World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
5577
World Ranking
10790
National Ranking
544

Patrick Mäder 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 Patrick Mäder 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: 158 publications — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Patrick Mäder 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 Patrick Mäder 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: 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

Patrick Mäder is affiliated with Ilmenau University of Technology in Germany. Their research spans multiple fields primarily focused on Computer Science and Engineering, with notable contributions in Artificial Intelligence and Computer Vision and Pattern Recognition. Their work also extends into Ecology, Evolution, Behavior and Systematics, as well as Information Systems and Ecological Modeling.

The scientist's research topics cover a diverse range including Species Distribution and Climate Change, Software Engineering Research, Plant and Animal Studies, Remote Sensing in Agriculture, Advanced Memory and Neural Computing, Privacy-Preserving Technologies in Data, and Neural Networks and Applications.

Among recent publications authored or co-authored by Patrick Mäder are:

  • Sulfoximines as Rising Stars in Modern Drug Discovery? Current Status and Perspective on an Emerging Functional Group in Medicinal Chemistry, 2020, Journal of Medicinal Chemistry
  • The Flora Incognita app - Interactive plant species identification, 2021, Methods in Ecology and Evolution

Additional notable recent papers in related fields, where Mäder appears as co-author or part of the research community, include:

  • Multi-view classification with convolutional neural networks, 2021, PLoS ONE
  • Pollen analysis using multispectral imaging flow cytometry and deep learning, 2020, New Phytologist
  • Species delimitation 4.0: integrative taxonomy meets artificial intelligence, 2024, Trends in Ecology & Evolution

The scientist collaborates frequently with researchers including Jana Wäldchen, Michael Rzanny, Marco Seeland, Christian Cierpka, and David Boho. These collaborations have produced numerous publications reflecting interdisciplinary approaches to both computational and biological sciences.

Patrick Mäder has published extensively in various venues, with a significant presence in arXiv (Cornell University) reflecting an openness to preprint dissemination. Other frequent publication outlets include Frontiers in Plant Science, Neurocomputing, Experiments in Fluids, and the Journal of Advanced Joining Processes.

Best Publications

  • Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review

    Jana Wäldchen;Patrick Mäder

  • Recommending plant taxa for supporting on-site species identification

    Hans Christian Wittich;Marco Seeland;Jana Wäldchen;Michael Rzanny

  • Machine learning for image based species identification

    Jana Wäldchen;Patrick Mäder

  • Software traceability: trends and future directions

    Jane Cleland-Huang;Orlena C. Z. Gotel;Jane Huffman Hayes;Patrick Mäder

  • Automated plant species identification-Trends and future directions.

    Jana Wäldchen;Michael Rzanny;Marco Seeland;Patrick Mäder

  • Traceability Fundamentals

    Unknown

  • Strategic Traceability for Safety-Critical Projects

    Patrick Mader;Paul L. Jones;Yi Zhang;Jane Cleland-Huang

  • Multi-view classification with convolutional neural networks.

    Marco Seeland;Patrick Mäder

  • Traceability in the wild: automatically augmenting incomplete trace links

    Michael Rath;Jacob Rendall;Jin L. C. Guo;Jane Cleland-Huang

  • Do developers benefit from requirements traceability when evolving and maintaining a software system

    Patrick Mäder;Alexander Egyed

  • Motivation Matters in the Traceability Trenches

    Patrick Mader;Orlena Gotel;Ilka Philippow

  • Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain

    Michael Carsten Rzanny;Marco Seeland;Jana Wäldchen;Patrick Mäder

  • A survey on usage scenarios for requirements traceability in practice

    Elke Bouillon;Patrick Mäder;Ilka Philippow

  • Getting back to basics: Promoting the use of a traceability information model in practice

    Patrick Mader;Orlena Gotel;Ilka Philippow

  • Plant species classification using flower images-A comparative study of local feature representations.

    Marco Seeland;Michael Rzanny;Nedal Alaqraa;Jana Wäldchen

  • Pollen analysis using multispectral imaging flow cytometry and deep learning.

    Susanne Dunker;Elena Motivans;Elena Motivans;Demetra Rakosy;David Boho

  • Towards automated traceability maintenance

    Patrick Mäder;Orlena Gotel

  • Mind the gap: assessing the conformance of software traceability to relevant guidelines

    Patrick Rempel;Patrick Mäder;Tobias Kuschke;Jane Cleland-Huang

  • OmniDet: Surround View Cameras Based Multi-Task Visual Perception Network for Autonomous Driving

    Varun Ravi Kumar;Senthil Yogamani;Hazem Rashed;Ganesh Sitsu

  • Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton

    Susanne Dunker;David Boho;Jana Wäldchen;Patrick Mäder

  • Design pattern recovery based on annotations

    Ghulam Rasool;Ilka Philippow;Patrick Mäder

  • SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving

    Varun Ravi Kumar;Marvin Klingner;Senthil Yogamani;Stefan Milz

  • Enabling Automated Traceability Maintenance through the Upkeep of Traceability Relations

    Patrick Mäder;Orlena Gotel;Ilka Philippow

Frequent Co-Authors

Jane Cleland-Huang
Jane Cleland-Huang University of Notre Dame
Alexander Egyed
Alexander Egyed Johannes Kepler University of Linz
Jian Lu
Jian Lu Nanjing University
Tiffany M. Knight
Tiffany M. Knight Helmholtz Centre for Environmental Research
Andrian Marcus
Andrian Marcus The University of Texas at Dallas
David Lo
David Lo Singapore Management University
Rocco Oliveto
Rocco Oliveto University of Molise
Björn Regnell
Björn Regnell Lund University
Miguel D. Mahecha
Miguel D. Mahecha Leipzig University
Ernst-Detlef Schulze
Ernst-Detlef Schulze Max Planck Institute for Biogeochemistry

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