World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
8390
World Ranking
10536
National Ranking
4415

Tim Oates 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 Tim Oates 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 235 publications — 58th percentile

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

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

Tim Oates 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 Tim Oates sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Tim Oates is affiliated with the University of Maryland, Baltimore County in the United States. Their research primarily spans the field of Engineering, with a focus on several subfields including Mechanics of Materials, Mechanical Engineering, Control and Systems Engineering, Electrical and Electronic Engineering, and Artificial Intelligence.

Their scholarly work shows a concentration on topics related to mineral and mining engineering as well as advanced computational methods. Key topics of their research include:

  • Mineral Processing and Grinding
  • Mining Techniques and Economics
  • Rock Mechanics and Modeling
  • Geotechnical and Geomechanical Engineering
  • Graph theory and CDMA systems
  • Coding theory and cryptography

Tim Oates has published multiple articles in notable venues. Frequent publication outlets include:

  • Journal of the Southern African Institute of Mining and Metallurgy
  • arXiv (Cornell University)

The most recent papers by Tim Oates are:

  • "Stemming and best practice in the mining industry: A literature review," 2021, Journal of the Southern African Institute of Mining and Metallurgy
  • "A study of UG2 pillar strength using a new pillar database," 2023, Journal of the Southern African Institute of Mining and Metallurgy
  • "A Walsh Hadamard Derived Linear Vector Symbolic Architecture," 2024, arXiv (Cornell University)

Throughout their work, Tim Oates has collaborated with several researchers, including:

  • W. Spiteri
  • D.F. Malan
  • Mohammad Mahmudul Alam
  • Alexander Oberle
  • Edward Raff

The breadth of Tim Oates's research reflects a multidisciplinary approach, combining engineering principles with computational and theoretical methods. Their contributions cover the study of rock and mineral behavior in mining contexts, along with explorations in coding theory and graph-based communication systems.

Best Publications

  • Time series classification from scratch with deep neural networks: A strong baseline

    Zhiguang Wang;Weizhong Yan;Tim Oates

  • Imaging time-series to improve classification and imputation

    Zhiguang Wang;Tim Oates

  • Efficient progressive sampling

    Foster Provost;David Jensen;Tim Oates

  • Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks

    Zhiguang Wang;Tim Oates

  • The Effects of Training Set Size on Decision Tree Complexity

    Tim Oates;David Jensen

  • Detecting spam blogs: a machine learning approach

    Pranam Kolari;Akshay Java;Tim Finin;Tim Oates

  • Modeling the Spread of Influence on the Blogosphere

    Akshay Java;Pranam Kolari;Tim Finin;Tim Oates

  • Using dynamic time warping to bootstrap HMM-based clustering of time series

    Tim Oates;Laura Firoiu;Paul R. Cohen

  • Cooperative information-gathering: a distributed problem-solving approach

    Tim Oates;M. V. Nagendra Prasad;Victor R. Lesser

  • Identifying distinctive subsequences in multivariate time series by clustering

    Tim Oates

  • Searching for Structure in Multiple Streams of Data.

    Tim Oates;Paul R. Cohen

  • A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments

    Tim Oates;Matthew D. Schmill;Paul R. Cohen

  • A Review of Recent Research in Metareasoning and Metalearning

    Michael L. Anderson;Tim Oates

  • A Flexible Multichannel EEG Feature Extractor and Classifier for Seizure Detection

    Adam Page;Chris Sagedy;Emily Smith;Nasrin Attaran

  • PERUSE: An unsupervised algorithm for finding recurring patterns in time series

    T. Oates

  • Large datasets lead to overly complex models: an explanation and a solution

    Tim Oates;David Jensen

  • Visualizing Variable-Length Time Series Motifs.

    Yuan Li;Jessica Lin;Tim Oates

  • Hierarchical Bayesian Models for Latent Attribute Detection in Social Media.

    Delip Rao;Michael J. Paul;Clayton Fink;David Yarowsky

  • SensorNet: A Scalable and Low-Power Deep Convolutional Neural Network for Multimodal Data Classification

    Ali Jafari;Ashwinkumar Ganesan;Chetan Sai Kumar Thalisetty;Varun Sivasubramanian

  • Neo: learning conceptual knowledge by sensorimotor interaction with an environment

    Paul R. Cohen;Marc S. Atkin;Tim Oates;Carole R. Beal

  • The metacognitive loop I: Enhancing reinforcement learning with metacognitive monitoring and control for improved perturbation tolerance

    Michael L. Anderson;Tim Oates;Waiyian Chong;Donald Perlis

Frequent Co-Authors

Paul R. Cohen
Paul R. Cohen University of Pittsburgh
Tim Finin
Tim Finin University of Maryland, Baltimore County
David Jensen
David Jensen University of Massachusetts Amherst
Anupam Joshi
Anupam Joshi University of Maryland, Baltimore County
Douglas W. Oard
Douglas W. Oard University of Maryland, College Park
Veselin Stoyanov
Veselin Stoyanov Facebook (United States)
Yelena Yesha
Yelena Yesha University of Miami
Victor Lesser
Victor Lesser University of Massachusetts Amherst
Mohamed Younis
Mohamed Younis University of Maryland, Baltimore County

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