World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
8403
World Ranking
12891
National Ranking
630

Michael Pfeiffer 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 Michael Pfeiffer 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: 87 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

Michael Pfeiffer 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 Michael Pfeiffer 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: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Overview

Michael Pfeiffer is affiliated with the Bosch Center for Artificial Intelligence in Germany. Their research spans multiple fields, primarily within Engineering and Computer Science, with a focus on Artificial Intelligence, Aerospace Engineering, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, and Cognitive Neuroscience.

The scientist has contributed to several main topics throughout their work, including:

  • Adversarial Robustness in Machine Learning
  • Advanced Neural Network Applications
  • Advanced SAR Imaging Techniques
  • Radar Systems and Signal Processing
  • Advanced Memory and Neural Computing
  • Neural Dynamics and Brain Function
  • Ferroelectric and Negative Capacitance Devices

Michael Pfeiffer has authored a number of recent papers, with works published in various reputable venues. Some of the notable publications include:

  • Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks, 2020, Frontiers in Neuroscience
  • Robust Anomaly Detection in Images Using Adversarial Autoencoders, 2020, Lecture Notes in Computer Science
  • Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning, 2020, arXiv (Cornell University)
  • A generative growth model for thalamocortical axonal branching in primary visual cortex, 2020, PLoS Computational Biology
  • Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing, 2022, 2022 IEEE Radar Conference (RadarConf22)

Frequent publication venues for Michael Pfeiffer's work include:

  • arXiv (Cornell University)
  • Frontiers in Neuroscience
  • Lecture Notes in Computer Science
  • 2022 IEEE Radar Conference (RadarConf22)
  • PLoS Computational Biology

The scientist often collaborates with a group of coauthors, with frequent collaborators being:

  • Kanil Patel
  • William Beluch
  • Kilian Rambach
  • Bin Yang
  • Alexander Kugele

Best Publications

  • Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing

    Peter U. Diehl;Daniel Neil;Jonathan Binas;Matthew Cook

  • Training Deep Spiking Neural Networks Using Backpropagation.

    Jun Haeng Lee;Tobi Delbruck;Michael Pfeiffer

  • Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.

    Bodo Rueckauer;Iulia-Alexandra Lungu;Yuhuang Hu;Michael Pfeiffer;Michael Pfeiffer

  • Gland segmentation in colon histology images: The GlaS challenge contest

    Korsuk Sirinukunwattana;Josien P.W. Pluim;Hao Chen;Xiaojuan Qi

  • Deep Learning With Spiking Neurons: Opportunities and Challenges.

    Michael Pfeiffer;Thomas Pfeil

  • Real-time classification and sensor fusion with a spiking deep belief network

    Peter O'Connor;Daniel Neil;Shih-Chii Liu;Tobi Delbruck

  • Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

    Daniel Neil;Michael Pfeiffer;Shih-Chii Liu

  • Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

    Bernhard Nessler;Michael Pfeiffer;Michael Pfeiffer;Lars Buesing;Wolfgang Maass

  • DVS Benchmark Datasets for Object Tracking, Action Recognition, and Object Recognition.

    Yuhuang Hu;Hongjie Liu;Michael Pfeiffer;Tobi Delbruck

  • Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

    Daniel Neil;Michael Pfeiffer;Shih-Chii Liu

  • STDP enables spiking neurons to detect hidden causes of their inputs

    Bernhard Nessler;Michael Pfeiffer;Wolfgang Maass

  • Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization

    Philipp Kainz;Philipp Kainz;Michael Pfeiffer;Martin Urschler

  • Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks

    Bodo Rueckauer;Iulia-Alexandra Lungu;Yuhuang Hu;Michael Pfeiffer

  • Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

    Evangelos Stromatias;Daniel Neil;Michael Pfeiffer;Francesco Galluppi

  • Optimal sizing of a solar thermal building installation using particle swarm optimization

    Raffaele Bornatico;Michael Pfeiffer;Michael Pfeiffer;Andreas Witzig;Lino Guzzella

  • Deep Learning-based Object Classification on Automotive Radar Spectra

    Kanil Patel;Kilian Rambach;Tristan Visentin;Daniel Rusev

  • Scalable energy-efficient, low-latency implementations of trained spiking Deep Belief Networks on SpiNNaker

    Evangelos Stromatias;Daniel Neil;Francesco Galluppi;Michael Pfeiffer

  • Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks

    Alexander Kugele;Alexander Kugele;Thomas Pfeil;Michael Pfeiffer;Elisabetta Chicca

  • Robust Anomaly Detection in Images Using Adversarial Autoencoders

    Laura Beggel;Michael Pfeiffer;Bernd Bischl

  • Real-Time Gesture Interface Based on Event-Driven Processing From Stereo Silicon Retinas

    Jun Haeng Lee;Tobi Delbruck;Michael Pfeiffer;Paul K. J. Park

  • Learning to be efficient: algorithms for training low-latency, low-compute deep spiking neural networks

    Daniel Neil;Michael Pfeiffer;Shih-Chii Liu

  • A framework for plasticity implementation on the SpiNNaker neural architecture

    Francesco Galluppi;Xavier Lagorce;Evangelos Stromatias;Michael Pfeiffer

Frequent Co-Authors

Shih-Chii Liu
Shih-Chii Liu University of Zurich
Giacomo Indiveri
Giacomo Indiveri University of Zurich
Wolfgang Maass
Wolfgang Maass Graz University of Technology
Tobi Delbruck
Tobi Delbruck ETH Zurich
Bin Yang
Bin Yang University of Stuttgart
Martin Urschler
Martin Urschler University of Auckland
Steve Furber
Steve Furber University of Manchester
Bernd Bischl
Bernd Bischl Ludwig-Maximilians-Universität München

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