics

Computer Science

D-Index
100
Citations
47777
World Ranking
365
National Ranking
199

Percy Liang 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 Percy Liang 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: 266 publications — 66th percentile

66% of scientists in this discipline score the same or lower.

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

Percy Liang 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 Percy Liang 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: 100 D-Index — 97th percentile

97% 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

  • 2015 - Fellow of Alfred P. Sloan Foundation

Overview

Percy Liang is affiliated with Stanford University in the United States. Their scholarly work is primarily centered in the field of Computer Science, with a significant focus on Artificial Intelligence. Other areas of research include Computer Vision and Pattern Recognition, Information Systems, Molecular Biology, and Safety Research.

The main research topics covered by Percy Liang encompass:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Adversarial Robustness in Machine Learning
  • Machine Learning and Data Classification
  • Explainable Artificial Intelligence (XAI)

Some of the recent papers associated with Percy Liang's research include:

  • Emergent Abilities of Large Language Models, 2022, arXiv (Cornell University)
  • Lost in the Middle: How Language Models Use Long Contexts, 2024, Transactions of the Association for Computational Linguistics
  • Holistic Evaluation of Language Models, 2023, Annals of the New York Academy of Sciences
  • Prefix-Tuning: Optimizing Continuous Prompts for Generation, 2021, arXiv (Cornell University)
  • WILDS: A Benchmark of in-the-Wild Distribution Shifts, 2020, The Caltech Institute Archives (California Institute of Technology)

Frequent co-authors who have collaborated with Percy Liang include:

  • Rishi Bommasani
  • Tatsunori Hashimoto
  • Michihiro Yasunaga
  • Ananya Kumar
  • Kevin Klyman

Publications have appeared in venues such as:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Proceedings of the AAAI/ACM Conference on AI Ethics and Society
  • Transactions of the Association for Computational Linguistics
  • Annals of the New York Academy of Sciences

Percy Liang has been recognized as a Fellow of the Alfred P. Sloan Foundation, an award received in 2015.

Best Publications

  • SQuAD: 100,000+ Questions for Machine Comprehension of Text

    Pranav Rajpurkar;Jian Zhang;Konstantin Lopyrev;Percy Liang

  • Prefix-Tuning: Optimizing Continuous Prompts for Generation

    Xiang Lisa Li;Percy Liang

  • Know What You Don't Know: Unanswerable Questions for SQuAD

    Pranav Rajpurkar;Robin Jia;Percy Liang

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • Semantic Parsing on Freebase from Question-Answer Pairs

    Jonathan Berant;Andrew Chou;Roy Frostig;Percy Liang

  • Understanding black-box predictions via influence functions

    Pang Wei Koh;Percy Liang

  • Adversarial Examples for Evaluating Reading Comprehension Systems

    Robin Jia;Percy Liang

  • Generative Agents: Interactive Simulacra of Human Behavior

    Unknown

  • Holistic Evaluation of Language Models

    Unknown

  • QuAC: Question Answering in Context

    Eunsol Choi;He He;Mohit Iyyer;Mohit Iyyer;Mark Yatskar

  • Learning Dependency-Based Compositional Semantics

    Percy Liang;Michael Jordan;Dan Klein

  • Delete, retrieve, generate: A simple approach to sentiment and style transfer

    Juncen Li;Robin Jia;He He;Percy Liang

  • Data Recombination for Neural Semantic Parsing

    Robin Jia;Percy Liang

  • Compositional Semantic Parsing on Semi-Structured Tables

    Panupong Pasupat;Percy Liang

  • Semantic Parsing via Paraphrasing

    Jonathan Berant;Percy Liang

  • Strategies for Pre-training Graph Neural Networks

    Weihua Hu;Bowen Liu;Joseph Gomes;Marinka Zitnik

  • Certified Defenses against Adversarial Examples

    Aditi Raghunathan;Jacob Steinhardt;Percy Liang

  • Dropout Training as Adaptive Regularization

    Stefan Wager;Sida Wang;Percy S Liang

  • Diffusion-LM Improves Controllable Text Generation

    Unknown

  • Alignment by Agreement

    Percy Liang;Ben Taskar;Dan Klein

  • QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering.

    Michihiro Yasunaga;Hongyu Ren;Antoine Bosselut;Percy Liang

  • Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization.

    Shiori Sagawa;Pang Wei Koh;Tatsunori B. Hashimoto;Percy Liang

  • Unlabeled Data Improves Adversarial Robustness

    Yair Carmon;Aditi Raghunathan;Ludwig Schmidt;John C. Duchi

Frequent Co-Authors

Daniel Klein
Daniel Klein University of California, Berkeley
Pang Wei Koh
Pang Wei Koh University of Washington
Tatsunori B. Hashimoto
Tatsunori B. Hashimoto Stanford University
Christopher D. Manning
Christopher D. Manning Stanford University
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Jure Leskovec
Jure Leskovec Stanford University
John C. Duchi
John C. Duchi Stanford University
Tengyu Ma
Tengyu Ma Stanford University
Alex Aiken
Alex Aiken Stanford University

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