World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
21005
World Ranking
6645
National Ranking
2935

Dana Angluin 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 Dana Angluin 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: 113 publications — 12th percentile

12% of scientists in this discipline score the same or lower.

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

Dana Angluin 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 Dana Angluin 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: 46 D-Index — 53rd percentile

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

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

Overview

Dana Angluin is affiliated with Yale University in the United States and has an active research profile primarily in computer science, with a focus on artificial intelligence, computational theory, and electrical engineering. Their work intersects multiple subfields, ranging from automata theory to machine learning and natural language processing techniques.

Their research topics include:

  • Semigroups and automata theory
  • Machine learning and algorithms
  • Ferroelectric and negative capacitance devices
  • Natural language processing techniques
  • Topic modeling
  • DNA and biological computing
  • Formal methods in verification

Several recent papers authored or coauthored by Dana Angluin span prominent publication venues. These include:

  • "Regular ω-languages with an informative right congruence" (2020), published in Information and Computation
  • "Strongly Unambiguous Büchi Automata Are Polynomially Predictable With Membership Queries" (2020), published by Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • "Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity" (2022), published in Transactions of the Association for Computational Linguistics
  • "What Formal Languages Can Transformers Express? A Survey" (2023), pre-published on arXiv (Cornell University)
  • "What Formal Languages Can Transformers Express? A Survey" (2024), published in Transactions of the Association for Computational Linguistics

Their frequent coauthors include Dana Fisman, Lena Strobl, David Chiang, Robert Frank, and Yiding Hao. The collaboration network shows repeated partnerships, notably with Dana Fisman and Lena Strobl, indicating ongoing research in shared topics.

Dana Angluin's publications frequently appear in the following venues:

  • arXiv (Cornell University)
  • Transactions of the Association for Computational Linguistics
  • Information and Computation
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Logical Methods in Computer Science

Their broader scholarly work spans 30 publications in computer science, with significant contributions to artificial intelligence and computational theory. Their interdisciplinary approach also connects with molecular biology and computer vision, reflecting a diverse research portfolio.

Best Publications

  • Learning regular sets from queries and counterexamples

    Dana Angluin

  • Queries and Concept Learning

    Dana Angluin

  • Inductive Inference: Theory and Methods

    Dana Angluin;Carl H. Smith

  • Inductive inference of formal languages from positive data

    Dana Angluin

  • Learning From Noisy Examples

    Dana Angluin;Philip Laird

  • Fast probabilistic algorithms for hamiltonian circuits and matchings

    Dana Angluin;Leslie G. Valiant

  • Finding patterns common to a set of strings

    Dana Angluin

  • Local and global properties in networks of processors (Extended Abstract)

    Dana Angluin

  • Inference of Reversible Languages

    Dana Angluin

  • Computation in networks of passively mobile finite-state sensors

    Dana Angluin;James Aspnes;Zoë Diamadi;Michael J. Fischer

  • Queries and Concept Learning

    Unknown

  • Two notions of correctness and their relation to testing

    Timothy A. Budd;Dana Angluin

  • Negative Results for Equivalence Queries

    Dana Angluin

  • The computational power of population protocols

    Dana Angluin;James Aspnes;David Eisenstat;Eric Ruppert

  • On the complexity of minimum inference of regular sets

    Dana Angluin

  • Learning Conjunctions of Horn Clauses

    Dana Angluin;Michael Frazier;Leonard Pitt

  • Computational learning theory: survey and selected bibliography

    Dana Angluin

  • Fast computation by population protocols with a leader

    Dana Angluin;James Aspnes;David Eisenstat

  • When won't membership queries help?

    Dana Angluin;Michael Kharitonov

  • Learning read-once formulas with queries

    Dana Angluin;Lisa Hellerstein;Marek Karpinski

  • A simple population protocol for fast robust approximate majority

    Dana Angluin;James Aspnes;David Eisenstat

  • Learning from noisy examples

    Unknown

Frequent Co-Authors

James Aspnes
James Aspnes Yale University
Michael J. Fischer
Michael J. Fischer Yale University
Leonard Pitt
Leonard Pitt University of Illinois at Urbana-Champaign
Christos H. Papadimitriou
Christos H. Papadimitriou Columbia University
Carl Smith
Carl Smith University of Łódź
Joseph M. Hellerstein
Joseph M. Hellerstein University of California, Berkeley
Leslie G. Valiant
Leslie G. Valiant Harvard University
Marek Karpinski
Marek Karpinski University of Bonn
Thomas Dean
Thomas Dean Google (United States)

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