World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
38
Citations
12020
World Ranking
2276
National Ranking
140

Thomas Jaki publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Thomas Jaki sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 259 publications — 79th percentile

79% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Thomas Jaki D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Thomas Jaki sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 38 D-Index — 37th percentile

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

The last bar groups every scientist with 86 D-Index or more.

Best Publications

  • Remdesivir in adults with severe COVID-19: a randomised, double-blind, placebo-controlled, multicentre trial.

    Yeming Wang;Yeming Wang;Dingyu Zhang;Guanhua Du;Ronghui Du

  • Adaptive designs in clinical trials: why use them, and how to run and report them

    Philip Pallmann;Alun W. Bedding;Babak Choodari-Oskooei;Munyaradzi Dimairo

  • Effect of Dexamethasone in Hospitalized Patients with COVID-19 – Preliminary Report

    P Horby;W S Lim;J Emberson;M Mafham

  • Lopinavir–ritonavir in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

    Peter W Horby;Marion Mafham;Jennifer L Bell;Louise Linsell

  • Effect of Dexamethasone in Hospitalized Patients with COVID-19: Preliminary Report

    Peter Horby;Wei Shen Lim;Jonathan Emberson;Marion Mafham

  • Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): preliminary results of a randomised, controlled, open-label, platform trial

    P Horby;G Pessoa-Amorim;L Peto

  • Effect of Hydroxychloroquine in Hospitalized Patients with COVID-19: Preliminary results from a multi-centre, randomized, controlled trial.

    P Horby;M Mafham;L Linsell;J L Bell

  • A generalized Dunnett Test for Multi-arm Multi-stage Clinical Studies with Treatment Selection

    Dominic Magirr;Thomas Jaki;John Whitehead

  • The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design

    Munyaradzi Dimairo;Philip Pallmann;James Wason;James Wason;Susan Todd

  • A review of statistical updating methods for clinical prediction models

    Ting-Li Su;Thomas Friedrich Jaki;Graeme Hickey;Iain Buchan

  • Casirivimab and imdevimab in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

    P Horby;M Mafham;L Peto

  • Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models

    M. Sperrin;T. Jaki;E. Wit

  • Optimal design of multi-arm multi-stage trials

    James M. S. Wason;Thomas Jaki

  • Some recommendations for multi-arm multi-stage trials

    James Wason;Dominic Magirr;Martin Law;Thomas Jaki

  • Assessing differential effects: applying regression mixture models to identify variations in the influence of family resources on academic achievement.

    M. Lee Van Horn;Thomas Jaki;Katherine Masyn;Sharon Landesman Ramey

  • Optimal dose and safety of molnupiravir in patients with early SARS-CoV-2: a Phase I, open-label, dose-escalating, randomized controlled study.

    Saye H Khoo;Richard Fitzgerald;Thomas Fletcher;Thomas Fletcher;Sean Ewings

  • How to design a dose-finding study using the continual reassessment method

    Graham M. Wheeler;Adrian P. Mander;Alun Bedding;Kristian Brock

  • Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

    Peter W Horby;Lise Estcourt;Leon Peto

  • Estimation of pharmacokinetic parameters with the R package PK.

    Thomas Jaki;Martin J. Wolfsegger

  • Evaluation of the efficacy and safety of intravenous remdesivir in adult patients with severe COVID-19: study protocol for a phase 3 randomized, double-blind, placebo-controlled, multicentre trial.

    Yeming Wang;Fei Zhou;Dingyu Zhang;Jianping Zhao

Frequent Co-Authors

Peter Horby
Peter Horby University of Oxford
Jonathan Emberson
Jonathan Emberson University of Oxford
Saul N. Faust
Saul N. Faust University Hospital Southampton NHS Foundation Trust
Daniel J. Feaster
Daniel J. Feaster University of Miami
Saye Khoo
Saye Khoo University of Liverpool
Nigel Stallard
Nigel Stallard University of Warwick
Munir Pirmohamed
Munir Pirmohamed University of Liverpool
Christopher E. Brightling
Christopher E. Brightling University of Leicester
Paula R Williamson
Paula R Williamson University of Liverpool
Jon Nicholl
Jon Nicholl University of Sheffield

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Related Online Degrees & Career Pathways

Pursuing a Mathematics degree in the USA opens several related educational and career pathways, especially in data-driven and business-focused fields. Many students leverage their math background to transition into programs like a year long MBA programs, which provide essential business acumen and leadership skills in a condensed timeframe.

For those looking to build on previous academic credits, MBA programs that accept transfer credits offer flexibility and streamlined study options. This can be especially helpful for working professionals seeking career advancement without starting from scratch.

Careers in data sciences are a natural fit for math graduates, with many opting for a specialized masters in data analytics. This degree combines mathematical expertise with data interpretation skills, making graduates highly competitive in technology and finance sectors.

Additionally, for those concerned about admissions competitiveness, exploring mba programs easy to get into can provide practical options for advancing education without long delays or high rejection rates.

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