World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
57
Citations
11974
World Ranking
3871
National Ranking
1831

Wu-chun Feng 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 Wu-chun Feng 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: 384 publications — 85th percentile

85% of scientists in this discipline score the same or lower.

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

Wu-chun Feng 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 Wu-chun Feng 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: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Overview

Wu-chun Feng is affiliated with Virginia Tech in the United States and has contributed extensively to research in computer science and medicine. Their work spans multiple interdisciplinary domains, with a focus on medical imaging, machine learning, and parallel computing.

The main fields of study associated with Wu-chun Feng include:

  • Computer Science
  • Medicine

Within these broad areas, the subfields explored comprise:

  • Radiology, Nuclear Medicine and Imaging
  • Artificial Intelligence
  • Statistical and Nonlinear Physics
  • Molecular Biology
  • Biomedical Engineering

The research topics often addressed in Wu-chun Feng's publications are:

  • Medical Imaging Techniques and Applications
  • Complex Network Analysis Techniques
  • Radiomics and Machine Learning in Medical Imaging
  • Advanced X-ray and CT Imaging
  • Advanced Clustering Algorithms Research
  • Parallel Computing and Optimization Techniques
  • Caching and Content Delivery

Wu-chun Feng has authored papers in several reputable venues, with frequent publications in:

  • arXiv (Cornell University)
  • Scientific Reports
  • IEEE Transactions on Radiation and Plasma Medical Sciences
  • IEEE Transactions on Network Science and Engineering
  • Journal of Signal Processing Systems

Notable recent papers include:

  • "Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU" (2020, Scientific Reports)
  • "Improved 2-D Chest CT Image Enhancement With Multi-Level VGG Loss" (2024, IEEE Transactions on Radiation and Plasma Medical Sciences)
  • "SamBaS: Sampling-Based Stochastic Block Partitioning" (2021, arXiv (Cornell University))
  • "SamBaS: Sampling-Based Stochastic Block Partitioning" (2024, IEEE Transactions on Network Science and Engineering)
  • "IterML: Iterative Machine Learning for Intelligent Parameter Pruning and Tuning in Graphics Processing Units" (2020, Journal of Signal Processing Systems)

Frequent collaborators with Wu-chun Feng comprise:

  • Guohua Cao
  • Frank Wanye
  • Vitaliy Gleyzer
  • Edward K. Kao
  • Ayush Chaturvedi

Best Publications

  • The Quadrics network: high-performance clustering technology

    F. Petrini;Wu-chun Feng;A. Hoisie;S. Coll

  • A Power-Aware Run-Time System for High-Performance Computing

    Chung-hsing Hsu;Wu-chun Feng

  • The design, implementation, and evaluation of mpiBLAST

    A. E. Darling;L. Carey;W. C. Feng

  • Inter-block GPU communication via fast barrier synchronization

    Shucai Xiao;Wu-chun Feng

  • The Green500 List: Encouraging Sustainable Supercomputing

    Wu-chun Feng;K.W. Cameron

  • CPU MISER: A Performance-Directed, Run-Time System for Power-Aware Clusters

    Rong Ge;Xizhou Feng;Wu-chun Feng;K.W. Cameron

  • FAST TCP: from theory to experiments

    Cheng Jin;D. Wei;S.H. Low;J. Bunn

  • On the energy efficiency of graphics processing units for scientific computing

    S. Huang;S. Xiao;W. Feng

  • MOON: MapReduce On Opportunistic eNvironments

    Heshan Lin;Xiaosong Ma;Jeremy Archuleta;Wu-chun Feng

  • The Failure of TCP in High-Performance Computational Grids

    W. Feng;P. Tinnakornsrisuphap

  • On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing

    Mayank Daga;Ashwin M. Aji;Wu-chun Feng

  • Performance characterization of a 10-Gigabit Ethernet TOE

    W. Feng;P. Balaji;C. Baron;L.N. Bhuyan

  • Green Supercomputing Comes of Age

    Wu-chun Feng;Xizhou Feng;Rong Ce

  • Making a Case for Efficient Supercomputing: It is time for the computing community to use alternative metrics for evaluating performance.

    Wu-chun Feng

  • Performance evaluation of the quadrics interconnection network

    F. Petrini;A. Hoisie;Wu-chun Feng;R. Graham

  • Energy-Efficient Cluster Computing via Accurate Workload Characterization

    S. Huang;W. Feng

  • Optimizing 10-Gigabit Ethernet for Networks of Workstations, Clusters, and Grids: A Case Study

    Wu-chun Feng;Justin (Gus) Hurwitz;Harvey Newman;Sylvain Ravot

  • Map data processing in geographic information systems

    R. Kasturi;R. Fernandez;M.L. Amlani;W.-C. Feng

  • Making a case for a Green500 list

    Sushant Sharma;Chung-Hsing Hsu;Wu-chun Feng

  • Power and Performance Characterization of Computational Kernels on the GPU

    Y. Jiao;H. Lin;P. Balaji;W. Feng

Frequent Co-Authors

Pavan Balaji
Pavan Balaji Argonne National Laboratory
Rajeev Thakur
Rajeev Thakur Argonne National Laboratory
Fabrizio Petrini
Fabrizio Petrini Intel (United States)
Apu Kapadia
Apu Kapadia Indiana University
João C. Setubal
João C. Setubal Universidade de São Paulo
Dhabaleswar K. Panda
Dhabaleswar K. Panda The Ohio State University
Bronis R. de Supinski
Bronis R. de Supinski Lawrence Livermore National Laboratory
Naren Ramakrishnan
Naren Ramakrishnan Virginia Tech
James Ahrens
James Ahrens Los Alamos National Laboratory
Jun Wang
Jun Wang University of Central Florida

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