World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
13040
World Ranking
5786
National Ranking
177

Kate Smith-Miles 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 Kate Smith-Miles 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: 209 publications — 49th percentile

49% of scientists in this discipline score the same or lower.

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

Kate Smith-Miles 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 Kate Smith-Miles 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: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Overview

Kate Smith-Miles is affiliated with the University of Melbourne in Australia. Their research primarily spans the fields of Computer Science and Engineering, with significant contributions in subfields such as Artificial Intelligence, Industrial and Manufacturing Engineering, Computational Theory and Mathematics, Civil and Structural Engineering, and Management Science and Operations Research.

The scientist's work covers a range of topics including:

  • Machine Learning and Data Classification
  • Water Systems and Optimization
  • Advanced Multi-Objective Optimization Algorithms
  • Anomaly Detection Techniques and Applications
  • Data Stream Mining Techniques
  • Urban Stormwater Management Solutions
  • Machine Learning and Algorithms

Kate Smith-Miles has published multiple papers in diverse scholarly venues, with notable frequent venues being:

  • arXiv (Cornell University)
  • Computers & Operations Research
  • INFORMS journal on computing
  • European Journal of Operational Research
  • Journal of Water Resources Planning and Management

Recent publications reflect a focus on optimization, machine learning applications, and water resources management. Selected recent papers include:

  • A transformation technique for the clustered generalized traveling salesman problem with applications to logistics (2020), European Journal of Operational Research
  • Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management (2020), Computers & Operations Research
  • Leakage Detection in Water Distribution Systems Based on Time-Frequency Convolutional Neural Network (2020), Journal of Water Resources Planning and Management
  • Burst Detection in District Metering Areas Using Deep Learning Method (2020), Journal of Water Resources Planning and Management
  • China's enhanced urban wastewater treatment increases greenhouse gas emissions and regional inequality (2022), Water Research

The scientist has collaborated frequently with several coauthors, including:

  • Mario Andrés Muñoz
  • Shuming Liu
  • Ana Carolina Lorena
  • Tim D. Fletcher
  • Nicolau Andrés-Thió

Kate Smith-Miles's body of work demonstrates interdisciplinary engagement across computer science and engineering domains, particularly focusing on the development and application of machine learning techniques to optimization problems and environmental systems.

Best Publications

  • Automatic Age Estimation Based on Facial Aging Patterns

    Xin Geng;Zhi-Hua Zhou;K. Smith-Miles

  • Characteristic-Based Clustering for Time Series Data

    Xiaozhe Wang;Kate Smith;Rob Hyndman

  • Cross-disciplinary perspectives on meta-learning for algorithm selection

    Kate A. Smith-Miles

  • Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research

    Kate A. Smith

  • On learning algorithm selection for classification

    Shawkat Ali;Kate A. Smith

  • Neural networks in business: techniques and applications for the operations researcher

    Kate A. Smith;Jatinder N.D. Gupta

  • On chaotic simulated annealing

    L. Wang;K. Smith

  • Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series

    Xiaozhe Wang;Kate Smith-Miles;Rob Hyndman

  • Measuring instance difficulty for combinatorial optimization problems

    Kate Smith-Miles;Leo Lopes

  • Static and dynamic channel assignment using neural networks

    K. Smith;M. Palaniswami

  • Web page clustering using a self-organizing map of user navigation patterns

    Kate A. Smith;Alan Ng

  • Towards objective measures of algorithm performance across instance space

    Kate Smith-Miles;Davaatseren Baatar;Brendan Wreford;Rhyd Lewis

  • Visualising forecasting algorithm performance using time series instance spaces

    Yanfei Kang;Rob J. Hyndman;Kate Smith-Miles

  • An Analysis of Customer Retention and Insurance Claim Patterns using Data Mining: A Case Study

    Kate A Smith;Robert J Willis;Malcolm Brooks

  • A meta-learning approach to automatic kernel selection for support vector machines

    Shawkat Ali;Kate Amanda Smith-Miles

  • Neural techniques for combinatorial optimization with applications

    K. Smith;M. Palaniswami;M. Krishnamoorthy

  • ODAM: An optimized distributed association rule mining algorithm

    M.Z. Ashrafi;D. Taniar;K. Smith

  • Instance spaces for machine learning classification

    Mario A. Muñoz;Laura Villanova;Davaatseren Baatar;Kate Smith-Miles

  • Parallel Fuzzy c-Means Clustering for Large Data Sets

    Terence Kwok;Kate A. Smith;Sebastián Lozano;David Taniar

  • Towards insightful algorithm selection for optimisation using meta-learning concepts

    K.A. Smith-Miles

  • Facial age estimation by learning from label distributions

    Xin Geng;Kate Smith-Miles;Zhi-Hua Zhou

Frequent Co-Authors

Rob J. Hyndman
Rob J. Hyndman Monash University
Xin Geng
Xin Geng Southeast University
Zhi-Hua Zhou
Zhi-Hua Zhou Nanjing University
Liang Wang
Liang Wang Chinese Academy of Sciences
Kerrie Mengersen
Kerrie Mengersen Queensland University of Technology
Chung-Hsing Yeh
Chung-Hsing Yeh Monash University
Richard Weber
Richard Weber University of Chile
Wee Keong Ng
Wee Keong Ng Nanyang Technological University
Sui Huang
Sui Huang University of Calgary
Paolo Falcaro
Paolo Falcaro Graz University of Technology

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