World's Best Scientists 2026 revealed!

Miao Hu 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 Miao Hu 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+

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

Miao Hu 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 Miao Hu 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+

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

Overview

Miao Hu is a researcher affiliated with Binghamton University in the United States. Their work spans multiple areas of computer science and engineering, with a particular focus on advancing memory technologies, neural computing, and security methods.

The main fields of study addressed by Miao Hu include Computer Science and Engineering. Within these, their research delves into subfields such as Electrical and Electronic Engineering, Computer Networks and Communications, Plant Science, Computer Vision and Pattern Recognition, and Information Systems.

Miao Hu's research topics cover diverse areas, notably:

  • Advanced Memory and Neural Computing
  • Ferroelectric and Negative Capacitance Devices
  • CCD and CMOS Imaging Sensors
  • Advanced Malware Detection Techniques
  • Network Security and Intrusion Detection
  • Smart Agriculture and AI
  • Neural dynamics and brain function

Their publication record includes several influential papers across high-impact journals and conferences. Recent notable works include:

  • "Thousands of conductance levels in memristors integrated on CMOS" (2023), published in Nature
  • "Programming memristor arrays with arbitrarily high precision for analog computing" (2024), published in Science
  • "A hierarchical deep reinforcement learning model with expert prior knowledge for intelligent penetration testing" (2023), published in Computers & Security
  • "INNES: An intelligent network penetration testing model based on deep reinforcement learning" (2023), published in Applied Intelligence
  • "Mitigate Parasitic Resistance in Resistive Crossbar-based Convolutional Neural Networks" (2020), published in ACM Journal on Emerging Technologies in Computing Systems

Miao Hu frequently collaborates with other researchers in their field. Frequent co-authors include:

  • Qiangfei Xia
  • J. Joshua Yang
  • Wenhao Song
  • Wenbo Yin
  • Ning Ge

The venues where Miao Hu publishes reflect a range of disciplines and interdisciplinary interests, including:

  • Nature
  • Science
  • Computers & Security
  • Applied Intelligence
  • Industrial Crops and Products

Best Publications

  • 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

  • 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

  • Memristor Crossbar-Based Neuromorphic Computing System: A Case Study

    Miao Hu;Hai Li;Yiran Chen;Qing Wu

  • Long short-term memory networks in memristor crossbars

    Can Li;Zhongrui Wang;Mingyi Rao;Daniel Belkin

  • 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

  • In situ training of feed-forward and recurrent convolutional memristor networks

    Zhongrui Wang;Can Li;Can Li;Peng Lin;Mingyi Rao

  • Capacitive neural network with neuro-transistors.

    Zhongrui Wang;Mingyi Rao;Jin Woo Han;Jiaming Zhang

  • Rescuing Memristor-based Neuromorphic Design with High Defects

    Chenchen Liu;Miao Hu;John Paul Strachan;Hai (Helen) Li

  • Hardware realization of BSB recall function using memristor crossbar arrays

    Miao Hu;Hai Li;Qing Wu;Garrett S. Rose

  • Memristor-based approximated computation

    Boxun Li;Yi Shan;Miao Hu;Yu Wang

  • A Compact Memristor-Based Dynamic Synapse for Spiking Neural Networks

    Miao Hu;Yiran Chen;J. Joshua Yang;Yu Wang

  • Digital-assisted noise-eliminating training for memristor crossbar-based analog neuromorphic computing engine

    Beiye Liu;Miao Hu;Hai Li;Zhi-Hong Mao

  • Memristor crossbar based hardware realization of BSB recall function

    Miao Hu;Hai Li;Qing Wu;Garrett S. Rose

  • BSB training scheme implementation on memristor-based circuit

    Miao Hu;Hai Li;Yiran Chen;Qing Wu

  • Learning Driven Computation Offloading for Asymmetrically Informed Edge Computing

    Miao Hu;Lei Zhuang;Di Wu;Yipeng Zhou

Frequent Co-Authors

John Paul Strachan
John Paul Strachan Hewlett-Packard (United States)
J. Joshua Yang
J. Joshua Yang University of Southern California
Hai Li
Hai Li Duke University
Ning Ge
Ning Ge Tsinghua University
Qing Wu
Qing Wu United States Air Force Research Laboratory
R. Stanley Williams
R. Stanley Williams Texas A&M University
Qiangfei Xia
Qiangfei Xia University of Massachusetts Amherst
Zhiyong Li
Zhiyong Li Chinese Academy of Sciences
Garrett S. Rose
Garrett S. Rose University of Tennessee at Knoxville
Qinru Qiu
Qinru Qiu Syracuse University

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