World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
62
Citations
20054
World Ranking
1414
National Ranking
582

Computer Science

D-Index
66
Citations
22770
World Ranking
2278
National Ranking
1140

David Brooks publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where David Brooks sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 230 publications — 39th percentile

39% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

David Brooks D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where David Brooks sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 62 D-Index — 80th percentile

80% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Research.com Recognitions

  • 2020 - ACM Fellow For contributions to software and hardware design for power-efficient computer architectures

Overview

David Brooks is affiliated with Harvard University in the United States and has contributed extensively to the fields of computer science and engineering. Their research spans across several subfields including electrical and electronic engineering, hardware and architecture, artificial intelligence, computer vision and pattern recognition, and computer networks and communications.

The scientist's work focuses on a variety of topics related to computing and technology. These include:

  • Parallel Computing and Optimization Techniques
  • Advanced Neural Network Applications
  • Advanced Memory and Neural Computing
  • Embedded Systems Design Techniques
  • Ferroelectric and Negative Capacitance Devices
  • Green IT and Sustainability
  • Cloud Computing and Resource Management

David Brooks has published papers in numerous venues, with frequently appearing outlets such as:

  • arXiv (Cornell University)
  • IEEE Micro
  • IEEE Journal of Solid-State Circuits
  • ACM Transactions on Architecture and Code Optimization
  • ACM SIGEnergy Energy Informatics Review

Some of their recent papers include the following:

  • Chasing Carbon: The Elusive Environmental Footprint of Computing, 2022, IEEE Micro
  • SMAUG, 2020, ACM Transactions on Architecture and Code Optimization
  • DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference, 2020, arXiv (Cornell University)
  • The Sky Is Not the Limit: A Visual Performance Model for Cyber-Physical Co-Design in Autonomous Machines, 2020, IEEE Computer Architecture Letters
  • Bridging Python to Silicon: The SODA Toolchain, 2022, IEEE Micro

Frequent co-authors who have collaborated with David Brooks include:

  • Gu-Yeon Wei
  • Carole-Jean Wu
  • Udit Gupta
  • Tianyu Jia
  • Samuel Hsia

In recognition of their work, David Brooks was named an ACM Fellow in 2020. The award citation highlighted contributions to software and hardware design for power-efficient computer architectures.

Best Publications

  • Wattch: a framework for architectural-level power analysis and optimizations

    David Brooks;Vivek Tiwari;Margaret Martonosi

  • Dynamic thermal management for high-performance microprocessors

    D. Brooks;M. Martonosi

  • System level analysis of fast, per-core DVFS using on-chip switching regulators

    Wonyoung Kim;M.S. Gupta;Gu-Yeon Wei;D. Brooks

  • Power-aware microarchitecture: design and modeling challenges for next-generation microprocessors

    D.M. Brooks;P. Bose;S.E. Schuster;H. Jacobson

  • Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

    Kim Hazelwood;Sarah Bird;David Brooks;Soumith Chintala

  • Accurate and efficient regression modeling for microarchitectural performance and power prediction

    Benjamin C. Lee;David M. Brooks

  • Minerva: enabling low-power, highly-accurate deep neural network accelerators

    Brandon Reagen;Paul Whatmough;Robert Adolf;Saketh Rama

  • Profiling a Warehouse-Scale Computer

    Svilen Kanev;Juan Pablo Darago;Kim Hazelwood;Parthasarathy Ranganathan

  • Machine Learning at Facebook: Understanding Inference at the Edge

    Carole-Jean Wu;David Brooks;Kevin Chen;Douglas Chen

  • Dynamically exploiting narrow width operands to improve processor power and performance

    D. Brooks;M. Martonosi

  • Process Variation Tolerant 3T1D-Based Cache Architectures

    Xiaoyao Liang;Ramon Canal;Gu-Yeon Wei;David Brooks

  • Thread motion: fine-grained power management for multi-core systems

    Krishna K. Rangan;Gu-Yeon Wei;David Brooks

  • Sustainable AI: Environmental Implications, Challenges and Opportunities.

    Carole-Jean Wu;Ramya Raghavendra;Udit Gupta;Bilge Acun

  • A Fully-Integrated 3-Level DC-DC Converter for Nanosecond-Scale DVFS

    Wonyoung Kim;D. Brooks;Gu-Yeon Wei

  • MachSuite: Benchmarks for accelerator design and customized architectures

    Brandon Reagen;Robert Adolf;Yakun Sophia Shao;Gu-Yeon Wei

  • Aladdin: a Pre-RTL, power-performance accelerator simulator enabling large design space exploration of customized architectures

    Yakun Sophia Shao;Brandon Reagen;Gu-Yeon Wei;David Brooks

  • A Dynamic Compilation Framework for Controlling Microprocessor Energy and Performance

    Qiang Wu;Margaret Martonosi;Douglas W. Clark;V. J. Reddi

  • Methods of inference and learning for performance modeling of parallel applications

    Benjamin C. Lee;David M. Brooks;Bronis R. de Supinski;Martin Schulz

  • An adaptive issue queue for reduced power at high performance

    Alper Buyuktosunoglu;Stanley Schuster;David Brooks;Pradip Bose

  • Chasing Carbon: The Elusive Environmental Footprint of Computing

    Udit Gupta;Young Geun Kim;Sylvia Lee;Jordan Tse

  • The Architectural Implications of Facebook's DNN-Based Personalized Recommendation

    Udit Gupta;Carole-Jean Wu;Xiaodong Wang;Maxim Naumov

  • Ares: a framework for quantifying the resilience of deep neural networks

    Brandon Reagen;Udit Gupta;Lillian Pentecost;Paul Whatmough

  • MLPerf Training Benchmark.

    Peter Mattson;Christine Cheng;Cody Coleman;Greg Diamos

Frequent Co-Authors

Gu-Yeon Wei
Gu-Yeon Wei Harvard University
Vijay Janapa Reddi
Vijay Janapa Reddi Harvard University
Pradip Bose
Pradip Bose IBM (United States)
Margaret Martonosi
Margaret Martonosi Princeton University
Kim Hazelwood
Kim Hazelwood Facebook (United States)
Carole-Jean Wu
Carole-Jean Wu Meta Platforms, Inc.
Kevin Skadron
Kevin Skadron University of Virginia
Alper Buyuktosunoglu
Alper Buyuktosunoglu IBM (United States)
Michael J. Lyons
Michael J. Lyons Boston University
David H. Albonesi
David H. Albonesi Cornell University

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