World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
67738
World Ranking
4169
National Ranking
1969

James Hays 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 James Hays 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: 108 publications — 11th percentile

11% of scientists in this discipline score the same or lower.

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

James Hays 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 James Hays 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: 55 D-Index — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2015 - Fellow of Alfred P. Sloan Foundation

Overview

James Hays is affiliated with the Georgia Institute of Technology in the United States. Their research primarily spans the fields of Computer Science and Engineering, with a significant focus on computer vision and related subfields.

The main areas of study for James Hays include:

  • Computer Science
  • Engineering

Within these fields, their subfields of focus are:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Aerospace Engineering
  • Automotive Engineering
  • Biomedical Engineering

The research topics frequently covered by James Hays encompass:

  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Robotics and Sensor-Based Localization
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Advanced Vision and Imaging
  • Autonomous Vehicle Technology and Safety

James Hays has published extensively, with notable works appearing in a variety of venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Lecture Notes in Computer Science
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Scientific Reports
  • Atmospheric Measurement Techniques

Selected recent papers by James Hays are:

  • RU-AI: A Large Multimodal Dataset for Machine Generated Content Detection, 2024, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting, 2023, arXiv (Cornell University)
  • PVA: Pixel-aligned Volumetric Avatars, 2021, arXiv (Cornell University)
  • Localizing and orienting street views using overhead imagery, 2024, arXiv (Cornell University)

Collaboration plays a role in their work, with frequent co-authors being:

  • Deva Ramanan
  • Charles C. Kemp
  • Chengcheng Tang
  • Samarth Brahmbhatt
  • Christopher D. Twigg

James Hays received recognition as a Fellow of the Alfred P. Sloan Foundation in 2015.

Best Publications

  • Microsoft COCO: Common Objects in Context

    Tsung-Yi Lin;Michael Maire;Serge J. Belongie;James Hays

  • SUN database: Large-scale scene recognition from abbey to zoo

    Jianxiong Xiao;James Hays;Krista A. Ehinger;Aude Oliva

  • Microsoft COCO: Common Objects in Context

    Tsung-Yi Lin;Michael Maire;Serge Belongie;Lubomir Bourdev

  • Argoverse: 3D Tracking and Forecasting With Rich Maps

    Ming-Fang Chang;Deva Ramanan;James Hays;John Lambert

  • IM2GPS: estimating geographic information from a single image

    J. Hays;A.A. Efros

  • How do humans sketch objects

    Mathias Eitz;James Hays;Marc Alexa

  • Scene completion using millions of photographs

    James Hays;Alexei A. Efros

  • SUN attribute database: Discovering, annotating, and recognizing scene attributes

    Genevieve Patterson;James Hays

  • An empirical study of context in object detection

    Santosh K Divvala;Derek Hoiem;James H Hays;Alexei A Efros

  • The sketchy database: learning to retrieve badly drawn bunnies

    Patsorn Sangkloy;Nathan Burnell;Cusuh Ham;James Hays

  • Scribbler: Controlling Deep Image Synthesis with Sketch and Color

    Patsorn Sangkloy;Jingwan Lu;Chen Fang;Fisher Yu

  • Edge-based blur kernel estimation using patch priors

    Libin Sun;Sunghyun Cho;Jue Wang;J. Hays

  • The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding

    Genevieve Patterson;Chen Xu;Hang Su;James Hays

  • Learning deep representations for ground-to-aerial geolocalization

    Tsung-Yi Lin;Yin Cui;Serge Belongie;James Hays

  • Geometry-Aware Learning of Maps for Camera Localization

    Samarth Brahmbhatt;Jinwei Gu;Kihwan Kim;James Hays

  • On Convergence and Stability of GANs

    Naveen Kodali;James Hays;Jacob Abernethy;Zsolt Kira

  • Near-regular texture analysis and manipulation

    Yanxi Liu;Wen-Chieh Lin;James Hays

  • SUN Database: Exploring a Large Collection of Scene Categories

    Jianxiong Xiao;Krista A. Ehinger;James Hays;Antonio Torralba

  • SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis

    Wengling Chen;James Hays

  • Composing Text and Image for Image Retrieval - an Empirical Odyssey

    Nam Vo;Lu Jiang;Chen Sun;Kevin Murphy

  • Argoverse: 3D Tracking and Forecasting with Rich Maps

    Ming-Fang Chang;John Lambert;Patsorn Sangkloy;Jagjeet Singh

  • MapNet: Geometry-Aware Learning of Maps for Camera Localization

    Samarth Brahmbhatt;Jinwei Gu;Kihwan Kim;James Hays

Frequent Co-Authors

Alexei A. Efros
Alexei A. Efros University of California, Berkeley
Jianxiong Xiao
Jianxiong Xiao AutoX, Inc.
Serge Belongie
Serge Belongie University of Copenhagen
Yanxi Liu
Yanxi Liu Pennsylvania State University
Tsung-Yi Lin
Tsung-Yi Lin Nvidia (United States)
Irfan Essa
Irfan Essa Georgia Institute of Technology
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Fisher Yu
Fisher Yu ETH Zurich

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