World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
10035
World Ranking
11903
National Ranking
1478

Felipe Cucker 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 Felipe Cucker 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 201 publications — 47th percentile

47% of scientists in this discipline score the same or lower.

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

Felipe Cucker 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 Felipe Cucker sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Felipe Cucker is affiliated with the City University of Hong Kong in China. Their research spans multiple intersecting areas within computer science and mathematics, focusing particularly on computational theory and mathematics.

The main fields of study in Cucker's work include:

  • Computer Science
  • Mathematics

Within these fields, key subfields addressed are:

  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Algebra and Number Theory
  • Computational Mechanics
  • Mathematical Physics

Cucker's research covers a range of specialized topics such as:

  • Polynomial and algebraic computation
  • Topological and Geometric Data Analysis
  • Commutative Algebra and Its Applications
  • Advanced Numerical Analysis Techniques
  • Machine Learning and Algorithms
  • Homotopy and Cohomology in Algebraic Topology
  • Numerical Methods and Algorithms

Several recent papers authored or co-authored by Cucker illustrate the scope and progression of their scientific contributions:

  • Functional norms, condition numbers and numerical algorithms in algebraic geometry, 2022, Forum of Mathematics Sigma
  • On the Complexity of the Plantinga-Vegter Algorithm, 2022, Discrete & Computational Geometry
  • Computing the Homology of Semialgebraic Sets. II: General Formulas, 2021, Foundations of Computational Mathematics
  • Smale 17th Problem: Advances and Open Directions, 2021, New Zealand journal of mathematics
  • On the Complexity of the Plantinga-Vegter Algorithm, 2020, arXiv (Cornell University)

Cucker has frequently published in the following venues:

  • arXiv (Cornell University)
  • Foundations of Computational Mathematics
  • Forum of Mathematics Sigma
  • Discrete & Computational Geometry
  • New Zealand journal of mathematics

Cucker's collaborative network includes multiple frequent co-authors, specifically:

  • Josué Tonelli-Cueto
  • Alperen A. Ergür
  • Peter Bürgisser
  • Alexander Bastounis
  • Anders C. Hansen

Best Publications

  • On the mathematical foundations of learning

    Felipe Cucker;Steve Smale;Steve Smale

  • Complexity and Real Computation

    Lenore Blum;Felipe Cucker;Michael Shub;Steve Smale

  • Learning Theory: An Approximation Theory Viewpoint

    Felipe Cucker;Ding Xuan Zhou

  • On the mathematics of emergence

    Felipe Cucker;Steve Smale

  • Learning Theory

    Unknown

  • Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem

    Felipe Cucker;Steve Smale

  • Learning Theory: An Approximation Theory Viewpoint (Cambridge Monographs on Applied & Computational Mathematics)

    Felipe Cucker;Ding Xuan Zhou

  • Condition : The Geometry of Numerical Algorithms

    Peter Brgisser;Felipe Cucker

  • A General Collision-Avoiding Flocking Framework

    F Cucker;Jiu-Gang Dong

  • Computational Complexity

    Unknown

  • Modeling Language Evolution

    Felipe Cucker;Steve Smale;Ding-Xuan Zhou

  • On a problem posed by Steve Smale

    Peter Bürgisser;Felipe Cucker

  • On mixed and componentwise condition numbers for Moore–Penrose inverse and linear least squares problems

    Felipe Cucker;Huaian Diao;Yimin Wei

  • A new condition number for linear programming

    Dennis Cheung;Felipe Cucker

  • Complexity estimates depending on condition and round-off error

    Felipe Cucker;Steve Smale

  • Flocking with informed agents

    Felipe Cucker;Cristián Huepe

  • ON THE CRITICAL EXPONENT FOR FLOCKS UNDER HIERARCHICAL LEADERSHIP

    Felipe Cucker;Jiu-Gang Dong

  • A Polynomial Time Algorithm for Diophantine Equations in One Variable

    F Cucker;P Koiran;S Smale

  • Counting complexity classes for numeric computations II: Algebraic and semialgebraic sets

    Peter Bürgisser;Felipe Cucker

  • On digital nondeterminism

    Felipe Cucker;Martín Matamala

  • SIAM Journal on Optimization

    C Audet;H H Bauschke;L T Biegler;P L Combettes

  • Learning Theory: An Approximation Theory Viewpoint: Support vector machines for classification 157

    Felipe Cucker;Ding Xuan Zhou

  • Foundations of Computational Mathematics

    Felipe Cucker;Michael Shub

  • Learning Theory: An Approximation Theory Viewpoint: The framework of learning

    Felipe Cucker;Ding Xuan Zhou

  • Learning Theory: An Approximation Theory Viewpoint: Least squares regularization

    Felipe Cucker;Ding Xuan Zhou

Frequent Co-Authors

Michael J. Todd
Michael J. Todd Cornell University
Endre Süli
Endre Süli University of Oxford
Marek Karpinski
Marek Karpinski University of Bonn
Charles Audet
Charles Audet Polytechnique Montréal
Jorge Nocedal
Jorge Nocedal Northwestern University
Michael L. Overton
Michael L. Overton Courant Institute of Mathematical Sciences
Masao Fukushima
Masao Fukushima Kyoto University
Nicholas I. M. Gould
Nicholas I. M. Gould University of Oxford
Patrick L. Combettes
Patrick L. Combettes North Carolina State University
Lorenz T. Biegler
Lorenz T. Biegler Carnegie Mellon University

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