World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
8371
World Ranking
7557
National Ranking
3285

Nanyun Peng 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 Nanyun Peng 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: 349 publications — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Nanyun Peng 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 Nanyun Peng 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: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Overview

Nanyun Peng is a researcher affiliated with the University of California, Los Angeles in the United States. Their research work primarily spans the field of Computer Science, with a focused emphasis on Artificial Intelligence. Within this broad domain, they have contributed substantially to subfields such as Computer Vision and Pattern Recognition, Information Systems, Molecular Biology, and Sociology and Political Science.

The scientist's main topics of work include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Speech and Dialogue Systems
  • Text Readability and Simplification
  • Artificial Intelligence in Games
  • Domain Adaptation and Few-Shot Learning

Nanyun Peng has an extensive publication record with a notable presence in several venues:

  • arXiv (Cornell University)
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Some of their recent papers include:

  • An Empirical Study of Training End-to-End Vision-and-Language Transformers (2022) - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • DEGREE: A Data-Efficient Generation-Based Event Extraction Model (2022) - Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction (2021) - Proceedings of the AAAI Conference on Artificial Intelligence
  • Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone (2022) - arXiv (Cornell University)
  • On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark (2022) - Findings of the Association for Computational Linguistics: ACL 2022

Frequent collaborators contributing to Nanyun Peng's research output include:

  • Kai-Wei Chang
  • Zi-Yi Dou
  • I-Hung Hsu
  • Kuan-Hao Huang
  • Prem Natarajan

Best Publications

  • Cross-Sentence N-ary Relation Extraction with Graph LSTMs

    Nanyun Peng;Hoifung Poon;Chris Quirk;Kristina Toutanova

  • An Empirical Study of Training End-to-End Vision-and-Language Transformers

    Unknown

  • Style Transfer in Text: Exploration and Evaluation

    Zhenxin Fu;Xiaoye Tan;Nanyun Peng;Dongyan Zhao

  • The Woman Worked as a Babysitter: On Biases in Language Generation

    Emily Sheng;Kai-Wei Chang;Premkumar Natarajan;Nanyun Peng

  • Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings

    Nanyun Peng;Mark Dredze

  • Plan-And-Write: Towards Better Automatic Storytelling

    Lili Yao;Nanyun Peng;Ralph M. Weischedel;Kevin Knight

  • Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning

    Nanyun Peng;Mark Dredze

  • Generalized Decoding for Pixel, Image, and Language

    Unknown

  • DEGREE: A Data-Efficient Generation-Based Event Extraction Model

    Unknown

  • Stack-Pointer Networks for Dependency Parsing.

    Xuezhe Ma;Zecong Hu;Jingzhou Liu;Nanyun Peng

  • On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing

    Wasi Uddin Ahmad;Zhisong Zhang;Xuezhe Ma;Eduard H. Hovy

  • Multi-task Domain Adaptation for Sequence Tagging

    Nanyun Peng;Mark Dredze

  • Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone

    Unknown

  • Towards Controllable Story Generation

    Nanyun Peng;Marjan Ghazvininejad;Jonathan May;Kevin Knight

  • Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

    Rujun Han;Qiang Ning;Nanyun Peng

  • Societal Biases in Language Generation: Progress and Challenges

    Emily Sheng;Kai-Wei Chang;Prem Natarajan;Nanyun Peng

  • Content Planning for Neural Story Generation with Aristotelian Rescoring

    Seraphina Goldfarb-Tarrant;Tuhin Chakrabarty;Ralph M. Weischedel;Nanyun Peng

  • Towards Controllable Biases in Language Generation

    Emily Sheng;Kai-Wei Chang;Premkumar Natarajan;Nanyun Peng

  • Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings

    Sarik Ghazarian;Johnny Tian-Zheng Wei;Aram Galstyan;Nanyun Peng

  • TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions

    Qiang Ning;Hao Wu;Rujun Han;Nanyun Peng

  • Modeling Word Forms Using Latent Underlying Morphs and Phonology

    Ryan Cotterell;Nanyun Peng;Jason Eisner

  • STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation

    Nader Akoury;Shufan Wang;Josh Whiting;Stephen Hood

  • Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering.

    Peifeng Wang;Nanyun Peng;Filip Ilievski;Pedro A. Szekely

  • Espresso: A Fast End-to-End Neural Speech Recognition Toolkit

    Yiming Wang;Sanjeev Khudanpur;Tongfei Chen;Hainan Xu

  • Cross-Sentence N-ary Relation Extraction with Graph LSTMs

    Nanyun Peng;Hoifung Poon;Chris Quirk;Kristina Toutanova

Frequent Co-Authors

Kai-Wei Chang
Kai-Wei Chang University of California, Los Angeles
Aram Galstyan
Aram Galstyan University of Southern California
Ralph Weischedel
Ralph Weischedel University of Southern California
Mark Dredze
Mark Dredze Johns Hopkins University
Dongyan Zhao
Dongyan Zhao Peking University
Rui Yan
Rui Yan Renmin University of China
Prem Natarajan
Prem Natarajan Capital One (United States)
Eduard Hovy
Eduard Hovy Carnegie Mellon University
Emilio Ferrara
Emilio Ferrara University of Southern California
Ryan Cotterell
Ryan Cotterell ETH Zurich

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens diverse options for further study and specialized careers. Many students find value in broadening their scope with related degrees like the best online electrical engineering programs USA offers. These programs often blend computing fundamentals with hardware and innovation skills.

For those seeking a quicker route to career advancement, consider easy certifications to get online. These can supplement your computer science foundation and lead to fast-growing tech roles.

If your goal is a graduate degree but you’re short on time, investigate the shortest master degree programs. Many U.S. universities now offer intensive, accelerated online options to help you upskill without a lengthy commitment.

Finally, it’s important to understand which programs can maximize your career potential. Discover which master's degree is most in demand in usa to align your education with today’s job market and future-proof your career.

Best Scientists Citing Nanyun Peng

Trending Scientists

Recently Published Articles