World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
10096
World Ranking
12376
National Ranking
5015

D. Sculley 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 D. Sculley 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: 57 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

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

D. Sculley 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 D. Sculley 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

D. Sculley is affiliated with Google in the United States and has contributed extensively to research at the intersection of computer science and medicine. Their work spans multiple fields of study, primarily focusing on artificial intelligence, molecular biology, and computational theory and mathematics.

The scientist's research covers diverse subfields, including:

  • Artificial Intelligence
  • Molecular Biology
  • Computational Theory and Mathematics
  • Ophthalmology
  • Spectroscopy

Their main topics of work address several specialized areas such as:

  • Genomics and Phylogenetic Studies
  • Machine Learning in Bioinformatics
  • Advanced Proteomics Techniques and Applications
  • Metaheuristic Optimization Algorithms Research
  • Evolutionary Algorithms and Applications
  • Advanced Multi-Objective Optimization Algorithms
  • DNA and Biological Computing

D. Sculley has been published in several venues, with a significant number of publications appearing on arXiv (Cornell University). Frequent publication venues include:

  • arXiv (Cornell University)
  • American Journal of Transplantation
  • Nature Biotechnology
  • The American Journal of Human Genetics

Notable recent papers authored or co-authored by D. Sculley include:

  • "Underspecification Presents Challenges for Credibility in Modern Machine Learning", 2020, arXiv (Cornell University)
  • "Using deep learning to annotate the protein universe", 2022, Nature Biotechnology
  • "Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift", 2020, arXiv (Cornell University)
  • "Population-Based Black-Box Optimization for Biological Sequence Design", 2020, arXiv (Cornell University)
  • "Best-Effort Lazy Evaluation for Python Software Built on APIs", 2021, arXiv (Cornell University)

D. Sculley frequently collaborates with several researchers, including:

  • Zachary Nado
  • Lucy J. Colwell
  • Dan Moldovan
  • Babak Alipanahi
  • Farhad Hormozdiari

The combination of computer science and medicine in D. Sculley's work is reflected by their contributions to both advanced algorithmic approaches and biological applications. This multidisciplinary involvement includes research on machine learning methods tailored to bioinformatics challenges, proteomics, and optimization algorithms applied to biological sequence design.

Best Publications

  • Web-scale k-means clustering

    D. Sculley

  • Hidden technical debt in Machine learning systems

    D. Sculley;Gary Holt;Daniel Golovin;Eugene Davydov

  • Ad click prediction: a view from the trenches

    H. Brendan McMahan;Gary Holt;D. Sculley;Michael Young

  • Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift

    Yaniv Ovadia;Emily Fertig;Jie Ren;Zachary Nado

  • Google Vizier: A Service for Black-Box Optimization

    Daniel Golovin;Benjamin Solnik;Subhodeep Moitra;Greg Kochanski

  • Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

    Yaniv Ovadia;Emily Fertig;Jie Ren;Zachary Nado

  • Underspecification Presents Challenges for Credibility in Modern Machine Learning

    Alexander D'Amour;Katherine A. Heller;Dan Moldovan;Ben Adlam

  • Relaxed online SVMs for spam filtering

    D. Sculley;Gabriel M. Wachman

  • Machine Learning: The High Interest Credit Card of Technical Debt

    D. Sculley;Gary Holt;Daniel Golovin;Eugene Davydov

  • The ML test score: A rubric for ML production readiness and technical debt reduction

    Eric Breck;Shanqing Cai;Eric Nielsen;Michael Salib

  • No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World

    Shreya Shankar;Yoni Halpern;Eric Breck;James Atwood

  • Combined regression and ranking

    D. Sculley

  • Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks

    Jennifer N. Wei;Jennifer N. Wei;David Belanger;Ryan P. Adams;D. Sculley

  • Fairness is not static: deeper understanding of long term fairness via simulation studies

    Alexander D'Amour;Hansa Srinivasan;James Atwood;Pallavi Baljekar

  • TensorFlow.js: Machine Learning for the Web and Beyond

    Daniel Smilkov;Nikhil Thorat;Yannick Assogba;Ann Yuan

  • Predicting bounce rates in sponsored search advertisements

    D. Sculley;Robert G. Malkin;Sugato Basu;Roberto J. Bayardo

  • Online Active Learning Methods for Fast Label-Efficient Spam Filtering.

    D. Sculley

  • Winner's Curse? On Pace, Progress, and Empirical Rigor.

    D. Sculley;Jasper Snoek;Alexander B. Wiltschko;Ali Rahimi

  • Compression and machine learning: a new perspective on feature space vectors

    D. Sculley;C.E. Brodley

  • TensorFlow.js: Machine Learning for the Web and Beyond

    Daniel Smilkov;Nikhil Thorat;Yannick Assogba;Ann Yuan

  • Direct-Manipulation Visualization of Deep Networks

    Daniel Smilkov;Shan Carter;D. Sculley;Fernanda B. Viégas

  • Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

    Zachary Nado;Shreyas Padhy;D. Sculley;Alexander D'Amour

  • Using Deep Learning to Annotate the Protein Universe

    Maxwell L Bileschi;David Belanger;Drew H Bryant;Theo Sanderson

Frequent Co-Authors

Jasper Snoek
Jasper Snoek Google (United States)
Balaji Lakshminarayanan
Balaji Lakshminarayanan Google (United States)
Carla E. Brodley
Carla E. Brodley Northeastern University
Martin Wattenberg
Martin Wattenberg Harvard University
Tiark Rompf
Tiark Rompf Purdue University West Lafayette
H. Brendan McMahan
H. Brendan McMahan Google (United States)
Ryan P. Adams
Ryan P. Adams Princeton University
Fernanda B. Viégas
Fernanda B. Viégas Harvard University
Sebastian Nowozin
Sebastian Nowozin Microsoft (United States)
Mark A. DePristo
Mark A. DePristo BigHat Biosciences

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