World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
8197
World Ranking
8325
National Ranking
3572

Lance Kaplan 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 Lance Kaplan 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: 274 publications — 68th percentile

68% of scientists in this discipline score the same or lower.

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

Lance Kaplan 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 Lance Kaplan 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: 42 D-Index — 43rd percentile

43% 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

  • 2016 - IEEE Fellow For contributions to signal processing and information fusion for situational awareness

Overview

Lance Kaplan is affiliated with the United States Army Research Laboratory in the United States. Their research primarily focuses on the field of Computer Science, with a significant body of work in Artificial Intelligence, as well as contributions to Signal Processing, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, and Management Science and Operations Research.

Their work includes exploration in several core topics such as:

  • Bayesian Modeling and Causal Inference
  • Explainable Artificial Intelligence (XAI)
  • Anomaly Detection Techniques and Applications
  • Adversarial Robustness in Machine Learning
  • Logic, Reasoning, and Knowledge
  • Time Series Analysis and Forecasting
  • Complex Network Analysis Techniques

Lance Kaplan has published extensively in reputable venues, including:

  • IEEE Aerospace and Electronic Systems Magazine
  • arXiv (Cornell University)
  • IEEE Transactions on Aerospace and Electronic Systems
  • IEEE Transactions on Signal Processing
  • Expert Systems with Applications

Frequent collaborators have included:

  • Daniel O'hagan
  • Michael Rice
  • Mary Randall
  • Dawn Melley
  • Kevin Lisankie

Representative recent publications by Lance Kaplan are:

  • "Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI," 2020, Patterns
  • "Uncertainty-Aware Deep Classifiers Using Generative Models," 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Efficient modeling of higher-order dependencies in networks: from algorithm to application for anomaly detection," 2020, EPJ Data Science
  • "Non-Bayesian Social Learning With Uncertain Models," 2020, IEEE Transactions on Signal Processing
  • "A survey on uncertainty reasoning and quantification in belief theory and its application to deep learning," 2023, Information Fusion

In recognition of contributions to their fields, Lance Kaplan was awarded the IEEE Fellow distinction in 2016 for work related to signal processing and information fusion for situational awareness.

Best Publications

  • Evidential Deep Learning to Quantify Classification Uncertainty

    Murat Sensoy;Lance M. Kaplan;Melih Kandemir

  • TOPS: new DOA estimator for wideband signals

    Yeo-Sun Yoon;L.M. Kaplan;J.H. McClellan

  • On truth discovery in social sensing: a maximum likelihood estimation approach

    Dong Wang;Lance Kaplan;Hieu Le;Tarek Abdelzaher

  • Extended fractal analysis for texture classification and segmentation

    L.M. Kaplan

  • Global node selection for localization in a distributed sensor network

    L.M. Kaplan

  • Using humans as sensors: an estimation-theoretic perspective

    Dong Wang;Tanvir Amin;Shen Li;Tarek Abdelzaher

  • Maximum likelihood methods for bearings-only target localization

    L.M. Kaplan;Qiang Le;N. Molnar

  • Improved SAR target detection via extended fractal features

    L.M. Kaplan

  • The Age of Social Sensing

    Dong Wang;Boleslaw K. Szymanski;Tarek Abdelzaher;Heng Ji

  • Performance Measures of Covariance and Information Matrices in Resource Management for Target State Estimation

    Chun Yang;L. Kaplan;E. Blasch

  • GeoBurst: Real-Time Local Event Detection in Geo-Tagged Tweet Streams

    Chao Zhang;Guangyu Zhou;Quan Yuan;Honglei Zhuang

  • On the quality and value of information in sensor networks

    Chatschik Bisdikian;Lance M. Kaplan;Mani B. Srivastava

  • Social Sensing: Building Reliable Systems on Unreliable Data

    Dong Wang;Tarek Abdelzaher;Lance Kaplan

  • Fractal estimation from noisy data via discrete fractional Gaussian noise (DFGN) and the Haar basis

    L.M. Kaplan;C.-C.J. Kuo

  • Recursive Fact-Finding: A Streaming Approach to Truth Estimation in Crowdsourcing Applications

    Dong Wang;Tarek Abdelzaher;Lance Kaplan;Charu C. Aggarwal

  • Assignment costs for multiple sensor track-to-track association

    L. Kaplan;Y. Bar-Shalom;W. Blair

  • Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks

    Jingbo Shang;Meng Qu;Jialu Liu;Lance M. Kaplan

  • Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI.

    Richard Tomsett;Alun D. Preece;Dave Braines;Federico Cerutti;Federico Cerutti

  • Texture roughness analysis and synthesis via extended self-similar (ESS) model

    L.M. Kaplan;C.-C.J. Kuo

  • Extending self-similarity for fractional Brownian motion

    L.M. Kaplan;C.-C.J. Kuo

  • Building principles for a quality of information specification for sensor information

    Chatschik Bisdikian;Lance M. Kaplan;Mani B. Srivastava;David J. Thornley

Frequent Co-Authors

Tarek Abdelzaher
Tarek Abdelzaher University of Illinois at Urbana-Champaign
Dong Wang
Dong Wang Peking University
Jiawei Han
Jiawei Han University of Illinois at Urbana-Champaign
Mani Srivastava
Mani Srivastava University of California, Los Angeles
James H. McClellan
James H. McClellan Georgia Institute of Technology
Chatschik Bisdikian
Chatschik Bisdikian IBM (United States)
Alun Preece
Alun Preece Cardiff University
Rick S. Blum
Rick S. Blum Lehigh University
Lu Su
Lu Su Purdue University West Lafayette
Timothy J. Norman
Timothy J. Norman University of Southampton

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