World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
35487
World Ranking
3318
National Ranking
1608

Sameer Singh 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 Sameer Singh 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: 177 publications — 37th percentile

37% of scientists in this discipline score the same or lower.

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

Sameer Singh 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 Sameer Singh 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: 59 D-Index — 77th percentile

77% 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

  • 2020 - Hellman Fellow

Overview

Sameer Singh is a researcher affiliated with the University of California, Irvine in the United States. Their primary domain of work lies within Computer Science, with a substantial number of publications-196 in total. Their research spans several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Plant Science, Sociology and Political Science, and Molecular Biology.

Their research interests include multiple main topics such as Topic Modeling, Natural Language Processing Techniques, Explainable Artificial Intelligence (XAI), Multimodal Machine Learning Applications, Adversarial Robustness in Machine Learning, Agricultural pest management studies, and Machine Learning and Data Classification.

Sameer Singh has contributed to various scholarly venues, with frequent publications appearing in:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Journal of Thoracic and Cardiovascular Surgery

Their recent papers demonstrate a focus on topics related to machine learning interpretability, natural language processing, and generative adversarial networks. Notable works include:

  • "Fooling LIME and SHAP" (2020), published in Proceedings of the AAAI/ACM Conference on AI Ethics and Society
  • "An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews" (2021), published in International Journal of Research in Marketing
  • "Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models" (2022), published in Findings of the Association for Computational Linguistics: ACL 2022
  • "Image Augmentations for GAN Training" (2020), published on arXiv (Cornell University)
  • "Improved Consistency Regularization for GANs" (2021), published in Proceedings of the AAAI Conference on Artificial Intelligence

Frequent collaborators in their research include Matt Gardner, Kamal Ravi Sharma, Dylan Slack, Himabindu Lakkaraju, and Padhraic Smyth, reflecting ongoing partnerships across different areas of study.

Sameer Singh's work has been recognized with awards such as the Hellman Fellowship, which was received in 2020.

Best Publications

  • “Why Should I Trust You?”: Explaining the Predictions of Any Classifier

    Marco Túlio Ribeiro;Sameer Singh;Carlos Guestrin

  • Anchors: High-Precision Model-Agnostic Explanations

    Marco Tulio Ribeiro;Sameer Singh;Carlos Guestrin

  • AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

    Taylor Shin;Yasaman Razeghi;Robert L. Logan;Eric Wallace

  • Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods

    Dylan Slack;Sophie Hilgard;Emily Jia;Sameer Singh

  • Knowledge Enhanced Contextual Word Representations

    Matthew E. Peters;Mark Neumann;Robert L. Logan;Roy Schwartz

  • Beyond accuracy: Behavioral testing of NLP models with checklist

    Marco Tulio Ribeiro;Tongshuang Wu;Carlos Guestrin;Sameer Singh

  • Model-Agnostic Interpretability of Machine Learning.

    Marco Túlio Ribeiro;Sameer Singh;Carlos Guestrin

  • Universal Adversarial Triggers for Attacking and Analyzing NLP

    Eric Wallace;Shi Feng;Nikhil Kandpal;Matt Gardner

  • DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

    Dheeru Dua;Yizhong Wang;Pradeep Dasigi;Gabriel Stanovsky

  • Semantically Equivalent Adversarial Rules for Debugging NLP models

    Marco Tulio Ribeiro;Sameer Singh;Carlos Guestrin

  • Generating Natural Adversarial Examples

    Zhengli Zhao;Dheeru Dua;Sameer Singh

  • Calibrate Before Use: Improving Few-shot Performance of Language Models

    Zihao Zhao;Eric Wallace;Shi Feng;Dan Klein

  • Evaluating Models’ Local Decision Boundaries via Contrast Sets

    Matt Gardner;Yoav Artzi;Victoria Basmov;Jonathan Berant

  • FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs

    Andrew McCallum;Karl Schultz;Sameer Singh

  • Injecting Logical Background Knowledge into Embeddings for Relation Extraction

    Tim Rocktäschel;Sameer Singh;Sebastian Riedel

  • Design Challenges for Entity Linking

    Xiao Ling;Sameer Singh;Daniel Weld

  • Entity Linking via Joint Encoding of Types, Descriptions, and Context

    Nitish Gupta;Sameer Singh;Dan Roth

  • Do NLP Models Know Numbers? Probing Numeracy in Embeddings

    Eric Wallace;Yizhong Wang;Sujian Li;Sameer Singh

  • COVIDLIES: Detecting COVID-19 Misinformation on Social Media

    Tamanna Hossain;Robert L. Logan;Arjuna Ugarte;Yoshitomo Matsubara

  • Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling

    Robert L. Logan;Nelson F. Liu;Matthew E. Peters;Matt Gardner

  • Evaluating Models' Local Decision Boundaries via Contrast Sets.

    Matt Gardner;Yoav Artzi;Victoria Basmova;Jonathan Berant

  • Calibrate Before Use: Improving Few-Shot Performance of Language Models

    Tony Z. Zhao;Eric Wallace;Shi Feng;Dan Klein

Frequent Co-Authors

Matt Gardner
Matt Gardner Allen Institute for Artificial Intelligence
Andrew McCallum
Andrew McCallum University of Massachusetts Amherst
Sebastian Riedel
Sebastian Riedel University College London
Carlos Guestrin
Carlos Guestrin Stanford University
Tim Rocktäschel
Tim Rocktäschel University College London
Dan Roth
Dan Roth University of Pennsylvania
Noah A. Smith
Noah A. Smith University of Washington
Anthony Chen
Anthony Chen Hong Kong Polytechnic University
Jonathan Berant
Jonathan Berant Tel Aviv University
Hannaneh Hajishirzi
Hannaneh Hajishirzi University of Washington

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens doors to various related fields and flexible learning options. Many students are interested in pursuing an online degree in mechanical engineering, which combines principles of physics and mathematics with computer science concepts, supporting careers in robotics, automation, and more.

For those intrigued by scientific research or advanced theory, an online theoretical physics degree offers rigorous training in computational modeling and the fundamentals that power emerging technologies.

Data-driven careers are also on the rise. Many universities in the USA provide reputable data science programs, teaching skills in analytics, machine learning, and big data that are highly sought after in today’s tech landscape.

If you prefer working with circuits or smart systems, pursuing an online bachelor’s in electrical engineering can lay a strong foundation for technology and innovation careers.

Considering any of these related online degrees can expand your career pathways, boost technical expertise, and help you thrive in dynamic STEM industries.

Best Scientists Citing Sameer Singh

Trending Scientists