World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
8539
World Ranking
10524
National Ranking
4408

Tomas Pfister 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 Tomas Pfister 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: 149 publications — 26th percentile

26% of scientists in this discipline score the same or lower.

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

Tomas Pfister 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 Tomas Pfister 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: 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

Tomas Pfister is affiliated with Google in the United States and has contributed extensively to the field of computer science with a focus on artificial intelligence. Their research spans multiple subfields including artificial intelligence, computer vision and pattern recognition, signal processing, management science and operations research, and epidemiology.

Their main topics of work include:

  • Domain Adaptation and Few-Shot Learning
  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Machine Learning and Data Classification
  • Anomaly Detection Techniques and Applications
  • Time Series Analysis and Forecasting

Pfister has published extensively in several venues, with a strong presence on arXiv and in major conferences:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • npj Digital Medicine
  • 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Recent publications include:

  • "Learning to Prompt for Continual Learning," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "A Simple Semi-Supervised Learning Framework for Object Detection," 2020, arXiv (Cornell University)
  • "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," 2021, International Journal of Forecasting
  • "Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding," 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • "TabNet: Attentive Interpretable Tabular Learning," 2021, Proceedings of the AAAI Conference on Artificial Intelligence

Frequent co-authors working alongside Pfister include:

  • Sercan Ö. Arık
  • Chunliang Li
  • Jinsung Yoon
  • Chen-Yu Lee
  • Zizhao Zhang

Pfister's body of work includes 173 publications predominantly in computer science, with 115 specifically focusing on artificial intelligence and 40 addressing computer vision and pattern recognition. Their research covers diverse applications and techniques, reflecting a multidisciplinary approach spanning theory and applied machine learning. This range indicates an active role in advancing methods related to learning frameworks, interpretable models, and forecasting techniques.

Best Publications

  • Learning from Simulated and Unsupervised Images through Adversarial Training

    Ashish Shrivastava;Tomas Pfister;Oncel Tuzel;Joshua Susskind

  • Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

    Bryan Lim;Sercan Ömer Arik;Nicolas Loeff;Tomas Pfister

  • CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

    Chun-Liang Li;Kihyuk Sohn;Jinsung Yoon;Tomas Pfister

  • TabNet: Attentive Interpretable Tabular Learning.

    Sercan Ömer Arik;Tomas Pfister

  • A Spontaneous Micro-expression Database: Inducement, collection and baseline

    Xiaobai Li;Tomas Pfister;Xiaohua Huang;Guoying Zhao

  • Flowing ConvNets for Human Pose Estimation in Videos

    Tomas Pfister;James Charles;Andrew Zisserman

  • Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-Expression Spotting and Recognition Methods

    Xiaobai Li;Xiaopeng Hong;Antti Moilanen;Xiaohua Huang

  • Learning to Prompt for Continual Learning

    Unknown

  • Recognising spontaneous facial micro-expressions

    Tomas Pfister;Xiaobai Li;Guoying Zhao;Matti Pietikainen

  • A Simple Semi-Supervised Learning Framework for Object Detection.

    Kihyuk Sohn;Zizhao Zhang;Chun-Liang Li;Han Zhang

  • DualPrompt: Complementary Prompting for Rehearsal-Free Continual Learning

    Unknown

  • PseudoSeg: Designing Pseudo Labels for Semantic Segmentation

    Yuliang Zou;Zizhao Zhang;Han Zhang;Chun-Liang Li

  • Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

    Unknown

  • Deep Convolutional Neural Networks for Efficient Pose Estimation in Gesture Videos

    Tomas Pfister;Karen Simonyan;James Charles;Andrew Zisserman

  • Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

    Bryan Lim;Sercan O. Arik;Nicolas Loeff;Tomas Pfister

  • Consistency-Based Semi-supervised Active Learning: Towards Minimizing Labeling Cost

    Mingfei Gao;Zizhao Zhang;Guo Yu;Sercan Ömer Arik

  • TabNet: Attentive Interpretable Tabular Learning

    Sercan O. Arik;Tomas Pfister

  • Learning from Simulated and Unsupervised Images through Adversarial Training

    Ashish Shrivastava;Tomas Pfister;Oncel Tuzel;Josh Susskind

  • Distilling Effective Supervision From Severe Label Noise

    Zizhao Zhang;Han Zhang;Sercan O. Arik;Honglak Lee

  • Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding

    Unknown

  • Differentiating spontaneous from posed facial expressions within a generic facial expression recognition framework

    Tomas Pfister;Xiaobai Li;Guoying Zhao;Matti Pietikainen

  • Flowing ConvNets for Human Pose Estimation in Videos

    Tomas Pfister;James Charles;Andrew Zisserman

  • Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval

    Unknown

  • Personalizing Human Video Pose Estimation

    James Charles;Tomas Pfister;Derek Magee;David Hogg

  • Domain-Adaptive Discriminative One-Shot Learning of Gestures

    Tomas Pfister;James Charles;Andrew Zisserman

  • Learning and Evaluating Representations for Deep One-Class Classification

    Kihyuk Sohn;Chun-Liang Li;Jinsung Yoon;Minho Jin

  • Automatic and Efficient Human Pose Estimation for Sign Language Videos

    James Charles;Tomas Pfister;Mark Everingham;Andrew Zisserman

  • Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

    Estee Y Cramer;Evan L Ray;Velma K Lopez;Johannes Bracher

  • Data Valuation using Reinforcement Learning

    Jinsung Yoon;Sercan Arik;Tomas Pfister

Frequent Co-Authors

Andrew Zisserman
Andrew Zisserman University of Oxford
Matti Pietikäinen
Matti Pietikäinen University of Oulu
Kihyuk Sohn
Kihyuk Sohn Google (United States)
Guoying Zhao
Guoying Zhao University of Oulu
Linchao Zhu
Linchao Zhu University of Technology Sydney
David C. Hogg
David C. Hogg University of Leeds
Been Kim
Been Kim Google (United States)
Pradeep Ravikumar
Pradeep Ravikumar Carnegie Mellon University
Honglak Lee
Honglak Lee University of Michigan–Ann Arbor
Xiang Zhang
Xiang Zhang University of Hong Kong

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