World's Best Scientists 2026 revealed!
Gari D. Clifford

Gari D. Clifford

D-Index & Metrics

Computer Science

D-Index
75
Citations
24947
World Ranking
1406
National Ranking
730

Gari D. Clifford 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 Gari D. Clifford 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: 305 publications — 75th percentile

75% of scientists in this discipline score the same or lower.

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

Gari D. Clifford 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 Gari D. Clifford 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: 75 D-Index — 90th percentile

90% of scientists in this discipline score the same or lower.

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

Overview

Gari D. Clifford is affiliated with Emory University in the United States and has a strong research presence in the field of medicine, with a particular focus on cardiology and cardiovascular medicine. Their work spans several subfields including clinical psychology, cognitive neuroscience, epidemiology, and biomedical engineering.

Their research topics cover a range of areas in medical science, including:

  • ECG Monitoring and Analysis
  • Posttraumatic Stress Disorder Research
  • Heart Rate Variability and Autonomic Control
  • Traumatic Brain Injury Research
  • Non-Invasive Vital Sign Monitoring
  • EEG and Brain-Computer Interfaces
  • Phonocardiography and Auscultation Techniques

Clifford has contributed to numerous publications, particularly in venues such as UNC Libraries, arXiv (Cornell University), and bioRxiv (Cold Spring Harbor Laboratory). Other frequent publication venues include Physiological Measurement and the Journal of Electrocardiology.

Some of their recent papers include:

  • Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020, 2020, Physiological Measurement
  • Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021, 2021, 2021 Computing in Cardiology (CinC)
  • The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification, 2021, IEEE Journal of Biomedical and Health Informatics
  • Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020, 2020, bioRxiv (Cold Spring Harbor Laboratory)
  • The 2023 wearable photoplethysmography roadmap, 2023, Physiological Measurement

Clifford frequently collaborates with other researchers, including Thomas C. Neylan, Sarah D. Linnstaedt, Samuel A. McLean, Francesca L. Beaudoin, and Karestan C. Koenen. Their collaborations reflect a diverse set of interests aligned with their medical and psychological research focuses.

Best Publications

  • A dynamical model for generating synthetic electrocardiogram signals

    P.E. McSharry;G.D. Clifford;L. Tarassenko;L.A. Smith

  • Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database.

    Mohammed Saeed;Mauricio Villarroel;Andrew T. Reisner;Gari Clifford;Gari Clifford

  • Advanced Methods And Tools for ECG Data Analysis

    Gari D. Clifford;Francisco Azuaje;Patrick McSharry

  • AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017

    Gari D Clifford;Chengyu Liu;Benjamin Moody;Li-wei H. Lehman

  • An open access database for the evaluation of heart sound algorithms.

    Chengyu Liu;David Springer;Qiao Li;Benjamin Moody

  • A Nonlinear Bayesian Filtering Framework for ECG Denoising

    R. Sameni;M.B. Shamsollahi;C. Jutten;G.D. Clifford

  • Logistic Regression-HSMM-Based Heart Sound Segmentation

    David B. Springer;Lionel Tarassenko;Gari D. Clifford

  • Automated de-identification of free-text medical records

    Ishna Neamatullah;Margaret M Douglass;Li-wei H Lehman;Andrew Tomas Reisner

  • A Review of Fetal ECG Signal Processing; Issues and Promising Directions.

    Reza Sameni;Gari D. Clifford

  • Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter.

    Q Li;R G Mark;G D Clifford

  • Machine Learning and Decision Support in Critical Care

    Alistair E. W. Johnson;Mohammad M. Ghassemi;Shamim Nemati;Katherine E. Niehaus

  • Quantifying errors in spectral estimates of HRV due to beat replacement and resampling

    G.D. Clifford;L. Tarassenko

  • Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms

    G D Clifford;J Behar;Q Li;Q Li;I Rezek

  • ECG Signal Quality During Arrhythmia and Its Application to False Alarm Reduction

    J. Behar;J. Oster;Qiao Li;G. D. Clifford

  • Application of independent component analysis in removing artefacts from the electrocardiogram

    Taigang He;Gari Clifford;Lionel Tarassenko

  • Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020.

    Erick A Perez Alday;Annie Gu;Amit J Shah;Chad Robichaux

  • Ventricular Fibrillation and Tachycardia Classification Using a Machine Learning Approach

    Qiao Li;Cadathur Rajagopalan;Gari D. Clifford

  • Dynamic time warping and machine learning for signal quality assessment of pulsatile signals

    Q Li;Q Li;G D Clifford

  • An open source benchmarked toolbox for cardiovascular waveform and interval analysis.

    Adriana N Vest;Giulia Da Poian;Qiao Li;Chengyu Liu

  • Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform

    Anton Aboukhalil;Larry Nielsen;Mohammed Saeed;Roger G. Mark

  • Smartphone app for non-invasive detection of anemia using only patient-sourced photos

    Robert G. Mannino;Robert G. Mannino;Robert G. Mannino;David R Myers;David R Myers;David R Myers;Erika A. Tyburski;Erika A. Tyburski;Erika A. Tyburski;Christina Caruso

  • Machine Learning and Decision Support in Critical Care This paper discusses the issues of compartmentalization, corruption, and complexity involved in collection and preprocessing of critical care data.

    Alistair E. W. Johnson;Mohammad M. Ghassemi;Shamim Nemati;Katherine E. Niehaus

Frequent Co-Authors

Lionel Tarassenko
Lionel Tarassenko University of Oxford
Chengyu Liu
Chengyu Liu Southeast University
Patrick E. McSharry
Patrick E. McSharry Carnegie Mellon University
David A. Clifton
David A. Clifton University of Oxford
Atul Malhotra
Atul Malhotra University of California, San Diego
Kerry J. Ressler
Kerry J. Ressler Harvard University
Thomas C. Neylan
Thomas C. Neylan University of California, San Francisco
Diego A. Pizzagalli
Diego A. Pizzagalli Harvard University

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