World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
67
Citations
32282
World Ranking
2140
National Ranking
1076

Foster Provost 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 Foster Provost 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: 218 publications — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Foster Provost 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 Foster Provost 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: 67 D-Index — 85th percentile

85% of scientists in this discipline score the same or lower.

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

Overview

Foster Provost is affiliated with New York University in the United States. Their research primarily spans the fields of Mathematics and Computer Science, with a total of 14 and 13 publications respectively. Subfields of focus include Statistics and Probability, Artificial Intelligence, Management Science and Operations Research, Sociology and Political Science, and Information Systems.

Their work addresses several key topics such as Advanced Causal Inference Techniques, Statistical Methods in Clinical Trials, Explainable Artificial Intelligence (XAI), Statistical Methods and Bayesian Inference, Bayesian Modeling and Causal Inference, Recommender Systems and Techniques, and Privacy, Security, and Data Protection.

Foster Provost has authored papers published across various venues, including:

  • arXiv (Cornell University)
  • INFORMS Journal on Data Science
  • Information Systems Research
  • Machine Learning
  • MIS Quarterly

Recent papers authored or coauthored by Foster Provost cover diverse topics and publication venues, such as:

  • Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach, 2020, arXiv (Cornell University)
  • Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach, 2022, MIS Quarterly
  • Causal Decision Making and Causal Effect Estimation Are Not the Same...and Why It Matters, 2022, INFORMS Journal on Data Science
  • A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation, 2022, Information Systems Research
  • Node classification over bipartite graphs through projection, 2020, Machine Learning

Frequent collaborators include:

  • Carlos Fernández-Loría
  • Jesse Anderton
  • Benjamin Carterette
  • Praveen Chandar
  • Xintian Han

Their research contributions cover methodological advancements in causal inference and explainability within AI systems, as well as applications in recommender systems and data-driven decision processes. The publications reflect an intersection of statistical and computational methods applied to operational and societal challenges.

Best Publications

  • E-Commerce Recommendation Applications

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

  • DATA SCIENCE AND ITS RELATIONSHIP TO BIG DATA AND DATA-DRIVEN DECISION MAKING

    Foster J. Provost;Tom Fawcett

  • Robust Classification for Imprecise Environments

    Foster Provost;Tom Fawcett

  • Get another label? improving data quality and data mining using multiple, noisy labelers

    Victor S. Sheng;Foster Provost;Panagiotis G. Ipeirotis

  • Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

    Foster Provost;Tom Fawcett

  • Adaptive Fraud Detection

    Tom Fawcett;Foster Provost

  • Quality management on Amazon Mechanical Turk

    Panagiotis G. Ipeirotis;Foster Provost;Jing Wang

  • Learning when training data are costly: the effect of class distribution on tree induction

    Gary M. Weiss;Foster Provost

  • The Case against Accuracy Estimation for Comparing Induction Algorithms

    Foster J. Provost;Tom Fawcett;Ron Kohavi

  • Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions

    Foster Provost;Tom Fawcett

  • Network-Based Marketing: Identifying Likely Adopters Via Consumer Networks

    Shawndra Hill;Foster Provost;Chris Volinsky

  • Classification in Networked Data: A Toolkit and a Univariate Case Study

    Sofus A. Macskassy;Foster Provost

  • Tree Induction for Probability-Based Ranking

    Foster Provost;Pedro Domingos

  • Machine Learning from Imbalanced Data Sets 101

    Foster Provost

  • Activity monitoring: noticing interesting changes in behavior

    Tom Fawcett;Foster Provost

  • The effect of class distribution on classifier learning

    Gary M. Weiss;Foster Provost

  • The effect of class distribution on classifier learning: an empirical study

    Gary M. Weiss;Foster Provost

  • Efficient progressive sampling

    Foster Provost;David Jensen;Tim Oates

  • Tree induction vs. logistic regression: a learning-curve analysis

    Claudia Perlich;Foster Provost;Jeffrey S. Simonoff

  • Handling Missing Values when Applying Classification Models

    Maytal Saar-Tsechansky;Foster Provost

  • Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction

    F. Provost;G. M. Weiss

Frequent Co-Authors

David Martens
David Martens University of Antwerp
Panagiotis G. Ipeirotis
Panagiotis G. Ipeirotis New York University
Abraham Bernstein
Abraham Bernstein University of Zurich
Bruce G. Buchanan
Bruce G. Buchanan University of Pittsburgh
Rami Melhem
Rami Melhem University of Pittsburgh
Ron Kohavi
Ron Kohavi Microsoft (United States)
Arun Sundararajan
Arun Sundararajan New York University
Gary M. Weiss
Gary M. Weiss Fordham University
Raymond J. Mooney
Raymond J. Mooney The University of Texas at Austin

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