World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
12367
World Ranking
6080
National Ranking
366

Rong Qu 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 Rong Qu 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: 292 publications — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Rong Qu 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 Rong Qu 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: 48 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

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

Overview

Rong Qu is affiliated with the University of Nottingham in the United Kingdom. Their research spans primarily the fields of Computer Science and Engineering, with a focus on several key subfields that include Artificial Intelligence, Industrial and Manufacturing Engineering, Computer Vision and Pattern Recognition, Signal Processing, and Computer Networks and Communications.

The main topics of their work cover a broad range of areas in optimization, machine learning, and data analysis. These topics include:

  • Vehicle Routing Optimization Methods
  • Metaheuristic Optimization Algorithms Research
  • Optimization and Packing Problems
  • Maritime Ports and Logistics
  • Time Series Analysis and Forecasting
  • Anomaly Detection Techniques and Applications
  • Advanced Multi-Objective Optimization Algorithms

Rong Qu has published numerous papers in a variety of academic venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Expert Systems with Applications
  • IEEE Transactions on Evolutionary Computation
  • SSRN Electronic Journal
  • Scientific Reports

Some of the recent papers authored or coauthored by Rong Qu are:

  • Anti-Inflammatory and Intestinal Microbiota Modulation Properties of Jinxiang Garlic (Allium sativum L.) Polysaccharides toward Dextran Sodium Sulfate-Induced Colitis (2020, Journal of Agricultural and Food Chemistry)
  • An Efficient Federated Distillation Learning System for Multitask Time Series Classification (2022, IEEE Transactions on Instrumentation and Measurement)
  • Deep Contrastive Representation Learning With Self-Distillation (2023, IEEE Transactions on Emerging Topics in Computational Intelligence)
  • Densely Knowledge-Aware Network for Multivariate Time Series Classification (2024, IEEE Transactions on Systems Man and Cybernetics Systems)
  • CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition (2023, IEEE Transactions on Neural Networks and Learning Systems)

Rong Qu has collaborated frequently with several researchers, including:

  • Ruibin Bai
  • Huanlai Xing
  • Zhiwen Xiao
  • Xinan Chen
  • Chunbo Chen

In addition to journal papers, Rong Qu has a book published by Springer Science+Business Media entitled Automated Design of Machine Learning and Search Algorithms (2021).

Best Publications

  • Hyper-heuristics: a survey of the state of the art

    Edmund K. Burke;Michel Gendreau;Matthew R. Hyde;Graham Kendall

  • A Survey of Deep Learning-Based Object Detection

    Licheng Jiao;Fan Zhang;Fang Liu;Shuyuan Yang

  • A Graph-Based Hyper-Heuristic for Educational Timetabling Problems

    Edmund K. Burke;Barry McCollum;Amnon Meisels;Sanja Petrovic

  • A survey of search methodologies and automated system development for examination timetabling

    R. Qu;E. K. Burke;B. Mccollum;L. T. Merlot

  • Case-based heuristic selection for timetabling problems

    Edmund K. Burke;Sanja Petrovic;Rong Qu

  • Setting the Research Agenda in Automated Timetabling: The Second International Timetabling Competition

    Barry McCollum;Andrea Schaerf;Ben Paechter;Paul McMullan

  • 2015 IEEE Symposium Series on Computational Intelligence

    Honorary Chairs;Jacek Zurada;Andries Engelbrecht;Mengjie Zhang

  • A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems

    Edmund K. Burke;Jingpeng Li;Rong Qu

  • A hybrid heuristic ordering and variable neighbourhood search for the nurse rostering problem

    Edmund K. Burke;Timothy Curtois;Gerhard F. Post;Rong Qu

  • Personnel scheduling: Models and complexity

    Peter Brucker;Rong Qu;Edmund K. Burke

  • Workforce scheduling and routing problems: literature survey and computational study

    J. Arturo Castillo-Salazar;Dario Landa-Silva;Rong Qu

  • Hybrid variable neighbourhood approaches to university exam timetabling

    Edmund Burke;Adam J Eckersley;Barry McCollum;Sanja Petrovic

  • A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization

    Khin Lwin;Rong Qu;Graham Kendall

  • Mean-VaR portfolio optimization: A nonparametric approach

    Khin T. Lwin;Rong Qu;Bart L. MacCarthy

  • Hybridizations within a graph-based hyper-heuristic framework for university timetabling problems

    Rong Qu;Edmund K. Burke

  • Hyper-Heuristics: Theory and Applications

    Nelishia Pillay;Rong Qu

  • A scatter search methodology for the nurse rostering problem

    E K Burke;T Curtois;R Qu;G Vanden Berghe

  • A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems

    Nasser R. Sabar;Masri Ayob;Graham Kendall;Rong Qu

  • A graph coloring constructive hyper-heuristic for examination timetabling problems

    Nasser R. Sabar;Masri Ayob;Rong Qu;Graham Kendall

  • Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems

    Nasser R. Sabar;Masri Ayob;Graham Kendall;Rong Qu

  • A shift sequence based approach for nurse scheduling and a new benchmark dataset

    Peter Brucker;Edmund K. Burke;Tim Curtois;Rong Qu

  • A honey-bee mating optimization algorithm for educational timetabling problems

    Nasser R. Sabar;Masri Ayob;Graham Kendall;Rong Qu

  • Analyzing the landscape of a graph based hyper-heuristic for timetabling problems

    Gabriela Ochoa;Rong Qu;Edmund K. Burke

  • Discrete Optimization A graph-based hyper-heuristic for educational timetabling problems

    Edmund K. Burke;Barry McCollum;Amnon Meisels;Sanja Petrovic

Frequent Co-Authors

Edmund K. Burke
Edmund K. Burke Bangor University
Graham Kendall
Graham Kendall MILA University
Sanja Petrovic
Sanja Petrovic University of Nottingham
Bart L. MacCarthy
Bart L. MacCarthy University of Nottingham
Uwe Aickelin
Uwe Aickelin University of Melbourne
Salwani Abdullah
Salwani Abdullah National University of Malaysia
Peter Brucker
Peter Brucker Osnabrück University
Licheng Jiao
Licheng Jiao Xidian University
Tianrui Li
Tianrui Li Southwest Jiaotong University

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