World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
5226
World Ranking
11703
National Ranking
4793

Maya Gokhale 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 Maya Gokhale 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: 137 publications — 21st percentile

21% of scientists in this discipline score the same or lower.

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

Maya Gokhale 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 Maya Gokhale 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Maya Gokhale is affiliated with the Lawrence Livermore National Laboratory in the United States. Their research contributions primarily lie within the field of Computer Science, with a focus on several subfields including Computer Networks and Communications, Hardware and Architecture, Information Systems, Artificial Intelligence, and Electrical and Electronic Engineering.

The main topics covered in their work span across Advanced Data Storage Technologies, Parallel Computing and Optimization Techniques, Cloud Computing and Resource Management, Distributed and Parallel Computing Systems, Scientific Computing and Data Management, Advanced Neural Network Applications, and Seismology and Earthquake Studies.

Frequent publication venues for Maya Gokhale include:

  • Proceedings of the IEEE
  • arXiv (Cornell University)
  • IEEE Micro
  • The International Journal of High Performance Computing Applications
  • Hematological Oncology

Among recent papers authored or coauthored by Maya Gokhale are the following:

  • Accelerators for Classical Molecular Dynamics Simulations of Biomolecules, 2022, Journal of Chemical Theory and Computation
  • Enabling Scalable and Extensible Memory-Mapped Datastores in Userspace, 2021, IEEE Transactions on Parallel and Distributed Systems
  • Metall: A persistent memory allocator for data-centric analytics, 2022, Parallel Computing
  • Combining Emulation and Simulation to Evaluate a Near Memory Key/Value Lookup Accelerator, 2021, arXiv (Cornell University)
  • Semi-supervised on-device neural network adaptation for remote and portable laser-induced breakdown spectroscopy, 2021, arXiv (Cornell University)

Collaborative work is a notable aspect of Maya Gokhale's career, with frequent coauthors including:

  • John Baillieul
  • Gert Cauwenberghs
  • Jocelyn Chanussot
  • Hsiao-Hwa Chen
  • Jack Dongarra

The scope of Gokhale's research integrates developments in high performance computing and data storage with advances in distributed and parallel systems, reflecting a broad engagement with current challenges and technical innovations in computing infrastructure and applications.

Best Publications

  • Processing in Memory: The Terasys Massively Parallel PIM Array

    Unknown

  • Building and using a highly parallel programmable logic array

    M. Gokhale;W. Holmes;A. Kopser;S. Lucas

  • Stream-oriented FPGA computing in the Streams-C high level language

    Unknown

  • Scalable metagenomic taxonomy classification using a reference genome database

    Sasha K. Ames;David A. Hysom;Shea N. Gardner;G. Scott Lloyd

  • Multithreaded Asynchronous Graph Traversal for In-Memory and Semi-External Memory

    Roger Pearce;Roger Pearce;Maya Gokhale;Nancy M. Amato

  • NAPA C: compiling for a hybrid RISC/FPGA architecture

    Unknown

  • Accelerating a Random Forest Classifier: Multi-Core, GP-GPU, or FPGA?

    Brian Van Essen;Chris Macaraeg;Maya Gokhale;Ryan Prenger

  • System evaluation of the Intel optane byte-addressable NVM

    Ivy B. Peng;Maya B. Gokhale;Eric W. Green

  • Granidt: Towards Gigabit Rate Network Intrusion Detection Technology

    Unknown

  • Evaluation of the streams-C C-to-FPGA compiler: an applications perspective

    Unknown

  • Trident: From High-Level Language to Hardware Circuitry

    J.L. Tripp;M.B. Gokhale;K.D. Peterson

  • Dynamic reconfiguration for management of radiation-induced faults in FPGAs

    Maya Gokhale;Paul Graham;Michael Wirthlin;D. Eric Johnson

  • Faster parallel traversal of scale free graphs at extreme scale with vertex delegates

    Roger Pearce;Maya Gokhale;Nancy M. Amato

  • Hardware/Software Approach to Molecular Dynamics on Reconfigurable Computers

    Ronald Scrofano;Maya Gokhale;Frans Trouw;Viktor Prasanna

  • Scaling Techniques for Massive Scale-Free Graphs in Distributed (External) Memory

    Roger Pearce;Maya Gokhale;Nancy M. Amato

  • Minerva: Accelerating Data Analysis in Next-Generation SSDs

    Arup De;Maya Gokhale;Rajesh Gupta;Steven Swanson

  • Extreme Heterogeneity 2018 - Productive Computational Science in the Era of Extreme Heterogeneity: Report for DOE ASCR Workshop on Extreme Heterogeneity

    Jeffrey S Vetter;Ron Brightwell;Maya Gokhale;Pat McCormick

  • Hardware Technologies for High-Performance Data-Intensive Computing

    M. Gokhale;J. Cohen;A. Yoo;W.M. Miller

  • Real-Time Classification of Multimedia Traffic Using FPGA

    Weirong Jiang;Maya Gokhale

  • DI-MMAP--a scalable memory-map runtime for out-of-core data-intensive applications

    Brian Essen;Henry Hsieh;Sasha Ames;Roger Pearce

  • On the Role of NVRAM in Data-intensive Architectures: An Evaluation

    Brian Van Essen;Roger Pearce;Sasha Ames;Maya Gokhale

  • Reliability Analysis of Large Circuits Using Scalable Techniques and Tools

    D. Bhaduri;S.K. Shukla;P.S. Graham;M.B. Gokhale

  • Accelerating Molecular Dynamics Simulations with Reconfigurable Computers

    R. Scrofano;M.B. Gokhale;F. Trouw;V.K. Prasanna

  • Experience with a Hybrid Processor: K-Means Clustering

    Maya Gokhale;Jan Frigo;Kevin Mccabe;James Theiler

  • Near memory data structure rearrangement

    Maya Gokhale;Scott Lloyd;Chris Hajas

Frequent Co-Authors

Nancy M. Amato
Nancy M. Amato University of Illinois at Urbana-Champaign
Sriram Krishnamoorthy
Sriram Krishnamoorthy University of California, Santa Barbara
John Shalf
John Shalf Lawrence Berkeley National Laboratory
Ron Brightwell
Ron Brightwell Sandia National Laboratories
Robert Ross
Robert Ross Argonne National Laboratory
Jeffrey S. Vetter
Jeffrey S. Vetter Oak Ridge National Laboratory
P. Sadayappan
P. Sadayappan University of Utah
Bronis R. de Supinski
Bronis R. de Supinski Lawrence Livermore National Laboratory
Keren Bergman
Keren Bergman Columbia University

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