World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
24398
World Ranking
6282
National Ranking
2809

Byron C. Wallace 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 Byron C. Wallace 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: 173 publications — 36th percentile

36% of scientists in this discipline score the same or lower.

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

Byron C. Wallace 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 Byron C. Wallace 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: 47 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Overview

Byron C. Wallace is affiliated with Northeastern University in the United States. Their research focuses primarily on computer science, with a specialization in artificial intelligence. They have contributed extensively to subfields including molecular biology, statistics, probability and uncertainty, health informatics, and general social sciences.

Their main research topics cover a range of areas related to text processing and machine learning, such as:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Biomedical Text Mining and Ontologies
  • Machine Learning in Healthcare
  • Text Readability and Simplification
  • Meta-analysis and Systematic Reviews
  • Artificial Intelligence in Healthcare and Education

Byron C. Wallace has published research in notable venues including:

  • Zenodo (CERN European Organization for Nuclear Research)
  • arXiv (Cornell University)
  • OPAL (Open@LaTrobe) (La Trobe University)
  • PubMed
  • Journal of the American Medical Informatics Association

Frequent collaborators include authors such as Iain Marshall, Ani Nenkova, Frank Soboczenski, Benjamin Nye, and James Thomas.

Among their recent publications are:

  • "Trialstreamer: A living, automatically updated database of clinical trial reports" (2020), Journal of the American Medical Informatics Association
  • "Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study" (2020), JMIR Medical Informatics
  • "Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization" (2020), arXiv (Cornell University)
  • "Evaluating Factuality in Text Simplification" (2022), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • "The views of health guideline developers on the use of automation in health evidence synthesis" (2021), Systematic Reviews

Best Publications

  • A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification

    Ye Zhang;Byron C. Wallace

  • Closing the Gap between Methodologists and End-Users: R as a Computational Back-End

    Byron C. Wallace;Issa J. Dahabreh;Thomas A. Trikalinos;Joseph Lau

  • Attention is not Explanation.

    Sarthak Jain;Byron C. Wallace

  • Meta-Analyst: software for meta-analysis of binary, continuous and diagnostic data

    Byron C Wallace;Byron C Wallace;Christopher H Schmid;Joseph Lau;Thomas A Trikalinos

  • Deploying an interactive machine learning system in an evidence-based practice center: abstrackr

    Byron C. Wallace;Kevin Small;Carla E. Brodley;Joseph Lau

  • Toward systematic review automation: a practical guide to using machine learning tools in research synthesis

    Iain J. Marshall;Byron C. Wallace

  • ERASER: A Benchmark to Evaluate Rationalized NLP Models

    Jay DeYoung;Sarthak Jain;Nazneen Fatema Rajani;Eric Lehman

  • Semi-automated screening of biomedical citations for systematic reviews

    Byron C. Wallace;Byron C. Wallace;Thomas A. Trikalinos;Joseph Lau;Carla E. Brodley

  • OpenMEE : Intuitive, open-source software for meta-analysis in ecology and evolutionary biology

    Byron C. Wallace;Marc J. Lajeunesse;George Dietz;Issa J. Dahabreh

  • Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide.

    Iain J. Marshall;Anna Noel-Storr;Joël Kuiper;James Thomas

  • Living systematic reviews: 2. Combining human and machine effort.

    James Thomas;Anna Noel-Storr;Iain Marshall;Byron Wallace

  • RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials

    Iain James Marshall;Joël Kuiper;Byron C. Wallace

  • Modelling Context with User Embeddings for Sarcasm Detection in Social Media

    Silvio Amir;Byron C. Wallace;Hao Lyu;Paula Carvalho

  • Class Imbalance, Redux

    Byron C. Wallace;Kevin Small;Carla E. Brodley;Thomas A. Trikalinos

  • A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature.

    Benjamin E. Nye;Junyi Jessy Li;Roma Patel;Yinfei Yang

  • Rationale-Augmented Convolutional Neural Networks for Text Classification

    Ye Zhang;Iain James Marshall;Byron C. Wallace

  • Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach

    Byron C. Wallace;Anna Noel-Storr;Iain James Marshall;Aaron M. Cohen

  • Neural information retrieval: at the end of the early years

    Kezban Dilek Onal;Kezban Dilek Onal;Ye Zhang;Ismail Sengor Altingovde;Md. Mustafizur Rahman

  • Humans Require Context to Infer Ironic Intent (so Computers Probably do, too)

    Byron C. Wallace;Do Kook Choe;Laura Kertz;Eugene Charniak

  • Active learning for biomedical citation screening

    Byron C. Wallace;Kevin Small;Carla E. Brodley;Thomas A. Trikalinos

  • Active discriminative text representation learning

    Ye Zhang;Matthew Lease;Byron C. Wallace

  • Extracting PICO sentences from clinical trial reports using supervised distant supervision

    Byron C. Wallace;Joël Kuiper;Aakash Sharma;Mingxi Zhu

  • Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions

    Xiaochuang Han;Byron C. Wallace;Yulia Tsvetkov

Frequent Co-Authors

Thomas A Trikalinos
Thomas A Trikalinos Brown University
Ani Nenkova
Ani Nenkova Adobe Systems (United States)
Matthew Lease
Matthew Lease The University of Texas at Austin
Joseph Lau
Joseph Lau Chinese University of Hong Kong
Carla E. Brodley
Carla E. Brodley Northeastern University
Christopher H. Schmid
Christopher H. Schmid Brown University
Michael J. Paul
Michael J. Paul University of Colorado Boulder
Joydeep Ghosh
Joydeep Ghosh The University of Texas at Austin
Eugene Charniak
Eugene Charniak Brown University
Zachary C. Lipton
Zachary C. Lipton Carnegie Mellon University

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

Choosing the right education pathway in computer science can open doors to a variety of rewarding careers. For those looking to quickly gain relevant skills, an online associate's degree offers a fast entry point into tech fields, especially for students balancing work or family commitments.

Many working professionals return to school for one of the most useful masters degrees to advance in high-demand tech roles or leadership positions. Online programs make it easier to pursue graduate study while maintaining a job.

Budget-conscious students have more options than ever before. Attending one of the cheapest online college programs can reduce student debt while providing access to respected computer science courses.

Additionally, if you’re concerned about past academic performance, consider applying to the best colleges for low gpa, which offer flexible admission policies without sacrificing educational quality.

Best Scientists Citing Byron C. Wallace

Trending Scientists

Recently Published Articles