World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
5241
World Ranking
13497
National Ranking
649

Bernhard Sick 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 Bernhard Sick 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: 328 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Bernhard Sick 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 Bernhard Sick 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: 31 D-Index — 6th percentile

6% of scientists in this discipline score the same or lower.

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

Overview

Bernhard Sick is affiliated with the University of Kassel in Germany and has an extensive research portfolio primarily in computer science and engineering fields. Their work includes a strong focus on artificial intelligence and its applications across various technical domains.

The scientist's main fields of study include:

  • Computer Science
  • Engineering

Within these broad areas, Bernhard Sick has concentrated on several subfields such as:

  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition
  • Automotive Engineering
  • Biomedical Engineering

The topics most frequently addressed in their research comprise:

  • Autonomous Vehicle Technology and Safety
  • Anomaly Detection Techniques and Applications
  • Machine Learning and Algorithms
  • Machine Learning and Data Classification
  • Traffic Prediction and Management Techniques
  • Energy Load and Power Forecasting
  • Advanced Neural Network Applications

Bernhard Sick has published numerous papers, including recent work such as:

  • Object detection for automotive radar point clouds - a comparison, 2021, AI Perspectives
  • Novelty detection in continuously changing environments, 2020, Future Generation Computer Systems
  • CLeaR: An adaptive continual learning framework for regression tasks, 2021, AI Perspectives
  • Model selection, adaptation, and combination for transfer learning in wind and photovoltaic power forecasts, 2023, Energy and AI
  • Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets, 2020, IFAC-PapersOnLine

Frequent co-authors in Bernhard Sick's research include:

  • Konrad Doll
  • Maarten Bieshaar
  • Denis Huseljic
  • Stefan Zernetsch
  • Marek Herde

Their work has been published in venues such as:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Scientific Reports
  • Machine Learning
  • Preprints.org

Best Publications

  • Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks

    Andre Gensler;Janosch Henze;Bernhard Sick;Nils Raabe

  • ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH

    Bernhard Sick

  • Evolutionary optimization of radial basis function classifiers for data mining applications

    O. Buchtala;M. Klimek;B. Sick

  • Online Signature Verification With Support Vector Machines Based on LCSS Kernel Functions

    Christian Gruber;Thiemo Gruber;Sebastian Krinninger;Bernhard Sick

  • Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations

    E Fuchs;T Gruber;J Nitschke;B Sick

  • Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar

    Nicolas Scheiner;Florian Kraus;Fangyin Wei;Buu Phan

  • Engineering and Mastering Interwoven Systems

    Sven Tomforde;Jörg Hähner;Hella Seebach;Wolfgang Reif

  • Online Intrusion Alert Aggregation with Generative Data Stream Modeling

    A Hofmann;B Sick

  • On-line motif detection in time series with SwiftMotif

    Erich Fuchs;Thiemo Gruber;Jiri Nitschke;Bernhard Sick

  • Feature selection for intrusion detection: an evolutionary wrapper approach

    A. Hofmann;T. Horeis;B. Sick

  • Trajectory prediction of cyclists using a physical model and an artificial neural network

    Stefan Zernetsch;Sascha Kohnen;Michael Goldhammer;Konrad Doll

  • On the versatility of radial basis function neural networks: A case study in the field of intrusion detection

    Dominik Fisch;Alexander Hofmann;Bernhard Sick

  • Evolutionary optimization of radial basis function networks for intrusion detection

    A. Hofmann;B. Sick

  • Wave-front reconstruction with a Shack–Hartmann sensor with an iterative spline fitting method

    Sascha Groening;Bernhard Sick;Klaus Donner;Johannes Pfund

  • Quantitative Emergence -- A Refined Approach Based on Divergence Measures

    Dominik Fisch;Martin Janicke;Bernhard Sick;Christian Muller-Schloer

  • Temporal data mining using shape space representations of time series

    Erich Fuchs;Thiemo Gruber;Helmuth Pree;Bernhard Sick

  • Object detection for automotive radar point clouds – a comparison

    Nicolas Scheiner;Florian Kraus;Nils Appenrodt;Jürgen Dickmann

  • Intentions of Vulnerable Road Users—Detection and Forecasting by Means of Machine Learning

    Michael Goldhammer;Sebastian Kohler;Stefan Zernetsch;Konrad Doll

  • Transductive active learning – A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data

    Tobias Reitmaier;Adrian Calma;Bernhard Sick

  • Description of Corner Cases in Automated Driving: Goals and Challenges

    Daniel Bogdoll;Jasmin Breitenstein;Florian Heidecker;Maarten Bieshaar

  • Camera based pedestrian path prediction by means of polynomial least-squares approximation and multilayer perceptron neural networks

    Michael Goldhammer;Sebastian Kohler;Konrad Doll;Bernhard Sick

  • SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis

    Dominik Fisch;T Gruber;B Sick

  • Let us know your decision: Pool-based active training of a generative classifier with the selection strategy 4DS

    Tobias Reitmaier;Bernhard Sick

  • Signature Verification with Dynamic RBF Networks and Time Series Motifs

    Christian Gruber;Michael Coduro;Bernhard Sick

Frequent Co-Authors

Paul Lukowicz
Paul Lukowicz German Research Centre for Artificial Intelligence
Seppo J. Ovaska
Seppo J. Ovaska Aalto University
Jan Marco Leimeister
Jan Marco Leimeister University of Kassel
Albrecht Schmidt
Albrecht Schmidt Ludwig-Maximilians-Universität München
Gerd Stumme
Gerd Stumme University of Kassel
Christoph Stiller
Christoph Stiller Karlsruhe Institute of Technology
Klaus Dietmayer
Klaus Dietmayer University of Ulm
Felix Heide
Felix Heide Princeton University
Jim Torresen
Jim Torresen University of Oslo
Friedrich W. Herberg
Friedrich W. Herberg University of Kassel

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