World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
4575
World Ranking
13576
National Ranking
5420

Maya R. Gupta 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 Maya R. Gupta 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: 127 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.

Maya R. Gupta 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 Maya R. Gupta 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: 31 D-Index — 6th percentile

6% of scientists in this discipline score the same or lower.

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

Overview

Maya R. Gupta is affiliated with Google in the United States and has contributed extensively to the field of computer science, particularly focusing on artificial intelligence and related subfields. Their research encompasses a variety of topics including machine learning, ethics in AI, algorithm development, and advanced optimization techniques.

Their recent published papers include:

  • Pairwise Fairness for Ranking and Regression (2020), Proceedings of the AAAI Conference on Artificial Intelligence
  • Robust Optimization for Fairness with Noisy Protected Groups (2020), arXiv (Cornell University)
  • Fast Linear Interpolation (2021), ACM Journal on Emerging Technologies in Computing Systems
  • Barriers to and facilitators of pet grooming among clients served by a subsidized grooming service program (2022), Frontiers in Veterinary Science
  • Deontological Ethics By Monotonicity Shape Constraints (2020), arXiv (Cornell University)

Their frequent collaborators reflect a range of coauthorship in related areas and include:

  • Harikrishna Narasimhan
  • Serena Wang
  • Andrew Cotter
  • Kevin Robert Canini

Gupta's work has appeared primarily in these venues:

  • arXiv (Cornell University)
  • ACM Journal on Emerging Technologies in Computing Systems
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Frontiers in Veterinary Science
  • Bioinformatics

The scientist's main field of study is computer science, with a focus on several subfields:

  • Artificial Intelligence
  • Safety Research
  • Immunology
  • Molecular Biology
  • Management Science and Operations Research

Their research topics cover a broad spectrum of machine learning and ethical implications of AI technologies, including:

  • Machine Learning and Data Classification
  • Ethics and Social Impacts of AI
  • Machine Learning and Algorithms
  • Advanced Bandit Algorithms Research
  • Bayesian Modeling and Causal Inference
  • Machine Learning in Materials Science
  • Medical Imaging Techniques and Applications

Best Publications

  • Recent advances in terahertz imaging

    D.M. Mittleman;M. Gupta;R. Neelamani;R.G. Baraniuk

  • Similarity-based Classification: Concepts and Algorithms

    Yihua Chen;Eric K. Garcia;Maya R. Gupta;Ali Rahimi

  • Theory and Use of the Em Algorithm

    Maya R. Gupta;Yihua Chen

  • To Trust Or Not To Trust A Classifier

    Heinrich Jiang;Been Kim;Melody Y. Guan;Maya R. Gupta

  • OCR binarization and image pre-processing for searching historical documents

    Maya R. Gupta;Nathaniel P. Jacobson;Eric K. Garcia

  • Bayesian Quadratic Discriminant Analysis

    Santosh Srivastava;Maya R. Gupta;Béla A. Frigyik

  • Design goals and solutions for display of hyperspectral images

    N.P. Jacobson;M.R. Gupta

  • How to Analyze Paired Comparison Data

    Kristi Tsukida;Maya R. Gupta

  • Satisfying real-world goals with dataset constraints

    Gabriel Goh;Andrew Cotter;Maya Gupta;Michael Friedlander

  • Monotonic calibrated interpolated look-up tables

    Maya Gupta;Andrew Cotter;Jan Pfeifer;Konstantin Voevodski

  • To Trust Or Not To Trust A Classifier.

    Heinrich Jiang;Been Kim;Melody Y. Guan;Maya Gupta

  • Training highly multiclass classifiers

    Maya R. Gupta;Samy Bengio;Jason Weston

  • Deep Lattice Networks and Partial Monotonic Functions

    Seungil You;David Ding;Kevin Robert Canini;Jan Pfeifer

  • Functional Bregman Divergence and Bayesian Estimation of Distributions

    B.A. Frigyik;S. Srivastava;M.R. Gupta

  • Linear Fusion of Image Sets for Display

    N.P. Jacobson;M.R. Gupta;J.B. Cole

  • Pairwise Fairness for Ranking and Regression.

    Harikrishna Narasimhan;Andy Cotter;Maya Gupta;Serena Lutong Wang

  • Learning kernels from indefinite similarities

    Yihua Chen;Maya R. Gupta;Benjamin Recht

  • Completely Lazy Learning

    Eric K Garcia;Sergey Feldman;Maya R Gupta;Santosh Srivastava

  • Satisfying Real-world Goals with Dataset Constraints

    Gabriel Goh;Andrew Cotter;Maya Gupta;Michael Friedlander

  • Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints

    Andrew Cotter;Maya R. Gupta;Heinrich Jiang;Nathan Srebro

  • Wavelet Principal Component Analysis and its Application to Hyperspectral Images

    Maya Gupta;Nathaniel Jacobson

  • Adaptive Local Linear Regression With Application to Printer Color Management

    M.R. Gupta;E.K. Garcia;E. Chin

  • Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals

    Andrew Cotter;Heinrich Jiang;Serena Wang;Taman Narayan

Frequent Co-Authors

Robert M. Gray
Robert M. Gray Stanford University
Karthik Sridharan
Karthik Sridharan Cornell University
Samy Bengio
Samy Bengio Apple (United States)
Been Kim
Been Kim Google (United States)
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Anna C. Gilbert
Anna C. Gilbert Yale University
Jason Weston
Jason Weston Facebook (United States)
Mari Ostendorf
Mari Ostendorf University of Washington
Richard G. Baraniuk
Richard G. Baraniuk Rice University
Daniel M. Mittleman
Daniel M. Mittleman Brown University

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