World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
6150
World Ranking
12035
National Ranking
4910

Carole-Jean Wu 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 Carole-Jean Wu 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: 102 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

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

Carole-Jean Wu 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 Carole-Jean Wu 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Carole-Jean Wu is affiliated with Meta Platforms, Inc. in the United States, contributing extensively to the field of computer science. Their work spans multiple subfields including artificial intelligence, computer networks and communications, electrical and electronic engineering, information systems, and computer vision and pattern recognition.

The primary research topics covered in Carole-Jean Wu's publications include green IT and sustainability, recommender systems and techniques, advanced neural network applications, privacy-preserving technologies in data, parallel computing and optimization techniques, stochastic gradient optimization techniques, and cloud computing and resource management.

Throughout their career, Carole-Jean Wu has contributed to a significant number of publications, with notable frequent venues being arXiv (Cornell University), IEEE Micro, ACM Transactions on Architecture and Code Optimization, ACM SIGEnergy Energy Informatics Review, and IEEE Transactions on Computers.

Selected recent papers authored or co-authored by Carole-Jean Wu include:

  • Sustainable AI: Environmental Implications, Challenges and Opportunities, 2021, arXiv (Cornell University)
  • MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance, 2020, IEEE Micro
  • Chasing Carbon: The Elusive Environmental Footprint of Computing, 2022, IEEE Micro
  • DataPerf: Benchmarks for Data-Centric AI Development, 2022, arXiv (Cornell University)
  • DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference, 2020, arXiv (Cornell University)

Frequent collaborators in Carole-Jean Wu's research include Bilge Acun, Gu-Yeon Wei, Udit Gupta, David Brooks, and Newsha Ardalani. These coauthors have contributed to numerous joint publications, reflecting ongoing collaborative research relationships.

Best Publications

  • Deep Learning Recommendation Model for Personalization and Recommendation Systems

    Maxim Naumov;Dheevatsa Mudigere;Hao-Jun Michael Shi;Jianyu Huang

  • Machine Learning at Facebook: Understanding Inference at the Edge

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

  • MLPerf inference benchmark

    Vijay Janapa Reddi;Christine Cheng;David Kanter;Peter Mattson

  • Sustainable AI: Environmental Implications, Challenges and Opportunities.

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

  • SHiP: signature-based hit predictor for high performance caching

    Carole-Jean Wu;Aamer Jaleel;Will Hasenplaugh;Margaret Martonosi

  • 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

  • RecNMP: accelerating personalized recommendation with near-memory processing

    Liu Ke;Udit Gupta;Benjamin Youngjae Cho;David Brooks

  • MCM-GPU: Multi-Chip-Module GPUs for Continued Performance Scalability

    Akhil Arunkumar;Evgeny Bolotin;Benjamin Cho;Ugljesa Milic

  • MLPerf Training Benchmark.

    Peter Mattson;Christine Cheng;Cody Coleman;Greg Diamos

  • ACT: designing sustainable computer systems with an architectural carbon modeling tool

    Unknown

  • DeepRecSys: a system for optimizing end-to-end at-scale neural recommendation inference

    Udit Gupta;Samuel Hsia;Vikram Saraph;Xiaodong Wang

  • MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance

    Peter Mattson;Hanlin Tang;Gu-Yeon Wei;Carole-Jean Wu

  • PACMan: prefetch-aware cache management for high performance caching

    Carole-Jean Wu;Aamer Jaleel;Margaret Martonosi;Simon C. Steely

  • MLPerf Training Benchmark

    Peter Mattson;Christine Cheng;Gregory F. Diamos;Cody Coleman

  • Carbon Explorer: A Holistic Framework for Designing Carbon Aware Datacenters

    Unknown

  • CAWA: coordinated warp scheduling and cache prioritization for critical warp acceleration of GPGPU workloads

    Shin-Ying Lee;Akhil Arunkumar;Carole-Jean Wu

  • CAWS: criticality-aware warp scheduling for GPGPU workloads

    Shin-Ying Lee;Carole-Jean Wu

  • Understanding data storage and ingestion for large-scale deep recommendation model training: industrial product

    Unknown

  • DataPerf: Benchmarks for Data-Centric AI Development

    Unknown

  • Performance, energy characterizations and architectural implications of an emerging mobile platform benchmark suite - MobileBench

    Dhinakaran Pandiyan;Shin-Ying Lee;Carole-Jean Wu

  • RecSSD: near data processing for solid state drive based recommendation inference

    Mark Wilkening;Udit Gupta;Samuel Hsia;Caroline Trippel

  • AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning

    Young Geun Kim;Carole-Jean Wu

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

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

Frequent Co-Authors

David Brooks
David Brooks Harvard University
Kim Hazelwood
Kim Hazelwood Facebook (United States)
Gu-Yeon Wei
Gu-Yeon Wei Harvard University
Hsien-Hsin S. Lee
Hsien-Hsin S. Lee Intel (United States)
Mikhail Smelyanskiy
Mikhail Smelyanskiy Nvidia (United States)
Vijay Janapa Reddi
Vijay Janapa Reddi Harvard University
Patrick E. Phelan
Patrick E. Phelan Arizona State University
Margaret Martonosi
Margaret Martonosi Princeton University
Trevor Mudge
Trevor Mudge University of Michigan–Ann Arbor
Sarma Vrudhula
Sarma Vrudhula Arizona State University

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