World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
63
Citations
64901
World Ranking
2665
National Ranking
1322

Lawrence O. Hall 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 Lawrence O. Hall 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: 335 publications — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Lawrence O. Hall 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 Lawrence O. Hall 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: 63 D-Index — 81st percentile

81% 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

  • 2018 - Fellow of the Indian National Academy of Engineering (INAE)
  • 2012 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2003 - IEEE Fellow For contributions to the theory and practice of fuzzy pattern recognition.
  • 1985 - Fellow of Alfred P. Sloan Foundation
  • 1933 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

Lawrence O. Hall is affiliated with the University of South Florida in the United States. Their research contributions span several fields including radiology, nuclear medicine and imaging, artificial intelligence, biophysics, pulmonary and respiratory medicine, and statistical and nonlinear physics.

The scientist's work focuses on topics such as radiomics and machine learning in medical imaging, AI in cancer detection, cell image analysis techniques, COVID-19 diagnosis using AI, lung cancer diagnosis and treatment, complex network analysis techniques, and antenna design and analysis.

Frequent co-authors in their research include Karen Hawkins, Thomas Siegert, Stephen Welby, Ellen Randall, and James Matthews. Their publications commonly appear in journals such as IEEE Transactions on Systems Man and Cybernetics Systems, IEEE Transactions on Cybernetics, IEEE Photonics Technology Letters, IEEE Transactions on Circuits and Systems I Regular Papers, and IEEE Aerospace and Electronic Systems Magazine.

Recent papers by Lawrence O. Hall include the following:

  • IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS-I: REGULAR PAPERS, 2022, IEEE Transactions on Circuits and Systems I Regular Papers
  • IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS-I: REGULAR PAPERS, 2022, IEEE Transactions on Circuits and Systems I Regular Papers
  • Challenges for the Repeatability of Deep Learning Models, 2020, IEEE Access
  • IEEE/ASME Transactions on Mechatronics, 2021, IEEE/ASME Transactions on Mechatronics
  • IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS-I: REGULAR PAPERS, 2022, IEEE Transactions on Circuits and Systems I Regular Papers

Lawrence O. Hall has received several recognitions over the years, including being named an IEEE Fellow in 2003 for contributions to the theory and practice of fuzzy pattern recognition. The scientist is also a fellow of the Indian National Academy of Engineering (2018), the American Association for the Advancement of Science (2012 and earlier in 1933), and the Alfred P. Sloan Foundation (1985).

Best Publications

  • SMOTE: synthetic minority over-sampling technique

    Nitesh V. Chawla;Kevin W. Bowyer;Lawrence O. Hall;W. Philip Kegelmeyer

  • SMOTE: Synthetic Minority Over-sampling Technique

    N. V. Chawla;K. W. Bowyer;L. O. Hall;W. P. Kegelmeyer

  • Radiomics: the process and the challenges

    Virendra Kumar;Yuhua Gu;Satrajit Basu;Anders Berglund

  • SMOTEBoost: Improving Prediction of the Minority Class in Boosting

    Nitesh V. Chawla;Aleksandar Lazarevic;Lawrence O. Hall;Kevin W. Bowyer

  • Review of MR image segmentation techniques using pattern recognition.

    J. C. Bezdek;L. O. Hall;L. P. Clarke

  • MRI segmentation: Methods and applications

    L.P. Clarke;R.P. Velthuizen;M.A. Camacho;J.J. Heine

  • A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

    L.O. Hall;A.M. Bensaid;L.P. Clarke;R.P. Velthuizen

  • Automatic tumor segmentation using knowledge-based techniques

    M.C. Clark;L.O. Hall;D.B. Goldgof;R. Velthuizen

  • Clustering with a genetically optimized approach

    L.O. Hall;I.B. Ozyurt;J.C. Bezdek

  • Validity-guided (re)clustering with applications to image segmentation

    A.M. Bensaid;L.O. Hall;J.C. Bezdek;L.P. Clarke

  • Fuzzy c-Means Algorithms for Very Large Data

    T. C. Havens;J. C. Bezdek;C. Leckie;L. O. Hall

  • Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

    M. Zhou;J. Scott;B. Chaudhury;L. Hall

  • Partially supervised clustering for image segmentation

    Amine M. Bensaid;Lawrence O. Hall;James C. Bezdek;Laurence P. Clarke

  • Automatic segmentation of non-enhancing brain tumors in magnetic resonance images

    Lynn M Fletcher-Heath;Lawrence O Hall;Dmitry B Goldgof;F.Reed Murtagh

  • Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.

    Yoganand Balagurunathan;Yuhua Gu;Hua Wang;Virendra Kumar

  • Active Learning to Recognize Multiple Types of Plankton

    Tong Luo;Kurt Kramer;Dmitry B. Goldgof;Lawrence O. Hall

  • Automatically countering imbalance and its empirical relationship to cost

    Nitesh V. Chawla;David A. Cieslak;Lawrence O. Hall;Ajay Joshi

  • Ensemble diversity measures and their application to thinning

    Robert E. Banfield;Lawrence O. Hall;Kevin W. Bowyer;W.Philip Kegelmeyer

  • MRI segmentation using fuzzy clustering techniques

    M.C. Clark;L.O. Hall;D.B. Goldgof;L.P. Clarke

  • A Comparison of Decision Tree Ensemble Creation Techniques

    R.E. Banfield;L.O. Hall;K.W. Bowyer;W.P. Kegelmeyer

  • Automatic segmentation of non-enhanced brain tumors in magnetic resonance images

    Lynn Marie Fletcher-Heath;Lawrence O. Hall

Frequent Co-Authors

Dmitry B. Goldgof
Dmitry B. Goldgof University of South Florida
Robert J. Gillies
Robert J. Gillies Moffitt Cancer Center
Kevin W. Bowyer
Kevin W. Bowyer University of Notre Dame
Robert A. Gatenby
Robert A. Gatenby Moffitt Cancer Center
Laurence P. Clarke
Laurence P. Clarke University of South Florida
Nitesh V. Chawla
Nitesh V. Chawla University of Notre Dame
James C. Bezdek
James C. Bezdek University of Melbourne
Abraham Kandel
Abraham Kandel University of South Florida
Jeffrey P. Krischer
Jeffrey P. Krischer University of South Florida
Thomas L. Hopkins
Thomas L. Hopkins University of South Florida St. Petersburg

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