World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
9829
World Ranking
12863
National Ranking
198

Shaul Markovitch 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 Shaul Markovitch 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: 97 publications — 8th percentile

8% of scientists in this discipline score the same or lower.

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

Shaul Markovitch 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 Shaul Markovitch 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: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Overview

Shaul Markovitch is affiliated with the Technion - Israel Institute of Technology in Israel. Their research primarily focuses on computer science, with a significant emphasis on artificial intelligence among related subfields such as information systems, computer vision and pattern recognition, and information systems and management.

The scientist has contributed to several areas within computer science, including topic modeling, natural language processing techniques, software engineering research, explainable artificial intelligence (XAI), semantic web and ontologies, multimodal machine learning applications, and speech and dialogue systems.

Markovitch's recent papers include:

  • Concept-Based Approach to Word-Sense Disambiguation (2021), Proceedings of the AAAI Conference on Artificial Intelligence
  • Teaching Machines to Learn by Metaphors (2021), Proceedings of the AAAI Conference on Artificial Intelligence
  • Interpreting Embedding Spaces by Conceptualization (2022), arXiv (Cornell University)
  • A General Search-Based Framework for Generating Textual Counterfactual Explanations (2024), Proceedings of the AAAI Conference on Artificial Intelligence
  • Knowledge-Based Learning through Feature Generation (2020), arXiv (Cornell University)

Frequent co-authors working with Markovitch include Daniel Gilo, Ariel Raviv, Omer Levy, Adi Simhi, and Michal Badian. These collaborations have contributed to various publications across multiple venues.

Markovitch's work has been published mainly in the following venues:

  • arXiv (Cornell University) with 4 publications
  • Proceedings of the AAAI Conference on Artificial Intelligence with 3 publications

Best Publications

  • Computing semantic relatedness using Wikipedia-based explicit semantic analysis

    Evgeniy Gabrilovich;Shaul Markovitch

  • Overcoming the brittleness bottleneck using wikipedia: enhancing text categorization with encyclopedic knowledge

    Evgeniy Gabrilovich;Shaul Markovitch

  • A word at a time: computing word relatedness using temporal semantic analysis

    Kira Radinsky;Eugene Agichtein;Evgeniy Gabrilovich;Shaul Markovitch

  • Wikipedia-based semantic interpretation for natural language processing

    Evgeniy Gabrilovich;Shaul Markovitch

  • Contextual word similarity and estimation from sparse data

    Ido Dagan;Shaul Marcus;Shaul Markovitch

  • Concept-Based Information Retrieval Using Explicit Semantic Analysis

    Ofer Egozi;Shaul Markovitch;Evgeniy Gabrilovich

  • Selective Sampling for Nearest Neighbor Classifiers

    Michael Lindenbaum;Shaul Markovitch;Dmitry Rusakov

  • Feature generation for text categorization using world knowledge

    Evgeniy Gabrilovich;Shaul Markovitch

  • CONTEXTUAL WORD SIMILARITY AND ESTIMATION FROM SPARSE DATA

    Ido Dagan;Shaul Marcus;Shaul Markovitch

  • Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5

    Evgeniy Gabrilovich;Shaul Markovitch

  • Learning causality for news events prediction

    Kira Radinsky;Sagie Davidovich;Shaul Markovitch

  • The role of forgetting in learning

    Shaul Markovitch;Paul D. Scott

  • Learning models of intelligent agents

    David Carmel;Shaul Markovitch

  • Feature Generation Using General Constructor Functions

    Shaul Markovitch;Dan Rosenstein

  • Learning implicit transfer for person re-identification

    Tamar Avraham;Ilya Gurvich;Michael Lindenbaum;Shaul Markovitch

  • Opponent Modeling in Multi-Agent Systems

    David Carmel;Shaul Markovitch

  • Information filtering: selection mechanisms in learning systems

    Shaul Markovitch;Paul D. Scott

  • Parameterized generation of labeled datasets for text categorization based on a hierarchical directory

    Dmitry Davidov;Evgeniy Gabrilovich;Shaul Markovitch

  • Text categorization using external knowledge

    Evgeniy Gabrilovich;Shaul Markovitch

  • Concept-based feature generation and selection for information retrieval

    Ofer Egozi;Evgeniy Gabrilovich;Shaul Markovitch

  • Incorporating opponent models into adversary search

    David Carmel;Shaul Markovitch

Frequent Co-Authors

Evgeniy Gabrilovich
Evgeniy Gabrilovich Google (United States)
David Carmel
David Carmel Amazon (Israel)
Michael Lindenbaum
Michael Lindenbaum Technion – Israel Institute of Technology
Ehud Rivlin
Ehud Rivlin Technion – Israel Institute of Technology
Orna Grumberg
Orna Grumberg Technion – Israel Institute of Technology
Carmel Domshlak
Carmel Domshlak Technion – Israel Institute of Technology
Omer Levy
Omer Levy Deep Mind
Ido Dagan
Ido Dagan Bar-Ilan University
Yoav Goldberg
Yoav Goldberg Bar-Ilan University
Malte Helmert
Malte Helmert University of Basel

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