World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
10152
World Ranking
10488
National Ranking
4390

Christopher Kanan 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 Christopher Kanan 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: 104 publications — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Christopher Kanan 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 Christopher Kanan 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

Christopher Kanan is affiliated with the University of Rochester in the United States. Their research primarily spans the field of computer science, with a significant focus on artificial intelligence. This includes specialized work in computer vision and pattern recognition, radiology, nuclear medicine and imaging, pulmonary and respiratory medicine, and cognitive neuroscience.

The scientist's work addresses several interconnected topics, including domain adaptation and few-shot learning, AI applications in cancer detection, radiomics and machine learning in medical imaging, multimodal machine learning applications, advanced neural network applications, adversarial robustness in machine learning, and machine learning and data classification.

Christopher Kanan has contributed to numerous publications, with a recorded output of over a hundred papers, many appearing in venues such as arXiv (Cornell University), where they have published 37 papers. Other notable publication venues include Cancer Research, Journal of Clinical Oncology, Nature Medicine, and Modern Pathology.

Recent papers authored or coauthored by Kanan include the following:

  • "A foundation model for clinical-grade computational pathology and rare cancers detection" (2024, Nature Medicine)
  • "Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies" (2020, Modern Pathology)
  • "Independent real-world application of a clinical-grade automated prostate cancer detection system" (2021, The Journal of Pathology)
  • "Avalanche: An end-to-end library for continual learning" (2021, CINECA IRIS Institutional research information system, University of Pisa)
  • "Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities" (2020, Scientific Reports)

Frequent coauthors collaborating with Christopher Kanan include:

  • Tyler L. Hayes
  • Ran Godrich
  • Robik Shrestha
  • Adam Casson
  • Thomas J. Fuchs

Best Publications

  • Continual lifelong learning with neural networks: A review.

    German Ignacio Parisi;Ronald Kemker;Jose L. Part;Christopher Kanan

  • Measuring Catastrophic Forgetting in Neural Networks

    Unknown

  • Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning

    Unknown

  • Robotic grasp detection using deep convolutional neural networks

    Unknown

  • Color-to-Grayscale: Does the Method Matter in Image Recognition?

    Christopher Kanan;Garrison W. Cottrell

  • Visual question answering: Datasets, algorithms, and future challenges

    Unknown

  • An Analysis of Visual Question Answering Algorithms

    Unknown

  • REMIND Your Neural Network to Prevent Catastrophic Forgetting

    Tyler L. Hayes;Kushal Kafle;Robik Shrestha;Manoj Acharya

  • Robust classification of objects, faces, and flowers using natural image statistics

    Christopher Kanan;Garrison Cottrell

  • DVQA: Understanding Data Visualizations via Question Answering

    Kushal Kafle;Brian Price;Scott Cohen;Christopher Kanan

  • Memory Efficient Experience Replay for Streaming Learning

    Tyler L. Hayes;Nathan D. Cahill;Christopher Kanan

  • VAIS: A dataset for recognizing maritime imagery in the visible and infrared spectrums

    Mabel M. Zhang;Jean Choi;Kostas Daniilidis;Michael T. Wolf

  • Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies.

    Patricia Raciti;Jillian Sue;Rodrigo Ceballos;Ran Godrich

  • Independent real-world application of a clinical-grade automated prostate cancer detection system.

    Leonard M da Silva;Emilio M Pereira;Paulo Go Salles;Ran Godrich

  • Data Augmentation for Visual Question Answering

    Unknown

  • Self-Taught Feature Learning for Hyperspectral Image Classification

    Unknown

  • Answer-Type Prediction for Visual Question Answering

    Unknown

  • Lifelong Machine Learning With Deep Streaming Linear Discriminant Analysis

    Tyler L. Hayes;Christopher Kanan

  • Avalanche: an End-to-End Library for Continual Learning

    Vincenzo Lomonaco;Lorenzo Pellegrini;Andrea Cossu;Antonio Carta

  • Replay in Deep Learning: Current Approaches and Missing Biological Elements

    Tyler L. Hayes;Giri P. Krishnan;Maxim Bazhenov;Hava T. Siegelmann

  • Humans have idiosyncratic and task-specific scanpaths for judging faces.

    Christopher Kanan;Dina N.F. Bseiso;Nicholas A. Ray;Janet H. Hsiao

  • On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law

    Damien Teney;Kushal Kafle;Robik Shrestha;Ehsan Abbasnejad

  • Answer Them All! Toward Universal Visual Question Answering Models

    Robik Shrestha;Kushal Kafle;Christopher Kanan

  • Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery

    Ronald Kemker;Ryan Luu;Christopher Kanan

  • A negative case analysis of visual grounding methods for VQA

    Robik Shrestha;Kushal Kafle;Christopher Kanan

  • Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities.

    Rakshit Sunil Kothari;Zhizhuo Yang;Christopher Kanan;Reynold Bailey

  • RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking

    Aayush K. Chaudhary;Rakshit Kothari;Manoj Acharya;Shusil Dangi

  • On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law

    Damien Teney;Ehsan Abbasnejad;Kushal Kafle;Robik Shrestha

Frequent Co-Authors

Jorge S. Reis-Filho
Jorge S. Reis-Filho Memorial Sloan Kettering Cancer Center
Brian Price
Brian Price Adobe Systems (United States)
Davide Maltoni
Davide Maltoni University of Bologna
Hava T. Siegelmann
Hava T. Siegelmann University of Massachusetts Amherst
Sarat Chandarlapaty
Sarat Chandarlapaty Memorial Sloan Kettering Cancer Center
Terrence J. Sejnowski
Terrence J. Sejnowski Salk Institute for Biological Studies
Maxim Bazhenov
Maxim Bazhenov University of California, San Diego
Andreas S. Tolias
Andreas S. Tolias Baylor College of Medicine

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