World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
3882
World Ranking
13697
National Ranking
51

Francesco Orabona 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 Francesco Orabona 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: 138 publications — 22nd percentile

22% of scientists in this discipline score the same or lower.

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

Francesco Orabona 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 Francesco Orabona 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

Francesco Orabona is affiliated with King Abdullah University of Science and Technology in Saudi Arabia. Their scholarly contributions span multiple fields within computer science and decision sciences, contributing to a broad spectrum of topics related to machine learning and optimization.

The scientist's main fields of study include:

  • Computer Science
  • Decision Sciences

Within these fields, their research covers several subfields, such as:

  • Artificial Intelligence
  • Management Science and Operations Research
  • Computational Mechanics
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

The principal topics Francesco Orabona works on comprise:

  • Advanced Bandit Algorithms Research
  • Stochastic Gradient Optimization Techniques
  • Machine Learning and Algorithms
  • Sparse and Compressive Sensing Techniques
  • Neural Networks and Applications
  • Reinforcement Learning in Robotics
  • Optimization and Search Problems

Among the recent publications authored or co-authored by Francesco Orabona are:

  • Understanding AdamW through Proximal Methods and Scale-Freeness, 2022, arXiv (Cornell University)
  • Online Learning Algorithms, 2020, Annual Review of Statistics and Its Application
  • A High Probability Analysis of Adaptive SGD with Momentum, 2020, arXiv (Cornell University)
  • Adam$^+$: A Stochastic Method with Adaptive Variance Reduction, 2020, arXiv (Cornell University)
  • A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance, 2020, arXiv (Cornell University)

Frequent coauthors include:

  • Mingrui Liu
  • Zhenxun Zhuang
  • Ashok Cutkosky
  • Xiaoyu Li
  • Nicolò Campolongo

The venues where this scientist has frequently published include:

  • arXiv (Cornell University)
  • Annual Review of Statistics and Its Application
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Harvard Dataverse
  • Università degli Studi di Urbino

Best Publications

  • Safety in numbers: Learning categories from few examples with multi model knowledge transfer

    Tatiana Tommasi;Francesco Orabona;Barbara Caputo

  • Learning Categories From Few Examples With Multi Model Knowledge Transfer

    Tatiana Tommasi;Francesco Orabona;Barbara Caputo

  • On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes

    Xiaoyu Li;Francesco Orabona

  • From N to N+1: Multiclass Transfer Incremental Learning

    Ilja Kuzborskij;Francesco Orabona;Barbara Caputo

  • Object-based Visual Attention: a Model for a Behaving Robot

    F. Orabona;G. Metta;G. Sandini

  • Stability and Hypothesis Transfer Learning

    Ilja Kuzborskij;Ilja Kuzborskij;Francesco Orabona

  • Learning from Candidate Labeling Sets

    Jie Luo;Francesco Orabona

  • The projectron: a bounded kernel-based Perceptron

    Francesco Orabona;Joseph Keshet;Barbara Caputo

  • Momentum-Based Variance Reduction in Non-Convex SGD

    Ashok Cutkosky;Francesco Orabona

  • Model adaptation with least-squares SVM for adaptive hand prosthetics

    Francesco Orabona;Claudio Castellini;Barbara Caputo;Angelo Emanuele Fiorilla

  • Bounded Kernel-Based Online Learning

    Francesco Orabona;Joseph Keshet;Barbara Caputo

  • A Modern Introduction to Online Learning.

    Francesco Orabona

  • Discriminative cue integration for medical image annotation

    Tatiana Tommasi;Francesco Orabona;Barbara Caputo

  • Improving Control of Dexterous Hand Prostheses Using Adaptive Learning

    T. Tommasi;F. Orabona;C. Castellini;B. Caputo

  • Online-batch strongly convex Multi Kernel Learning

    Francesco Orabona;Luo Jie;Barbara Caputo

  • A generalized online mirror descent with applications to classification and regression

    Francesco Orabona;Koby Crammer;Nicolò Cesa-Bianchi

  • Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning

    Francesco Orabona

  • Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

    Francesco Orabona;Luo Jie;Luo Jie

  • On-line independent support vector machines

    Francesco Orabona;Claudio Castellini;Barbara Caputo;Luo Jie

  • Robust bounds for classification via selective sampling

    Nicolò Cesa-Bianchi;Claudio Gentile;Francesco Orabona

  • A Proto-object Based Visual Attention Model

    Francesco Orabona;Giorgio Metta;Giulio Sandini

  • Scale-free online learning

    Francesco Orabona;Dávid Pál

  • Improved Strongly Adaptive Online Learning using Coin Betting

    Kwang-Sung Jun;Francesco Orabona;Rebecca Willett;Stephen J. Wright

Frequent Co-Authors

Barbara Caputo
Barbara Caputo Polytechnic University of Turin
Giulio Sandini
Giulio Sandini Italian Institute of Technology
Giorgio Metta
Giorgio Metta Italian Institute of Technology
Claudio Castellini
Claudio Castellini University of Erlangen-Nuremberg
Nicolò Cesa-Bianchi
Nicolò Cesa-Bianchi University of Milan
Claudio Gentile
Claudio Gentile Google (United States)
Rebecca Willett
Rebecca Willett University of Chicago
Lorenzo Natale
Lorenzo Natale Italian Institute of Technology
Frank W. Ohl
Frank W. Ohl Leibniz Institute for Neurobiology

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