World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
63
Citations
19351
World Ranking
2719
National Ranking
1352

AnHai Doan 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 AnHai Doan 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: 168 publications — 34th percentile

34% of scientists in this discipline score the same or lower.

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

AnHai Doan 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 AnHai Doan 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: 63 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2007 - Fellow of Alfred P. Sloan Foundation

Overview

AnHai Doan is affiliated with the University of Wisconsin-Madison in the United States. Their research primarily focuses on various areas within computer science and decision sciences, with particular attention to data quality and management as well as privacy-preserving technologies in data.

The main fields of study for AnHai Doan include:

  • Computer Science
  • Decision Sciences

Their subfields of study emphasize:

  • Artificial Intelligence
  • Management Science and Operations Research
  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management

The scientist's research work covers a range of topics such as:

  • Data Quality and Management
  • Privacy-Preserving Technologies in Data
  • Topic Modeling
  • Advanced Database Systems and Queries
  • Data Mining Algorithms and Applications
  • Cloud Computing and Resource Management
  • Scientific Computing and Data Management

AnHai Doan has produced several papers published in notable venues. Some of the recent papers include:

  • Deep learning for blocking in entity matching, 2021, Proceedings of the VLDB Endowment
  • The Seattle Report on Database Research, 2020, ACM SIGMOD Record
  • The Seattle report on database research, 2022, Communications of the ACM
  • Magellan, 2020, Communications of the ACM
  • Sparkly: A Simple yet Surprisingly Strong TF/IDF Blocker for Entity Matching, 2023, Proceedings of the VLDB Endowment

Frequent co-authors who have collaborated with AnHai Doan include:

  • Derek J. Paulsen
  • Yash Govind
  • Magdalena Bałazińska
  • Surajit Chaudhuri
  • Joseph M. Hellerstein

The research outputs have appeared in various publication venues, with multiple contributions to:

  • Proceedings of the VLDB Endowment
  • ACM SIGMOD Record
  • Communications of the ACM
  • Movebank
  • The VLDB Journal

Among their professional recognitions, AnHai Doan was named a Fellow of the Alfred P. Sloan Foundation in 2007.

Best Publications

  • Crowdsourcing systems on the World-Wide Web

    Anhai Doan;Raghu Ramakrishnan;Alon Y. Halevy

  • Learning to map between ontologies on the semantic web

    AnHai Doan;Jayant Madhavan;Pedro Domingos;Alon Halevy

  • Reconciling schemas of disparate data sources: a machine-learning approach

    AnHai Doan;Pedro Domingos;Alon Y. Halevy

  • Principles of Data Integration

    AnHai Doan;Alon Halevy;Zachary Ives

  • Ontology Matching: A Machine Learning Approach

    AnHai Doan;Jayant Madhavan;Pedro M. Domingos;Alon Y. Halevy

  • Semantic-integration research in the database community: A brief survey

    AnHai Doan;Alon Y. Halevy

  • Learning to match ontologies on the Semantic Web

    AnHai Doan;Jayant Madhavan;Robin Dhamankar;Pedro Domingos

  • iMAP: discovering complex semantic matches between database schemas

    Robin Dhamankar;Yoonkyong Lee;AnHai Doan;Alon Halevy

  • Corpus-based schema matching

    J. Madhavan;P.A. Bernstein;A. Doan;A. Halevy

  • Deep Learning for Entity Matching: A Design Space Exploration

    Sidharth Mudgal;Han Li;Theodoros Rekatsinas;AnHai Doan

  • Deep entity matching with pre-trained language models

    Yuliang Li;Jinfeng Li;Yoshihiko Suhara;AnHai Doan

  • An interactive clustering-based approach to integrating source query interfaces on the deep Web

    Wensheng Wu;Clement Yu;AnHai Doan;Weiyi Meng

  • Learning to Match the Schemas of Data Sources: A Multistrategy Approach

    Anhai Doan;Pedro Domingos;Alon Halevy

  • The Claremont report on database research

    Rakesh Agrawal;Anastasia Ailamaki;Philip A. Bernstein;Eric A. Brewer

  • Corleone: hands-off crowdsourcing for entity matching

    Chaitanya Gokhale;Sanjib Das;AnHai Doan;Jeffrey F. Naughton

  • Semantic-integration research in the database community

    AnHai Doan;Alon Y. Halevy

  • Privacy-preserving data integration and sharing

    Chris Clifton;Murat Kantarcioǧlu;AnHai Doan;Gunther Schadow

  • Learning Source Description for Data Integration.

    AnHai Doan;Pedro M. Domingos;Alon Y. Levy

  • Declarative information extraction using datalog with embedded extraction predicates

    Warren Shen;AnHai Doan;Jeffrey F. Naughton;Raghu Ramakrishnan

  • Tuffy: scaling up statistical inference in Markov logic networks using an RDBMS

    Feng Niu;Christopher Ré;AnHai Doan;Jude Shavlik

  • Magellan: toward building entity matching management systems

    Pradap Konda;Sanjib Das;G C Paul Suganthan;AnHai Doan

Frequent Co-Authors

Alon Halevy
Alon Halevy Facebook (United States)
Raghu Ramakrishnan
Raghu Ramakrishnan Microsoft (United States)
Jeffrey F. Naughton
Jeffrey F. Naughton University of Wisconsin–Madison
Zachary G. Ives
Zachary G. Ives University of Pennsylvania
Pedro Domingos
Pedro Domingos University of Washington
Peter Haddawy
Peter Haddawy Mahidol University
Donald Kossmann
Donald Kossmann Microsoft (United States)
Luis Gravano
Luis Gravano Columbia University
Michael J. Franklin
Michael J. Franklin University of Chicago
Clement Yu
Clement Yu University of Illinois at Chicago

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