World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
10395
World Ranking
9993
National Ranking
4210

Hadi Esmaeilzadeh 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 Hadi Esmaeilzadeh 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 111 publications — 12th percentile

12% of scientists in this discipline score the same or lower.

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

Hadi Esmaeilzadeh 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 Hadi Esmaeilzadeh sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

Hadi Esmaeilzadeh is affiliated with the University of California, San Diego in the United States. Their research primarily focuses on computer science, with a significant output in subfields such as artificial intelligence, computer vision and pattern recognition, electrical and electronic engineering, hardware and architecture, and computer networks and communications.

They have contributed extensively to several topics within their fields, including advanced neural network applications, parallel computing and optimization techniques, advanced memory and neural computing, ferroelectric and negative capacitance devices, adversarial robustness in machine learning, domain adaptation and few-shot learning, and machine learning and data classification.

Among their recent publications are:

  • Conscious Empathic AI in Service, 2022, Journal of Service Research
  • FastStereoNet: A Fast Neural Architecture Search for Improving the Inference of Disparity Estimation on Resource-Limited Platforms, 2021, IEEE Transactions on Systems Man and Cybernetics Systems
  • Privacy in Deep Learning: A Survey, 2020, arXiv (Cornell University)
  • ReLeQ: A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks, 2020, IEEE Micro
  • Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation, 2020, arXiv (Cornell University)

Frequent coauthors collaborating with Esmaeilzadeh include:

  • Soroush Ghodrati
  • Sean Kinzer
  • Byung Hoon Ahn
  • Rohan Mahapatra
  • Prannoy Pilligundla

Esmaeilzadeh has regularly published in venues such as arXiv (Cornell University), IEEE Micro, ACM Transactions on Design Automation of Electronic Systems, Journal of Service Research, and IEEE Transactions on Systems Man and Cybernetics Systems.

Best Publications

  • Dark silicon and the end of multicore scaling

    Hadi Esmaeilzadeh;Emily Blem;Renee St. Amant;Karthikeyan Sankaralingam

  • A reconfigurable fabric for accelerating large-scale datacenter services

    Andrew Putnam;Adrian M. Caulfield;Eric S. Chung;Derek Chiou

  • Neural acceleration for general-purpose approximate programs

    Hadi Esmaeilzadeh;Adrian Sampson;Luis Ceze;Doug Burger

  • Architecture support for disciplined approximate programming

    Hadi Esmaeilzadeh;Adrian Sampson;Luis Ceze;Doug Burger

  • Bit fusion: bit-level dynamically composable architecture for accelerating deep neural networks

    Hardik Sharma;Jongse Park;Naveen Suda;Liangzhen Lai

  • From high-level deep neural models to FPGAs

    Hardik Sharma;Jongse Park;Divya Mahajan;Emmanuel Amaro

  • Dark Silicon and the End of Multicore Scaling

    H. Esmaeilzadeh;E. Blem;R. St. Amant;K. Sankaralingam

  • AxBench: A Multiplatform Benchmark Suite for Approximate Computing

    Amir Yazdanbakhsh;Divya Mahajan;Hadi Esmaeilzadeh;Pejman Lotfi-Kamran

  • General-purpose code acceleration with limited-precision analog computation

    Renée St. Amant;Amir Yazdanbakhsh;Jongse Park;Bradley Thwaites

  • Power challenges may end the multicore era

    Hadi Esmaeilzadeh;Emily Blem;Renée St. Amant;Karthikeyan Sankaralingam

  • A Reconfigurable Fabric for Accelerating Large-Scale Datacenter Services

    Andrew Putnam;Adrian M. Caulfield;Eric S. Chung;Derek Chiou

  • TABLA: A unified template-based framework for accelerating statistical machine learning

    Divya Mahajan;Jongse Park;Emmanuel Amaro;Hardik Sharma

  • SNNAP: Approximate computing on programmable SoCs via neural acceleration

    Thierry Moreau;Mark Wyse;Jacob Nelson;Adrian Sampson

  • SnaPEA: predictive early activation for reducing computation in deep convolutional neural networks

    Vahideh Akhlaghi;Amir Yazdanbakhsh;Kambiz Samadi;Rajesh K. Gupta

  • Neural acceleration for GPU throughput processors

    Amir Yazdanbakhsh;Jongse Park;Hardik Sharma;Pejman Lotfi-Kamran

  • Looking back on the language and hardware revolutions: measured power, performance, and scaling

    Hadi Esmaeilzadeh;Ting Cao;Yang Xi;Stephen M. Blackburn

  • Planaria: Dynamic Architecture Fission for Spatial Multi-Tenant Acceleration of Deep Neural Networks

    Soroush Ghodrati;Byung Hoon Ahn;Joon Kyung Kim;Sean Kinzer

  • Privacy in Deep Learning: A Survey

    Fatemehsadat Mireshghallah;Mohammadkazem Taram;Praneeth Vepakomma;Abhishek Singh

  • GANAX: a unified MIMD-SIMD acceleration for generative adversarial networks

    Amir Yazdanbakhsh;Kambiz Samadi;Nam Sung Kim;Hadi Esmaeilzadeh

  • A network-centric hardware/algorithm co-design to accelerate distributed training of deep neural networks

    Youjie Li;Jongse Park;Mohammad Alian;Yifan Yuan

  • RFVP: Rollback-Free Value Prediction with Safe-to-Approximate Loads

    Amir Yazdanbakhsh;Gennady Pekhimenko;Bradley Thwaites;Hadi Esmaeilzadeh

Frequent Co-Authors

Doug Burger
Doug Burger Microsoft (United States)
Nam Sung Kim
Nam Sung Kim University of Illinois at Urbana-Champaign
Dean M. Tullsen
Dean M. Tullsen University of California, San Diego
Karthikeyan Sankaralingam
Karthikeyan Sankaralingam University of Wisconsin–Madison
Luis Ceze
Luis Ceze University of Washington
Abbas Rahimi
Abbas Rahimi IBM (United States)
Gennady Pekhimenko
Gennady Pekhimenko University of Toronto
Onur Mutlu
Onur Mutlu ETH Zurich
Todd C. Mowry
Todd C. Mowry Carnegie Mellon University
Scott Hauck
Scott Hauck University of Washington

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