World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
71
Citations
21942
World Ranking
1765
National Ranking
898

Mohit Bansal 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 Mohit Bansal 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 297 publications — 73rd percentile

73% of scientists in this discipline score the same or lower.

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

Mohit Bansal 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 Mohit Bansal sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 71 D-Index — 88th percentile

88% of scientists in this discipline score the same or lower.

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

Overview

Mohit Bansal is a researcher affiliated with the University of North Carolina at Chapel Hill in the United States. Their work primarily spans the field of Computer Science, with significant contributions to Artificial Intelligence and Computer Vision and Pattern Recognition. They have also published in areas such as Surgery, Control and Systems Engineering, and Information Systems.

Their research topics cover a diverse range within machine learning and artificial intelligence, including:

  • Topic Modeling
  • Multimodal Machine Learning Applications
  • Natural Language Processing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques
  • Explainable Artificial Intelligence (XAI)
  • Video Analysis and Summarization

Bansal's publication record features papers in notable venues such as arXiv (Cornell University), AAAI Conference on Artificial Intelligence, and conferences related to Computational Linguistics and Empirical Methods in NLP. Frequent venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Findings of the Association for Computational Linguistics: NAACL 2022

Examples of recent papers authored or co-authored by Bansal include:

  • "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning", 2022, arXiv (Cornell University)
  • "How Much Can CLIP Benefit Vision-and-Language Tasks?", 2021, arXiv (Cornell University)
  • "An Empirical Survey of Data Augmentation for Limited Data Learning in NLP", 2023, Transactions of the Association for Computational Linguistics
  • "Spoken language interaction with robots: Recommendations for future research", 2021, Computer Speech & Language
  • "Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits", 2020, Proceedings of the AAAI Conference on Artificial Intelligence

Bansal collaborates regularly with several co-authors, including:

  • Jaemin Cho
  • Elias Stengel-Eskin
  • Peter Hase
  • Shiyue Zhang
  • Prateek Yadav

Best Publications

  • LXMERT: Learning Cross-Modality Encoder Representations from Transformers

    Hao Tan;Mohit Bansal

  • End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures

    Makoto Miwa;Mohit Bansal

  • MAttNet: Modular Attention Network for Referring Expression Comprehension

    Licheng Yu;Zhe Lin;Xiaohui Shen;Jimei Yang

  • Adversarial NLI: A New Benchmark for Natural Language Understanding

    Yixin Nie;Adina Williams;Emily Dinan;Mohit Bansal

  • Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting

    Yen-Chun Chen;Mohit Bansal

  • Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling

    Jie Lei;Linjie Li;Luowei Zhou;Zhe Gan

  • Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

    Unknown

  • TVQA: Localized, Compositional Video Question Answering

    Jie Lei;Licheng Yu;Mohit Bansal;Tamara L. Berg

  • Towards Universal Paraphrastic Sentence Embeddings

    John Wieting;Mohit Bansal;Kevin Gimpel;Karen Livescu

  • Tailoring Continuous Word Representations for Dependency Parsing

    Mohit Bansal;Kevin Gimpel;Karen Livescu

  • Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout

    Hao Tan;Licheng Yu;Mohit Bansal

  • From Paraphrase Database to Compositional Paraphrase Model and Back

    John Wieting;Mohit Bansal;Kevin Gimpel;Karen Livescu

  • What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment

    Hongyuan Mei;Mohit Bansal;Matthew R. Walter

  • Combining Fact Extraction and Verification with Neural Semantic Matching Networks

    Yixin Nie;Haonan Chen;Mohit Bansal

  • A Joint Speaker-Listener-Reinforcer Model for Referring Expressions

    Licheng Yu;Hao Tan;Mohit Bansal;Tamara L. Berg

  • TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval

    Jie Lei;Licheng Yu;Tamara L. Berg;Mohit Bansal

  • Listen, attend, and walk: neural mapping of navigational instructions to action sequences

    Hongyuan Mei;Mohit Bansal;Matthew R. Walter

  • What Are You Talking About? Text-to-Image Coreference

    Chen Kong;Dahua Lin;Mohit Bansal;Raquel Urtasun

  • Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?

    Peter Hase;Mohit Bansal

  • Charagram: Embedding Words and Sentences via Character n-grams

    John Wieting;Mohit Bansal;Kevin Gimpel;Karen Livescu

  • Dynabench: Rethinking Benchmarking in NLP.

    Douwe Kiela;Max Bartolo;Yixin Nie;Divyansh Kaushik

Frequent Co-Authors

Kevin Gimpel
Kevin Gimpel Toyota Technological Institute at Chicago
Tamara L. Berg
Tamara L. Berg University of North Carolina at Chapel Hill
Karen Livescu
Karen Livescu Toyota Technological Institute at Chicago
Matthew R. Walter
Matthew R. Walter Toyota Technological Institute at Chicago
Daniel Klein
Daniel Klein University of California, Berkeley
Devi Parikh
Devi Parikh Facebook (United States)
Dhruv Batra
Dhruv Batra Georgia Institute of Technology
R.P. Saini
R.P. Saini Indian Institute of Technology Roorkee
Douwe Kiela
Douwe Kiela Stanford University
Ido Dagan
Ido Dagan Bar-Ilan University

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