World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
18461
World Ranking
7367
National Ranking
3211

Mihai Surdeanu 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 Mihai Surdeanu 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: 178 publications — 38th percentile

38% of scientists in this discipline score the same or lower.

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

Mihai Surdeanu 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 Mihai Surdeanu 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: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Overview

Mihai Surdeanu is affiliated with the University of Arizona in the United States. Their research primarily focuses on the field of Computer Science, with a strong emphasis on Artificial Intelligence. Additional subfields of study include Information Systems, Management Science and Operations Research, Molecular Biology, and Political Science and International Relations.

The scientist's work covers various topics, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Advanced Text Analysis Techniques
  • Expert finding and Q&A systems
  • Text Readability and Simplification
  • Software Engineering Research
  • Semantic Web and Ontologies

Their recent publications cover diverse aspects of natural language processing and related areas. Notable papers include:

  • "Time Travel in LLMs: Tracing Data Contamination in Large Language Models" (2023, arXiv, Cornell University)
  • "Towards the Necessity for Debiasing Natural Language Inference Datasets" (2020, Arabixiv, OSF Preprints)
  • "SuMe: A Dataset Towards Summarizing Biomedical Mechanisms" (2022, UA Campus Repository, The University of Arizona)
  • "Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models" (2023, arXiv, Cornell University)
  • "It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers" (2022, Computational Linguistics)

Frequent co-authors in their work include:

  • Shahriar Golchin
  • Robert Vacareanu
  • Razvan-Gabriel Dumitru
  • Zheng Tang
  • Zhengzhong Liang

The scientist has published extensively in the following venues:

  • arXiv (Cornell University)
  • UA Campus Repository (The University of Arizona)
  • Arabixiv (OSF Preprints)
  • Computational Linguistics
  • GSA Today

Mihai Surdeanu has authored at least one book, notably published by Cambridge University Press:

  • Deep Learning for Natural Language Processing (2024)

This profile describes a researcher with a broad and detailed engagement in the development of natural language processing methodologies and computational techniques, contributing both scholarly articles and a specialized book to the academic community.

Best Publications

  • The Stanford CoreNLP Natural Language Processing Toolkit

    Christopher Manning;Mihai Surdeanu;John Bauer;Jenny Finkel

  • Multi-instance Multi-label Learning for Relation Extraction

    Mihai Surdeanu;Julie Tibshirani;Ramesh Nallapati;Christopher D. Manning

  • The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages

    Jan Hajiċ;Massimiliano Ciaramita;Richard Johansson;Daisuke Kawahara

  • Stanford’s Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task

    Heeyoung Lee;Yves Peirsman;Angel Chang;Nathanael Chambers

  • The CoNLL 2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies

    Mihai Surdeanu;Richard Johansson;Adam Meyers;Lluís Màrquez

  • Deterministic coreference resolution based on entity-centric, precision-ranked rules

    Heeyoung Lee;Angel Chang;Yves Peirsman;Nathanael Chambers

  • Using Predicate-Argument Structures for Information Extraction

    Mihai Surdeanu;Sanda Harabagiu;John Williams;Paul Aarseth

  • Performance issues and error analysis in an open-domain question answering system

    Dan Moldovan;Marius Paşca;Sanda Harabagiu;Mihai Surdeanu

  • A Multi-Pass Sieve for Coreference Resolution

    Karthik Raghunathan;Heeyoung Lee;Sudarshan Rangarajan;Nate Chambers

  • FALCON: Boosting Knowledge for Answer Engines

    Sanda M. Harabagiu;Dan I. Moldovan;Marius. Paşca;Rada Mihalcea

  • Learning to Rank Answers on Large Online QA Collections

    Mihai Surdeanu;Massimiliano Ciaramita;Hugo Zaragoza

  • Joint Entity and Event Coreference Resolution across Documents

    Heeyoung Lee;Marta Recasens;Angel Chang;Mihai Surdeanu

  • Learning to rank answers to non-factoid questions from web collections

    Mihai Surdeanu;Massimiliano Ciaramita;Hugo Zaragoza

  • Event Extraction as Dependency Parsing

    David McClosky;Mihai Surdeanu;Christopher Manning

  • Discourse Complements Lexical Semantics for Non-factoid Answer Reranking

    Peter Jansen;Mihai Surdeanu;Peter Clark

  • The Role of Lexico-Semantic Feedback in Open-Domain Textual Question-Answering

    Sanda Harabagiu;Dan Moldovan;Marius Pasca;Rada Mihalcea

  • Answering complex, list and context questions with LCC's Question-Answering Server

    Sanda M. Harabagiu;Dan I. Moldovan;Marius. Paşca;Mihai Surdeanu

  • On the Importance of Text Analysis for Stock Price Prediction

    Heeyoung Lee;Mihai Surdeanu;Bill MacCartney;Dan Jurafsky

  • Overview of the TAC2013 Knowledge Base Population Evaluation: English Slot Filling and Temporal Slot Filling.

    Mihai Surdeanu

  • Combination strategies for semantic role labeling

    Mihai Surdeanu;Lluís Màrquez;Xavier Carreras;Pere R. Comas

Frequent Co-Authors

Christopher D. Manning
Christopher D. Manning Stanford University
Stephen G. Kobourov
Stephen G. Kobourov University of Arizona
Steven Bethard
Steven Bethard University of Arizona
Dan Moldovan
Dan Moldovan The University of Texas at Dallas
Sanda M. Harabagiu
Sanda M. Harabagiu The University of Texas at Dallas
Lluís Màrquez
Lluís Màrquez Amazon (United States)
Dan Jurafsky
Dan Jurafsky Stanford University
Massimiliano Ciaramita
Massimiliano Ciaramita Google (United States)
Angel X. Chang
Angel X. Chang Simon Fraser University
Eneko Agirre
Eneko Agirre University of the Basque Country

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