World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
63
Citations
24491
World Ranking
1716
National Ranking
552

John Paul Strachan publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where John Paul Strachan sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 189 publications — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

John Paul Strachan D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where John Paul Strachan sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 63 D-Index — 83rd percentile

83% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

John Paul Strachan is affiliated with Hewlett-Packard in the United States and works primarily in the fields of Engineering and Computer Science. Their research has a strong focus on Electrical and Electronic Engineering and Artificial Intelligence, with additional contributions to Molecular Biology, Computational Theory and Mathematics, and Hardware and Architecture.

The main topics covered in their work include Advanced Memory and Neural Computing, Ferroelectric and Negative Capacitance Devices, Neural Networks and Reservoir Computing, Quantum Computing Algorithms and Architecture, Machine Learning and ELM, Advanced biosensing and bioanalysis techniques, and Photonic and Optical Devices.

Among their recent publications are:

  • Embedded Devices for Neuromorphic Time-Series Assessment (2022), Maryland Shared Open Access Repository (USMAI Consortium)
  • Dynamical memristors for higher-complexity neuromorphic computing (2022), Nature Reviews Materials
  • Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks (2020), Nature Electronics
  • Analog content-addressable memories with memristors (2020), Nature Communications
  • Tree-based machine learning performed in-memory with memristive analog CAM (2021), arXiv (Cornell University)

Frequent coauthors in their research include Giacomo Pedretti, Can Li, Xia Sheng, Catherine E. Graves, and Dmitri B. Strukov.

The venues where John Paul Strachan has published most often include arXiv (Cornell University), Nature Communications, Nature Electronics, IEEE Transactions on Electron Devices, and Scientific Reports.

Best Publications

  • Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

    Zhongrui Wang;Saumil Joshi;Sergey E. Savel’ev;Hao Jiang

  • The future of electronics based on memristive systems

    Mohammed A. Zidan;John Paul Strachan;Wei D. Lu

  • ISAAC: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars

    Ali Shafiee;Anirban Nag;Naveen Muralimanohar;Rajeev Balasubramonian

  • Analogue signal and image processing with large memristor crossbars

    Can Li;Miao Hu;Miao Hu;Yunning Li;Hao Jiang

  • Fully memristive neural networks for pattern classification with unsupervised learning

    Zhongrui Wang;Saumil Joshi;Sergey Savel’ev;Wenhao Song

  • Efficient and self-adaptive in-situ learning in multilayer memristor neural networks

    Can Li;Daniel Belkin;Daniel Belkin;Yunning Li;Peng Yan;Peng Yan

  • Sub-nanosecond switching of a tantalum oxide memristor

    Antonio C Torrezan;John Paul Strachan;Gilberto Medeiros-Ribeiro;R Stanley Williams

  • High switching endurance in TaOx memristive devices

    J. Joshua Yang;M.-X. Zhang;John Paul Strachan;Feng Miao

  • Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine.

    Miao Hu;Catherine E. Graves;Can Li;Yunning Li

  • Dot-product engine for neuromorphic computing: programming 1T1M crossbar to accelerate matrix-vector multiplication

    Miao Hu;John Paul Strachan;Zhiyong Li;Emmanuelle M. Grafals

  • Chaotic dynamics in nanoscale NbO 2 Mott memristors for analogue computing

    Suhas Kumar;John Paul Strachan;R. Stanley Williams

  • AGATA - Advanced GAmma Tracking Array

    S. Akkoyun;A. Algora;B. Alikhani;F. Ameil

  • Dynamical memristors for higher-complexity neuromorphic computing

    Unknown

  • Anatomy of a nanoscale conduction channel reveals the mechanism of a high-performance memristor.

    Feng Miao;John Paul Strachan;J. Joshua Yang;Min-Xian Zhang

  • PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference

    Aayush Ankit;Izzat El Hajj;Sai Rahul Chalamalasetti;Geoffrey Ndu

  • Direct identification of the conducting channels in a functioning memristive device.

    John Paul Strachan;Matthew D. Pickett;J. Joshua Yang;Shaul Aloni

  • Long short-term memory networks in memristor crossbars

    Can Li;Zhongrui Wang;Mingyi Rao;Daniel Belkin

  • Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks

    Fuxi Cai;Fuxi Cai;Suhas Kumar;Thomas Van Vaerenbergh;Xia Sheng

  • Reinforcement learning with analogue memristor arrays

    Zhongrui Wang;Can Li;Wenhao Song;Mingyi Rao

  • Long short-term memory networks in memristor crossbar arrays

    Can Li;Can Li;Zhongrui Wang;Mingyi Rao;Daniel Belkin

  • High-speed and low-energy nitride memristors

    Byung Joon Choi;Byung Joon Choi;Antonio C. Torrezan;John Paul Strachan;P. G. Kotula

  • Engineering nonlinearity into memristors for passive crossbar applications

    J. Joshua Yang;M.-X. Zhang;Matthew D. Pickett;Feng Miao

Frequent Co-Authors

J. Joshua Yang
J. Joshua Yang University of Southern California
R. Stanley Williams
R. Stanley Williams Texas A&M University
Matthew D. Pickett
Matthew D. Pickett Hewlett-Packard (United States)
Miao Hu
Miao Hu Binghamton University
Gilberto Medeiros-Ribeiro
Gilberto Medeiros-Ribeiro Universidade Federal de Minas Gerais
Ning Ge
Ning Ge Tsinghua University
Feng Miao
Feng Miao Nanjing University
Qiangfei Xia
Qiangfei Xia University of Massachusetts Amherst
Dejan Milojicic
Dejan Milojicic Hewlett-Packard (United States)
Zhiyong Li
Zhiyong Li Chinese Academy of Sciences

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