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D-Index & Metrics

Computer Science

D-Index
37
Citations
6175
World Ranking
10713
National Ranking
4476

Taylor Berg-Kirkpatrick 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 Taylor Berg-Kirkpatrick 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 117 publications — 14th percentile

14% of scientists in this discipline score the same or lower.

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

Taylor Berg-Kirkpatrick 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 Taylor Berg-Kirkpatrick sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Taylor Berg-Kirkpatrick is a researcher affiliated with the University of California, San Diego in the United States. Their work mainly spans the field of computer science, with a focus on artificial intelligence, computer vision and pattern recognition, and signal processing. Their research also extends into cognitive neuroscience and music.

The scientist's publication record includes 260 works primarily related to computer science. Key subfields within their research encompass:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Cognitive Neuroscience
  • Music

The major topics they address in their work include:

  • Music and Audio Processing
  • Topic Modeling
  • Natural Language Processing Techniques
  • Music Technology and Sound Studies
  • Speech and Audio Processing
  • Neuroscience and Music Perception
  • Multimodal Machine Learning Applications

Frequent venues for their publications highlight a strong presence in preprint and conference proceedings, including:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recent papers authored or co-authored by the researcher include:

  • Towards a Unified View of Parameter-Efficient Transfer Learning, 2021, arXiv (Cornell University)
  • HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection, 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • A Probabilistic Formulation of Unsupervised Text Style Transfer, 2020, arXiv (Cornell University)
  • Mix and Match: Learning-free Controllable Text Generation using Energy Language Models, 2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Zero-Shot Audio Source Separation through Query-Based Learning from Weakly-Labeled Data, 2022, Proceedings of the AAAI Conference on Artificial Intelligence

Taylor Berg-Kirkpatrick has collaborated frequently with the following co-authors:

  • Julian McAuley
  • Shlomo Dubnov
  • Fatemehsadat Mireshghallah
  • Hao-Wen Dong
  • Kartik Goyal

Best Publications

  • Learning Bilingual Lexicons from Monolingual Corpora

    Aria Haghighi;Percy Liang;Taylor Berg-Kirkpatrick;Dan Klein

  • Speaker-Follower Models for Vision-and-Language Navigation

    Daniel Fried;Ronghang Hu;Volkan Cirik;Anna Rohrbach

  • Improved variational autoencoders for text modeling using dilated convolutions

    Zichao Yang;Zhiting Hu;Ruslan Salakhutdinov;Taylor Berg-Kirkpatrick

  • Large-Scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation

    Unknown

  • Learning Whom to Trust with MACE

    Dirk Hovy;Taylor Berg-Kirkpatrick;Ashish Vaswani;Eduard Hovy

  • Towards a Unified View of Parameter-Efficient Transfer Learning

    Junxian He;Chunting Zhou;Xuezhe Ma;Taylor Berg-Kirkpatrick

  • Painless Unsupervised Learning with Features

    Taylor Berg-Kirkpatrick;Alexandre Bouchard-Côté;John DeNero;Dan Klein

  • Unsupervised Text Style Transfer using Language Models as Discriminators

    Zichao Yang;Zhiting Hu;Chris Dyer;Eric P. Xing

  • Jointly Learning to Extract and Compress

    Taylor Berg-Kirkpatrick;Dan Gillick;Dan Klein

  • Lagging Inference Networks and Posterior Collapse in Variational Autoencoders

    Junxian He;Daniel Spokoyny;Graham Neubig;Taylor Berg-Kirkpatrick

  • HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection

    Unknown

  • An Empirical Investigation of Statistical Significance in NLP

    Taylor Berg-Kirkpatrick;David Burkett;Dan Klein

  • Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints

    Greg Durrett;Taylor Berg-Kirkpatrick;Dan Klein

  • Beyond BLEU: Training Neural Machine Translation with Semantic Similarity.

    John Wieting;Taylor Berg-Kirkpatrick;Kevin Gimpel;Graham Neubig

  • A Probabilistic Formulation of Unsupervised Text Style Transfer

    Junxian He;Xinyi Wang;Graham Neubig;Taylor Berg-Kirkpatrick

  • Learning to Describe Differences Between Pairs of Similar Images

    Harsh Jhamtani;Taylor Berg-Kirkpatrick

  • Tools for Automated Analysis of Cybercriminal Markets

    Rebecca S. Portnoff;Sadia Afroz;Greg Durrett;Jonathan K. Kummerfeld

  • SPINE: SParse Interpretable Neural Embeddings

    Anant Subramanian;Danish Pruthi;Harsh Jhamtani;Taylor Berg-Kirkpatrick

  • Using accelerometers to remotely and automatically characterize behavior in small animals.

    Talisin T. Hammond;Dwight Springthorpe;Rachel E. Walsh;Taylor Berg-Kirkpatrick

  • Phylogenetic Grammar Induction

    Taylor Berg-Kirkpatrick;Dan Klein

  • Using Syntax to Ground Referring Expressions in Natural Images.

    Volkan Cirik;Taylor Berg-Kirkpatrick;Louis-Philippe Morency

  • A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text

    Bohan Li;Junxian He;Graham Neubig;Taylor Berg-Kirkpatrick

Frequent Co-Authors

Graham Neubig
Graham Neubig Carnegie Mellon University
Daniel Klein
Daniel Klein University of California, Berkeley
Eduard Hovy
Eduard Hovy Carnegie Mellon University
Julian McAuley
Julian McAuley University of California, San Diego
Louis-Philippe Morency
Louis-Philippe Morency Carnegie Mellon University
Chris Dyer
Chris Dyer Google (United States)
Zhiting Hu
Zhiting Hu University of California, San Diego
Kevin Gimpel
Kevin Gimpel Toyota Technological Institute at Chicago
Eric P. Xing
Eric P. Xing Mohamed bin Zayed University of Artificial Intelligence

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