World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
88429
World Ranking
6962
National Ranking
3042

Ming-Wei Chang 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 Ming-Wei Chang 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: 105 publications — 10th percentile

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

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

Ming-Wei Chang 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 Ming-Wei Chang 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Ming-Wei Chang is affiliated with Google in the United States and has a focused research profile primarily in computer science with significant contributions across several related subfields. Their scholarly output includes a strong emphasis on artificial intelligence, computer vision and pattern recognition, biomedical engineering, pharmaceutical science, and biomaterials.

Their work spans numerous topics that intersect machine learning and biomedical applications. Key topics in Ming-Wei Chang's research include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Electrospun Nanofibers in Biomedical Applications
  • Advancements in Transdermal Drug Delivery
  • Electrohydrodynamics and Fluid Dynamics
  • Domain Adaptation and Few-Shot Learning

The scientist has published extensively, with 72 papers in computer science-related fields. Their most frequent publication venues are:

  • arXiv (Cornell University) - 25 publications
  • Journal of Drug Delivery Science and Technology - 5 publications
  • Advanced Drug Delivery Reviews - 3 publications
  • Drug Discovery Today - 2 publications
  • Micromachines - 2 publications

Among their recent papers are the following:

  • "EMBI" (2024), presented at Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • "REALM: Retrieval-Augmented Language Model Pre-Training" (2020), published in arXiv (Cornell University)
  • "A review of emerging technologies enabling improved solid oral dosage form manufacturing and processing" (2021), featured in Advanced Drug Delivery Reviews
  • "Application of mesoporous silica nanoparticles as drug delivery carriers for chemotherapeutic agents" (2020), in Drug Discovery Today
  • "High Precision 3D Printing for Micro to Nano Scale Biomedical and Electronic Devices" (2022), published in Micromachines

Ming-Wei Chang frequently collaborates with a range of researchers, including Zeeshan Ahmad, Muhammad Sohail Arshad, Kenton Lee, Saman Zafar, and Baolin Wang. The collaboration counts with these coauthors range from 9 to 31 joint publications.

Best Publications

  • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

    Jacob Devlin;Ming-Wei Chang;Kenton Lee;Kristina N. Toutanova

  • Natural Questions: A Benchmark for Question Answering Research

    Tom Kwiatkowski;Jennimaria Palomaki;Olivia Redfield;Michael Collins

  • Load forecasting using support vector Machines: a study on EUNITE competition 2001

    Bo-Juen Chen;Ming-Wei Chang;Chih-Jen lin

  • Latent Retrieval for Weakly Supervised Open Domain Question Answering

    Kenton Lee;Ming-Wei Chang;Kristina N. Toutanova

  • Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base

    Wen-tau Yih;Ming-Wei Chang;Xiaodong He;Jianfeng Gao

  • BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

    Christopher Clark;Kenton Lee;Ming-Wei Chang;Tom Kwiatkowski

  • A Knowledge-Grounded Neural Conversation Model

    Marjan Ghazvininejad;Chris Brockett;Ming-Wei Chang;Bill Dolan

  • REALM: Retrieval-Augmented Language Model Pre-Training.

    Kelvin Guu;Kenton Lee;Zora Tung;Panupong Pasupat

  • The Value of Semantic Parse Labeling for Knowledge Base Question Answering

    Wen-tau Yih;Matthew Richardson;Christopher Meek;Ming-Wei Chang

  • Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

    Iulia Turc;Ming-Wei Chang;Kenton Lee;Kristina Toutanova

  • Zero-shot Entity Linking by Reading Entity Descriptions

    Lajanugen Logeswaran;Ming-Wei Chang;Kenton Lee;Kristina N. Toutanova

  • Large Dual Encoders Are Generalizable Retrievers

    Unknown

  • Question Answering Using Enhanced Lexical Semantic Models

    Wen-tau Yih;Ming-Wei Chang;Christopher Meek;Andrzej Pastusiak

  • Driving Semantic Parsing from the World's Response

    James Clarke;Dan Goldwasser;Ming-Wei Chang;Dan Roth

  • Importance of semantic representation: dataless classification

    Ming-Wei Chang;Lev Ratinov;Dan Roth;Vivek Srikumar

  • Guiding Semi-Supervision with Constraint-Driven Learning

    Ming-Wei Chang;Lev Ratinov;Dan Roth

  • To Link or Not to Link? A Study on End-to-End Tweet Entity Linking

    Stephen Guo;Ming-Wei Chang;Emre Kiciman

  • Retrieval Augmented Language Model Pre-Training

    Kelvin Guu;Kenton Lee;Zora Tung;Panupong Pasupat

  • Search-based Neural Structured Learning for Sequential Question Answering

    Mohit Iyyer;Wen-tau Yih;Ming-Wei Chang

  • Leave-One-Out Bounds for Support Vector Regression Model Selection

    Ming-Wei Chang;Chih-Jen Lin

  • Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation

    Iulia Turc;Ming-Wei Chang;Kenton Lee;Kristina Toutanova

Frequent Co-Authors

Kenton Lee
Kenton Lee Google (United States)
Kristina Toutanova
Kristina Toutanova Google (United States)
Dan Roth
Dan Roth University of Pennsylvania
Wen-tau Yih
Wen-tau Yih Facebook (United States)
Jianfeng Gao
Jianfeng Gao Microsoft (United States)
Vivek Srikumar
Vivek Srikumar University of Utah
Chih-Jen Lin
Chih-Jen Lin National Taiwan University
Xiaodong He
Xiaodong He Chinese Academy of Sciences
William W. Cohen
William W. Cohen Carnegie Mellon University
Michael Collins
Michael Collins Google (United States)

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