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Kai-Wei Chang

D-Index & Metrics

Computer Science

D-Index
62
Citations
31158
World Ranking
2821
National Ranking
1395

Kai-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 Kai-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: 237 publications — 59th percentile

59% of scientists in this discipline score the same or lower.

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

Kai-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 Kai-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: 62 D-Index — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Overview

Kai-Wei Chang is affiliated with the University of California, Los Angeles in the United States. Their research primarily focuses on computer science, with a significant number of publications in artificial intelligence and computer vision and pattern recognition. Other areas of study include information systems, molecular biology, and signal processing.

Their work covers multiple topics, reflecting a broad interest in machine learning and related fields. Key research topics include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Speech Recognition and Synthesis
  • Domain Adaptation and Few-Shot Learning
  • Adversarial Robustness in Machine Learning
  • Explainable Artificial Intelligence (XAI)

Kai-Wei Chang has contributed extensively to various publication venues. These include:

  • arXiv (Cornell University)
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Recent notable papers by Kai-Wei Chang are:

  • "Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering" (2022), published in arXiv (Cornell University)
  • "How Much Can CLIP Benefit Vision-and-Language Tasks?" (2021), published in arXiv (Cornell University)
  • "DEGREE: A Data-Efficient Generation-Based Event Extraction Model" (2022), published in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • "Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies" (2021), published in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • "Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond" (2020), published in arXiv (Cornell University)

The scientist has collaborated frequently with several researchers, including:

  • Nanyun Peng
  • Kuan-Hao Huang
  • Aram Galstyan
  • Wasi Uddin Ahmad
  • Hung-yi Lee

Best Publications

  • LIBLINEAR: A Library for Large Linear Classification

    Rong-En Fan;Kai-Wei Chang;Cho-Jui Hsieh;Xiang-Rui Wang

  • Man is to computer programmer as woman is to homemaker? debiasing word embeddings

    Tolga Bolukbasi;Kai-Wei Chang;James Zou;Venkatesh Saligrama

  • VisualBERT: A Simple and Performant Baseline for Vision and Language.

    Liunian Harold Li;Mark Yatskar;Da Yin;Cho-Jui Hsieh

  • A dual coordinate descent method for large-scale linear SVM

    Cho-Jui Hsieh;Kai-Wei Chang;Chih-Jen Lin;S. Sathiya Keerthi

  • Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

    Jieyu Zhao;Tianlu Wang;Mark Yatskar;Vicente Ordonez

  • Generating Natural Language Adversarial Examples

    Moustafa Alzantot;Yash Sharma;Ahmed Elgohary;Bo-Jhang Ho

  • Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods

    Jieyu Zhao;Tianlu Wang;Mark Yatskar;Vicente Ordonez

  • Training and Testing Low-degree Polynomial Data Mappings via Linear SVM

    Yin-Wen Chang;Cho-Jui Hsieh;Kai-Wei Chang;Michael Ringgaard

  • Unified Pre-training for Program Understanding and Generation

    Wasi Uddin Ahmad;Saikat Chakraborty;Baishakhi Ray;Kai-Wei Chang

  • Mitigating Gender Bias in Natural Language Processing: Literature Review

    Tony Sun;Andrew Gaut;Shirlyn Tang;Yuxin Huang

  • GPT-GNN: Generative Pre-Training of Graph Neural Networks

    Ziniu Hu;Yuxiao Dong;Kuansan Wang;Kai-Wei Chang

  • Large Linear Classification When Data Cannot Fit in Memory

    Hsiang-Fu Yu;Cho-Jui Hsieh;Kai-Wei Chang;Chih-Jen Lin

  • The Woman Worked as a Babysitter: On Biases in Language Generation

    Emily Sheng;Kai-Wei Chang;Premkumar Natarajan;Nanyun Peng

  • Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations

    Tianlu Wang;Jieyu Zhao;Mark Yatskar;Kai-Wei Chang

  • Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

    Unknown

  • Learning Gender-Neutral Word Embeddings

    Jieyu Zhao;Yichao Zhou;Zeyu Li;Wei Wang

  • A Transformer-based Approach for Source Code Summarization

    Wasi Uddin Ahmad;Saikat Chakraborty;Baishakhi Ray;Kai-Wei Chang

  • Gender Bias in Contextualized Word Embeddings

    Jieyu Zhao;Tianlu Wang;Mark Yatskar;Ryan Cotterell

  • Multifaceted protein-protein interaction prediction based on Siamese residual RCNN.

    Muhao Chen;Chelsea J T Ju;Guangyu Zhou;Xuelu Chen

  • Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines

    Kai-Wei Chang;Cho-Jui Hsieh;Chih-Jen Lin

  • A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification

    Guo-Xun Yuan;Kai-Wei Chang;Cho-Jui Hsieh;Chih-Jen Lin

  • BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation

    Jwala Dhamala;Tony Sun;Varun Kumar;Satyapriya Krishna

Frequent Co-Authors

Cho-Jui Hsieh
Cho-Jui Hsieh University of California, Los Angeles
Nanyun Peng
Nanyun Peng University of California, Los Angeles
Muhao Chen
Muhao Chen University of California, Los Angeles
Dan Roth
Dan Roth University of Pennsylvania
Chih-Jen Lin
Chih-Jen Lin National Taiwan University
Wei Wang
Wei Wang University of California, Los Angeles
Yizhou Sun
Yizhou Sun University of California, Los Angeles
Adam Tauman Kalai
Adam Tauman Kalai Microsoft (United States)
Carlo Zaniolo
Carlo Zaniolo University of California, Los Angeles
Venkatesh Saligrama
Venkatesh Saligrama Boston University

External Links

Personal website of Kai-Wei Chang Google Scholar page

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Best Scientists Citing Kai-Wei Chang

Dan Roth

Dan Roth

University of Pennsylvania

Publications: 49

Ivor W. Tsang

Ivor W. Tsang

Agency for Science, Technology and Research

Publications: 40

Muhao Chen

Muhao Chen

University of California, Los Angeles

Publications: 37

Yejin Choi

Yejin Choi

Stanford University

Publications: 36

Cho-Jui Hsieh

Cho-Jui Hsieh

University of California, Los Angeles

Publications: 35

Chih-Jen Lin

Chih-Jen Lin

National Taiwan University

Publications: 35

Maosong Sun

Maosong Sun

Tsinghua University

Publications: 34

Hal Daumé

Hal Daumé

University of Maryland, College Park

Publications: 34

Björn Schuller

Björn Schuller

Imperial College London

Publications: 34

Inderjit S. Dhillon

Inderjit S. Dhillon

Google (United States)

Publications: 32

Noah A. Smith

Noah A. Smith

University of Washington

Publications: 30

Nanyun Peng

Nanyun Peng

University of California, Los Angeles

Publications: 29

Zhi-Hua Zhou

Zhi-Hua Zhou

Nanjing University

Publications: 28

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 27

Yulia Tsvetkov

Yulia Tsvetkov

University of Washington

Publications: 25

Benjamin Van Durme

Benjamin Van Durme

Johns Hopkins University

Publications: 25

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