World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
6447
World Ranking
10673
National Ranking
4465

Ian Davidson 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 Ian Davidson 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: 150 publications — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Ian Davidson 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 Ian Davidson 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

Ian Davidson is a researcher affiliated with the University of California, Davis in the United States. Their work spans multiple fields, primarily focusing on Engineering and Computer Science, with significant contributions in Electrical and Electronic Engineering and Artificial Intelligence.

The scientist's research covers several subfields and main topics, including:

  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Atomic and Molecular Physics, and Optics
  • Spectroscopy
  • Computer Vision and Pattern Recognition

  • Photonic Crystal and Fiber Optics
  • Advanced Fiber Laser Technologies
  • Optical Network Technologies
  • Advanced Fiber Optic Sensors
  • Spectroscopy and Laser Applications
  • Laser-Matter Interactions and Applications
  • Advanced Clustering Algorithms Research

Ian Davidson has published extensively, with recent papers including the following:

  • "Hollow core optical fibres with comparable attenuation to silica fibres between 600 and 1100 nm" (2020) in Nature Communications
  • "0.174 dB/km Hollow Core Double Nested Antiresonant Nodeless Fiber (DNANF)" (2022) at the Optical Fiber Communication Conference (OFC) 2022
  • "Hundred-meter-scale, kilowatt peak-power, near-diffraction-limited, mid-infrared pulse delivery via the low-loss hollow-core fiber" (2022) in Optics Letters
  • "A framework for deep constrained clustering" (2021) in Data Mining and Knowledge Discovery
  • "Comparing machine and deep learning-based algorithms for prediction of clinical improvement in psychosis with functional magnetic resonance imaging" (2020) in Human Brain Mapping

Their frequent co-authors include:

  • Francesco Poletti
  • David J. Richardson
  • Natalie V. Wheeler
  • Gregory T. Jasion
  • Hesham Sakr

Ian Davidson often publishes in venues such as:

  • arXiv (Cornell University)
  • Optics Express
  • Conference on Lasers and Electro-Optics
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Optica Advanced Photonics Congress 2022

Best Publications

  • Constrained Clustering: Advances in Algorithms, Theory, and Applications

    Sugato Basu;Ian Davidson;Kiri Wagstaff

  • Clustering with Constraints: Feasibility Issues and the k-Means Algorithm.

    Ian Davidson;S. S. Ravi

  • Agglomerative hierarchical clustering with constraints: theoretical and empirical results

    Ian Davidson;S. S. Ravi

  • Measuring constraint-set utility for partitional clustering algorithms

    Ian Davidson;Kiri L. Wagstaff;Sugato Basu

  • Flexible constrained spectral clustering

    Xiang Wang;Ian Davidson

  • Visual Data Mining : Techniques and Tools for Data Visualization and Mining

    Tom Soukup;Ian Davidson

  • Multisource domain adaptation and its application to early detection of fatigue

    Rita Chattopadhyay;Qian Sun;Wei Fan;Ian Davidson

  • On constrained spectral clustering and its applications

    Xiang Wang;Buyue Qian;Ian Davidson

  • Knowledge Discovery and Data Mining: Challenges and Realities

    Xingquan Zhu;Ian Davidson

  • Batch Mode Active Sampling Based on Marginal Probability Distribution Matching

    Rita Chattopadhyay;Zheng Wang;Wei Fan;Ian Davidson

  • Accelerating Online CP Decompositions for Higher Order Tensors

    Shuo Zhou;Nguyen Xuan Vinh;James Bailey;Yunzhe Jia

  • A principled and flexible framework for finding alternative clusterings

    ZiJie Qi;Ian Davidson

  • An improved categorization of classifier's sensitivity on sample selection bias

    Wei Fan;I. Davidson;B. Zadrozny;P.S. Yu

  • Finding Alternative Clusterings Using Constraints

    I. Davidson;Zijie Qi

  • Network discovery via constrained tensor analysis of fMRI data

    Ian Davidson;Sean Gilpin;Owen Carmichael;Peter Walker

  • Using instance-level constraints in agglomerative hierarchical clustering: theoretical and empirical results

    Ian Davidson;S. S. Ravi

  • Active Spectral Clustering

    Xiang Wang;Ian Davidson

  • When is constrained clustering beneficial, and why?

    Kiri L. Wagstaff;Sugato Basu;Ian Davidson

  • Guided learning for role discovery (GLRD): framework, algorithms, and applications

    Sean Gilpin;Tina Eliassi-Rad;Ian Davidson

  • Intractability and clustering with constraints

    Ian Davidson;S. S. Ravi

  • Joint Transfer and Batch-mode Active Learning

    Rita Chattopadhyay;Wei Fan;Ian Davidson;Sethuraman Panchanathan

Frequent Co-Authors

S. S. Ravi
S. S. Ravi University of Virginia
Wei Fan
Wei Fan Tencent (China)
Jieping Ye
Jieping Ye Alibaba Group (China)
Sethuraman Panchanathan
Sethuraman Panchanathan Arizona State University
Siegfried Nijssen
Siegfried Nijssen Université Catholique de Louvain
Owen Carmichael
Owen Carmichael Pennington Biomedical Research Center
Kiri L. Wagstaff
Kiri L. Wagstaff Oregon State University
Shuiwang Ji
Shuiwang Ji Texas A&M University
James Bailey
James Bailey University of Melbourne
Naren Ramakrishnan
Naren Ramakrishnan Virginia Tech

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