World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
59227
World Ranking
2506
National Ranking
1250

Ron Kohavi 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 Ron Kohavi 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: 126 publications — 17th percentile

17% of scientists in this discipline score the same or lower.

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

Ron Kohavi 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 Ron Kohavi 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: 64 D-Index — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Overview

Ron Kohavi is affiliated with Microsoft in the United States and specializes in several areas within mathematics, decision sciences, and computer science. Their research spans subfields such as statistics and probability, artificial intelligence, statistics, probability and uncertainty, management science and operations research, and information systems and management.

The scientist's main research topics include:

  • Statistical Methods in Clinical Trials
  • Advanced Causal Inference Techniques
  • Statistical Methods and Inference
  • Scientific Computing and Data Management
  • Advanced Statistical Process Monitoring
  • Big Data and Business Intelligence
  • Statistical Methods and Bayesian Inference

Kohavi has coauthored extensively with colleagues such as Diane Tang and Ya Xu, each with 32 joint publications, followed by Alex Deng with 3, and Nicholas Larsen and Jonathan W. Stallrich with 2 coauthored works each.

Their recent publications include:

  • "Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology" (2023) published in The American Statistician
  • "Online randomized controlled experiments at scale: lessons and extensions to medicine" (2020) published in Trials
  • "A/B Testing Intuition Busters" (2022) published in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • "Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology" (2022) published in arXiv (Cornell University)
  • "Index" (2020) published by Cambridge University Press eBooks

Among their book publications, Kohavi has contributed to Cambridge University Press with the title "Trustworthy Online Controlled Experiments" (2020), which has accrued significant citations.

Frequent venues where this scientist publishes include The American Statistician, Trials, Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, arXiv, and Cambridge University Press eBooks.

Best Publications

  • A study of cross-validation and bootstrap for accuracy estimation and model selection

    Ron Kohavi

  • Wrappers for feature subset selection

    Ron Kohavi;George H. John

  • Irrelevant features and the subset selection problem

    George H. John;Ron Kohavi;Karl Pfleger

  • Supervised and unsupervised discretization of continuous features

    James Dougherty;Ron Kohavi;Mehran Sahami

  • E-Commerce Recommendation Applications

    J. Ben Schafer;Joseph A. Konstan;John Riedl

  • An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

    Eric Bauer;Ron Kohavi

  • Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid

    Ron Kohavi

  • The Case against Accuracy Estimation for Comparing Induction Algorithms

    Foster J. Provost;Tom Fawcett;Ron Kohavi

  • The power of decision tables

    Ron Kohavi

  • Bias plus variance decomposition for zero-one loss functions

    Ron Kohavi;David Wolpert

  • Controlled experiments on the web: survey and practical guide

    Ron Kohavi;Roger Longbotham;Dan Sommerfield;Randal M. Henne

  • Real world performance of association rule algorithms

    Zijian Zheng;Ron Kohavi;Llew Mason

  • Data Mining Using MLC a Machine Learning Library in C

    Ron Kohavi;Dan Sommerfield;James Dougherty

  • Practical guide to controlled experiments on the web: listen to your customers not to the hippo

    Ron Kohavi;Randal M. Henne;Dan Sommerfield

  • Feature subset selection using the wrapper method: overfltting and dynamic search space topology

    Ron Kohavi;Dan Sommerfield

  • Emerging trends in business analytics

    Ron Kohavi;Neal J. Rothleder;Evangelos Simoudis

  • Wrappers for Performance Enhancements and Oblivious Decision Graphs.

    Ron Kohavi

  • Error-based and entropy-based discretization of continuous features

    Ron Kohavi;Mehran Sahami

  • Online controlled experiments at large scale

    Ron Kohavi;Alex Deng;Brian Frasca;Toby Walker

  • Guest Editors‘ Introduction: On Applied Research in MachineLearning

    Foster Provost;Ron Kohavi

  • Data mining using /spl Mscr//spl Lscr//spl Cscr/++ a machine learning library in C++

    R. Kohavi;D. Sommerfield;J. Dougherty

  • Feature Selection for Knowledge Discovery and Data Mining

    Ron Kohavi;George John

Frequent Co-Authors

Foster Provost
Foster Provost New York University
Jaideep Srivastava
Jaideep Srivastava University of Minnesota
Myra Spiliopoulou
Myra Spiliopoulou Otto-von-Guericke University Magdeburg
Carla E. Brodley
Carla E. Brodley Northeastern University
Mehran Sahami
Mehran Sahami Stanford University
Johannes Gehrke
Johannes Gehrke Microsoft (United States)
William DuMouchel
William DuMouchel Oracle (United States)
Pat Langley
Pat Langley Stanford University
David H. Wolpert
David H. Wolpert Santa Fe Institute
Nir Friedman
Nir Friedman Weizmann Institute of Science

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