World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
23916
World Ranking
2535
National Ranking
93

Chris Pal 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 Chris Pal 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: 221 publications — 54th percentile

54% of scientists in this discipline score the same or lower.

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

Chris Pal 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 Chris Pal 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: 64 D-Index — 82nd percentile

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

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

Overview

Chris Pal is affiliated with Polytechnique Montréal in Canada and conducts research primarily in Computer Science. Their work spans various subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Electrical and Electronic Engineering, and Management Science and Operations Research.

Their research topics cover a range of areas within these fields, focusing on:

  • Machine Learning and Data Classification
  • Reinforcement Learning in Robotics
  • Generative Adversarial Networks and Image Synthesis
  • Speech Recognition and Synthesis
  • Natural Language Processing Techniques
  • Handwritten Text Recognition Techniques
  • Data Quality and Management

Chris Pal has contributed to several publication venues, with a significant number of papers appearing in arXiv (Cornell University). Other frequent venues include:

  • Proceedings of the AAAI Conference on Artificial Intelligence
  • PolyPublie (École Polytechnique de Montréal)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Notable recent papers authored or co-authored by Chris Pal include:

  • "Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning" (2020), published in Proceedings of the AAAI Conference on Artificial Intelligence
  • "Role-Wise Data Augmentation for Knowledge Distillation" (2020), published on arXiv (Cornell University)
  • "Predicting Infectiousness for Proactive Contact Tracing" (2020), appearing in PolyPublie (École Polytechnique de Montréal)
  • "Workflow Discovery from Dialogues in the Low Data Regime" (2022), published on arXiv (Cornell University)
  • "ArK: Augmented Reality with Knowledge Interactive Emergent Ability" (2023), published on arXiv (Cornell University)

Collaboration is an element of their research, with frequent co-authors including Yoshua Bengio, Nasim Rahaman, Bernhard Schölkopf, Jie Fu, and David Vázquez. Each of these collaborators has co-authored multiple papers with Chris Pal, reflecting ongoing research partnerships.

Best Publications

  • Brain tumor segmentation with Deep Neural Networks

    Mohammad Havaei;Axel Davy;David Warde-Farley;Antoine Biard

  • Theano: A Python framework for fast computation of mathematical expressions

    Rami Al-Rfou;Guillaume Alain;Amjad Almahairi

  • Describing Videos by Exploiting Temporal Structure

    Li Yao;Atousa Torabi;Kyunghyun Cho;Nicolas Ballas

  • The Liver Tumor Segmentation Benchmark (LiTS)

    Patrick Bilic;Patrick Ferdinand Christ;Eugene Vorontsov;Grzegorz Chlebus

  • The Importance of Skip Connections in Biomedical Image Segmentation

    Michal Drozdzal;Eugene Vorontsov;Gabriel Chartrand;Samuel Kadoury

  • Learning Conditional Random Fields for Stereo

    D. Scharstein;C. Pal

  • Deep Learning: A Primer for Radiologists

    Gabriel Chartrand;Phillip M Cheng;Eugene Vorontsov;Michal Drozdzal

  • Activity recognition using the velocity histories of tracked keypoints

    Ross Messing;Chris Pal;Henry Kautz

  • Delving Deeper into Convolutional Networks for Learning Video Representations

    Nicolas Ballas;Li Yao;Chris Pal;Aaron Courville

  • ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

    Oskar Maier;Bjoern H. Menze;Janina von der Gablentz;Levin Häni

  • EmoNets: Multimodal deep learning approaches for emotion recognition in video

    Samira Ebrahimi Kahou;Xavier Bouthillier;Pascal Lamblin;Çaglar Gülçehre

  • Combining modality specific deep neural networks for emotion recognition in video

    Samira Ebrahimi Kahou;Christopher Pal;Xavier Bouthillier;Pierre Froumenty

  • Recurrent Neural Networks for Emotion Recognition in Video

    Samira Ebrahimi Kahou;Vincent Michalski;Kishore Konda;Roland Memisevic

  • Real-time preview system and method for panoramic images

    Chris Pal;Matthew Uyttendaele;Eric Rudolph;Patrick Baudisch

  • Deep Complex Networks

    Chiheb Trabelsi;Olexa Bilaniuk;Ying Zhang;Dmitriy Serdyuk

  • Movie Description

    Anna Rohrbach;Atousa Torabi;Marcus Rohrbach;Niket Tandon

  • A Panoramic View of Yeast Noncoding RNA Processing

    Wen Tao Peng;Mark D. Robinson;Sanie Mnaimneh;Nevan J. Krogan

  • Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

    Sandeep Subramanian;Adam Trischler;Yoshua Bengio;Christopher J Pal

  • Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations.

    David Krueger;Tegan Maharaj;János Kramár;Mohammad Pezeshki

  • Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation

    Michal Drozdzal;Michal Drozdzal;Gabriel Chartrand;Eugene Vorontsov;Mahsa Shakeri

  • Towards Deep Conversational Recommendations

    Raymond Li;Samira Ebrahimi Kahou;Hannes Schulz;Vincent Michalski

  • A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

    Yoshua Bengio;Tristan Deleu;Nasim Rahaman;Nan Rosemary Ke

Frequent Co-Authors

Yoshua Bengio
Yoshua Bengio University of Montreal
Aaron Courville
Aaron Courville University of Montreal
Pascal Vincent
Pascal Vincent Facebook (United States)
Samuel Kadoury
Samuel Kadoury Polytechnique Montréal
Andrew McCallum
Andrew McCallum University of Massachusetts Amherst
Adam Trischler
Adam Trischler Microsoft (United States)
Hugo Larochelle
Hugo Larochelle Google (United States)
Nicolas Ballas
Nicolas Ballas Facebook (United States)
Ian H. Witten
Ian H. Witten University of Waikato
Richard Szeliski
Richard Szeliski University of Washington

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