World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
18540
World Ranking
4204
National Ranking
1984

Sebastian Nowozin 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 Sebastian Nowozin 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: 146 publications — 25th percentile

25% of scientists in this discipline score the same or lower.

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

Sebastian Nowozin 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 Sebastian Nowozin 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: 55 D-Index — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Overview

Sebastian Nowozin is affiliated with Microsoft in the United States, contributing primarily to the field of Computer Science. Their research spans a range of subfields, including Artificial Intelligence, Computer Vision and Pattern Recognition, Cancer Research, Radiology, Nuclear Medicine and Imaging, and Materials Chemistry.

Their work frequently addresses key topics such as Domain Adaptation and Few-Shot Learning, Gaussian Processes and Bayesian Inference, Generative Adversarial Networks and Image Synthesis, Machine Learning and Algorithms, Adversarial Robustness in Machine Learning, Multimodal Machine Learning Applications, and cancer-related molecular mechanisms research.

Frequent publication venues for their research include:

  • arXiv (Cornell University)
  • Apollo (University of Cambridge)
  • ACM Transactions on Storage
  • Conference on Lasers and Electro-Optics

Among recent published papers, the following stand out with details on year and venue:

  • Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes, 2020, Apollo (University of Cambridge)
  • Hydra: Preserving Ensemble Diversity for Model Distillation, 2020, arXiv (Cornell University)
  • How Good is the Bayes Posterior in Deep Neural Networks Really?, 2020, arXiv (Cornell University)
  • TaskNorm: Rethinking Batch Normalization for Meta-Learning, 2020, arXiv (Cornell University)
  • The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks, 2020, arXiv (Cornell University)

The scientist has collaborated frequently with several coauthors, including:

  • Kevin A. Roth
  • John Bronskill
  • Richard E. Turner
  • Lorenzo Noci
  • Gregor Bachmann

Their publication record comprises over 40 works in Computer Science, with a concentration of 30 in Artificial Intelligence. The spectrum of their research highlights a mixture of theoretical foundations and applied machine learning methodologies, particularly focused on adaptive and Bayesian approaches to neural networks and meta-learning techniques.

Best Publications

  • Occupancy Networks: Learning 3D Reconstruction in Function Space

    Lars Mescheder;Michael Oechsle;Michael Niemeyer;Sebastian Nowozin

  • f -GAN: training generative neural samplers using variational divergence minimization

    Sebastian Nowozin;Botond Cseke;Ryota Tomioka

  • On feature combination for multiclass object classification

    Peter Gehler;Sebastian Nowozin

  • Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift

    Yaniv Ovadia;Emily Fertig;Jie Ren;Zachary Nado

  • Optimization for Machine Learning

    Suvrit Sra;Sebastian Nowozin;Stephen J. Wright

  • Which Training Methods for GANs do actually Converge

    Lars M. Mescheder;Andreas Geiger;Sebastian Nowozin

  • PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

    Yang Song;Taesup Kim;Sebastian Nowozin;Stefano Ermon

  • DSAC — Differentiable RANSAC for Camera Localization

    Eric Brachmann;Alexander Krull;Sebastian Nowozin;Jamie Shotton

  • Instructing people for training gestural interactive systems

    Simon Fothergill;Helena Mentis;Pushmeet Kohli;Sebastian Nowozin

  • Oblivious multi-party machine learning on trusted processors

    Olga Ohrimenko;Felix Schuster;Cédric Fournet;Aastha Mehta

  • Adversarial variational bayes: unifying Variational Autoencoders and Generative Adversarial Networks

    Lars Mescheder;Sebastian Nowozin;Andreas Geiger

  • DeepCoder: Learning to Write Programs

    Matej Balog;Alexander L. Gaunt;Marc Brockschmidt;Sebastian Nowozin

  • Structured Learning and Prediction in Computer Vision

    Sebastian Nowozin;Christoph H. Lampert

  • Stabilizing Training of Generative Adversarial Networks through Regularization

    Kevin Roth;Aurelien Lucchi;Sebastian Nowozin;Thomas Hofmann

  • The numerics of GANs

    Lars Mescheder;Sebastian Nowozin;Andreas Geiger

  • A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems

    Jorg H. Kappes;Bjoern Andres;Fred A. Hamprecht;Christoph Schnorr

  • Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

    Diane Bouchacourt;Ryota Tomioka;Sebastian Nowozin

  • Discriminative Subsequence Mining for Action Classification

    S. Nowozin;G. Bakir;K. Tsuda

  • A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems

    Jörg H. Kappes;Bjoern Andres;Fred A. Hamprecht;Christoph Schnörr

  • Efficient Nonlinear Markov Models for Human Motion

    Andreas M. Lehrmann;Peter V. Gehler;Sebastian Nowozin

  • How Good is the Bayes Posterior in Deep Neural Networks Really

    Florian Wenzel;Kevin Roth;Bastiaan Veeling;Jakub Swiatkowski

Frequent Co-Authors

Christoph H. Lampert
Christoph H. Lampert Institute of Science and Technology Austria
Carsten Rother
Carsten Rother Heidelberg University
Richard E. Turner
Richard E. Turner University of Cambridge
Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Andreas Geiger
Andreas Geiger University of Tübingen
Jamie Shotton
Jamie Shotton Microsoft (United States)
José Miguel Hernández-Lobato
José Miguel Hernández-Lobato University of Cambridge
Jasper Snoek
Jasper Snoek Google (United States)
Koji Tsuda
Koji Tsuda University of Tokyo

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