World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
31
Citations
4958
World Ranking
9667
National Ranking
319

Philipp Hennig publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Philipp Hennig sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 178 publications — 39th percentile

39% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Philipp Hennig D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Philipp Hennig sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 31 D-Index — 2nd percentile

2% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Philipp Hennig is affiliated with the University of Tübingen in Germany. Their academic contributions are primarily situated within the broad field of Computer Science, with a focus on Artificial Intelligence, Statistical and Nonlinear Physics, Statistics, Probability and Uncertainty, Computer Vision and Pattern Recognition, as well as Management Science and Operations Research.

Their research topics span several specialized areas, including:

  • Gaussian Processes and Bayesian Inference
  • Model Reduction and Neural Networks
  • Adversarial Robustness in Machine Learning
  • Probabilistic and Robust Engineering Design
  • Machine Learning and Algorithms
  • Neural Networks and Applications
  • Machine Learning and Data Classification

Philipp Hennig has published extensively, with a significant number of contributions to arXiv (Cornell University), as well as in journals such as Statistics and Computing, Journal of Computational Neuroscience, and publications by Cambridge University Press. Their book publication includes:

  • Probabilistic Numerics, published by Cambridge University Press in 2022

Some recent papers by Philipp Hennig include:

  • Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers, 2020, arXiv (Cornell University)
  • Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks, 2020, arXiv (Cornell University)
  • Laplace Redux -- Effortless Bayesian Deep Learning, 2021, arXiv (Cornell University)
  • Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers, 2022, arXiv (Cornell University)

Frequent co-authors collaborating with Philipp Hennig include:

  • Agustinus Kristiadi
  • Nicholas Krämer
  • Michael A. Osborne
  • Jonathan Schmidt
  • Frank Schneider

The visible pattern in publication venues and research areas indicates a strong engagement with both foundational theoretical work and applied methodologies in machine learning and probabilistic modeling. Contributions to arXiv demonstrate ongoing participation in open-access dissemination of research findings within the computational and statistical research communities.

Best Publications

  • Entropy search for information-efficient global optimization

    Philipp Hennig;Christian J. Schuler

  • Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

    Aaron Klein;Stefan Falkner;Simon Bartels;Philipp Hennig

  • Dense connectomic reconstruction in layer 4 of the somatosensory cortex

    Alessandro Motta;Manuel Berning;Kevin M. Boergens;Benedikt Staffler

  • Batch Bayesian Optimization via Local Penalization

    Javier Gonzalez;Zhenwen Dai;Philipp Hennig;Neil D. Lawrence

  • Probabilistic numerics and uncertainty in computations

    Philipp Hennig;Michael A. Osborne;Mark A. Girolami

  • Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences.

    Motonobu Kanagawa;Philipp Hennig;Dino Sejdinovic;Bharath K. Sriperumbudur

  • The Randomized Dependence Coefficient

    David Lopez-Paz;Philipp Hennig;Bernhard Schölkopf

  • Automatic LQR tuning based on Gaussian process global optimization

    Alonso Marco;Philipp Hennig;Jeannette Bohg;Stefan Schaal

  • Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

    Aaron Klein;Stefan Falkner;Simon Bartels;Philipp Hennig

  • Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization

    Alonso Marco;Felix Berkenkamp;Philipp Hennig;Angela P. Schoellig

  • Probabilistic Line Searches for Stochastic Optimization

    Maren Mahsereci;Philipp Hennig

  • Gaussian Process-Based Predictive Control for Periodic Error Correction

    Edgar D. Klenske;Melanie N. Zeilinger;Bernhard Scholkopf;Philipp Hennig

  • Quasi-Newton methods: a new direction

    Philipp Hennig;Martin Kiefel

  • Probabilistic ODE Solvers with Runge-Kutta Means

    Michael Schober;David K Duvenaud;Philipp Hennig

  • Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks

    Agustinus Kristiadi;Matthias Hein;Philipp Hennig

  • Active learning of linear embeddings for Gaussian processes

    Roman Garnett;Michael A. Osborne;Philipp Hennig

  • Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature

    Tom Gunter;Michael A Osborne;Roman Garnett;Philipp Hennig

  • Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients

    Lukas Balles;Philipp Hennig

  • Descending through a Crowded Valley — Benchmarking Deep Learning Optimizers

    Robin Marc Schmidt;Frank Schneider;Philipp Hennig

  • Limitations of the empirical Fisher approximation for natural gradient descent

    Frederik Kunstner;Philipp Hennig;Lukas Balles

  • Probabilistic Interpretation of Linear Solvers

    Philipp Hennig

  • A probabilistic model for the numerical solution of initial value problems

    Michael Schober;Simon Särkkä;Philipp Hennig

  • Inference of Cause and Effect with Unsupervised Inverse Regression

    Eleni Sgouritsa;Dominik Janzing;Philipp Hennig;Bernhard Schölkopf

  • Incremental Local Gaussian Regression

    Franziska Meier;Philipp Hennig;Stefan Schaal

  • Gaussian Probabilities and Expectation Propagation

    John P. Cunningham;Philipp Hennig;Simon Lacoste-Julien

  • Fast Bayesian hyperparameter optimization on large datasets

    Aaron Klein;Stefan Falkner;Simon Bartels;Philipp Hennig

  • Early Stopping without a Validation Set.

    Maren Mahsereci;Lukas Balles;Christoph Lassner;Philipp Hennig

Frequent Co-Authors

Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Stefan Schaal
Stefan Schaal Google (United States)
Matthias Hein
Matthias Hein University of Tübingen
Simo Särkkä
Simo Särkkä Aalto University
Thore Graepel
Thore Graepel University College London
David Duvenaud
David Duvenaud University of Toronto
Frank Hutter
Frank Hutter University of Freiburg
Jeannette Bohg
Jeannette Bohg Stanford University
Ralf Herbrich
Ralf Herbrich Hasso Plattner Institute

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