World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
5181
World Ranking
12140
National Ranking
4938

Stefan M. Wild 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 Stefan M. Wild 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: 161 publications — 31st percentile

31% of scientists in this discipline score the same or lower.

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

Stefan M. Wild 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 Stefan M. Wild 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Stefan M. Wild is affiliated with the Lawrence Berkeley National Laboratory in the United States. Their research primarily focuses on the field of computer science, with significant contributions in subfields such as artificial intelligence, management science and operations research, nuclear and high energy physics, computational theory and mathematics, and statistics and probability.

The scientist's main areas of work include simulation techniques and applications, Gaussian processes and Bayesian inference, stochastic gradient optimization techniques, quantum computing algorithms and architecture, advanced bandit algorithms research, nuclear physics research studies, and probabilistic and robust engineering design.

Stefan M. Wild has published extensively, with notable recent papers including:

  • Get on the BAND Wagon: a Bayesian framework for quantifying model uncertainties in nuclear dynamics (2021, Journal of Physics G Nuclear and Particle Physics)
  • A survey of nonlinear robust optimization (2020, INFOR Information Systems and Operational Research)
  • Exploiting Symmetry Reduces the Cost of Training QAOA (2021, IEEE Transactions on Quantum Engineering)
  • Towards precise and accurate calculations of neutrinoless double-beta decay (2022, Journal of Physics G Nuclear and Particle Physics)
  • Machine-learning-based inversion of nuclear responses (2021, Physical Review C)

The scientist frequently collaborates with several researchers, including Matt Menickelly, Jeffrey Larson, Matthew Plumlee, Ruslan Shaydulin, and Özge Sürer. These coauthors have worked with Wild on multiple occasions, indicating established research partnerships.

Publications are often found in venues such as arXiv (Cornell University), Journal of Physics G Nuclear and Particle Physics, Physical Review C, OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), and Optimization and Engineering. These venues reflect the interdisciplinary nature of their research spanning physics, operational research, and optimization.

Best Publications

  • Benchmarking Derivative-Free Optimization Algorithms

    Jorge J. Moré;Stefan M. Wild

  • Derivative-free optimization methods

    Jeffrey Larson;Matt Menickelly;Stefan M. Wild

  • Maximizing influence in a competitive social network: a follower's perspective

    Tim Carnes;Chandrashekhar Nagarajan;Stefan M. Wild;Anke van Zuylen

  • ORBIT: Optimization by Radial Basis Function Interpolation in Trust-Regions

    Stefan M. Wild;Rommel G. Regis;Christine A. Shoemaker

  • Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence

    Nathan Baker;Frank Alexander;Timo Bremer;Aric Hagberg

  • Improving non-negative matrix factorizations through structured initialization

    Stefan Wild;James Curry;Anne Dougherty

  • Axially deformed solution of the Skyrme-Hartree–Fock–Bogoliubov equations using the transformed harmonic oscillator basis (II) hfbtho v2.00d: A new version of the program

    M. V. Stoitsov;M. V. Stoitsov;Nicolas Schunck;Markus Kortelainen;Markus Kortelainen;Markus Kortelainen;N. Michel

  • DOE Advanced Scientific Computing Advisory Subcommittee (ASCAC) Report: Top Ten Exascale Research Challenges

    Robert Lucas;James Ang;Keren Bergman;Shekhar Borkar

  • Modeling an Augmented Lagrangian for Blackbox Constrained Optimization

    Robert B. Gramacy;Genetha A. Gray;Sébastien Le Digabel;Herbert K. H. Lee

  • Uncertainty quantification for nuclear density functional theory and information content of new measurements

    J. D. McDonnell;J. D. McDonnell;N. Schunck;D. Higdon;J. Sarich

  • Bayesian Calibration and Uncertainty Analysis for Computationally Expensive Models Using Optimization and Radial Basis Function Approximation

    Nikolay Bliznyuk;David Ruppert;Christine Shoemaker;Rommel Regis

  • DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks

    Prasanna Balaprakash;Michael Salim;Thomas Uram;Venkat Vishwanath

  • Applied Mathematics Research for Exascale Computing

    J Dongarra;J Hittinger;J Bell;L Chacon

  • Seeding Non-Negative Matrix Factorizations with the Spherical K-Means Clustering

    Stefan M. Wild

  • Global Convergence of Radial Basis Function Trust-Region Algorithms for Derivative-Free Optimization

    Stefan M. Wild;Christine A. Shoemaker

  • GLOBAL CONVERGENCE OF RADIAL BASIS FUNCTION TRUST REGION DERIVATIVE-FREE ALGORITHMS *

    Stefan M. Wild;Christine A. Shoemaker

  • Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales

    Ian Foster;Ian Foster

  • Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics

    D.R. Phillips;R.J. Furnstahl;U. Heinz;T. Maiti

  • Multi Objective Optimization of HPC Kernels for Performance, Power, and Energy

    Prasanna Balaprakash;Ananta Tiwari;Stefan M. Wild

  • Estimating Computational Noise

    Jorge J. Moré;Stefan M. Wild

  • Computational nuclear quantum many-body problem: The UNEDF project

    Scott Bogner;Aurel Bulgac;Joseph A. Carlson;Jonathan Engel

  • Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian

    Victor Picheny;Robert B. Gramacy;Stefan Wild;Sébastien Le Digabel

  • Estimating Derivatives of Noisy Simulations

    Jorge J. Moré;Stefan M. Wild

  • Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian

    Victor Picheny;Robert B. Gramacy;Stefan M. Wild;Sebastien Le Digabel

Frequent Co-Authors

Witold Nazarewicz
Witold Nazarewicz Michigan State University
Sven Leyffer
Sven Leyffer Argonne National Laboratory
Robert B. Gramacy
Robert B. Gramacy Virginia Tech
Jorge J. Moré
Jorge J. Moré Argonne National Laboratory
Robert Ross
Robert Ross Argonne National Laboratory
Paul-Gerhard Reinhard
Paul-Gerhard Reinhard University of Erlangen-Nuremberg
Christine A. Shoemaker
Christine A. Shoemaker National University of Singapore
Franck Cappello
Franck Cappello Argonne National Laboratory
Marc Snir
Marc Snir University of Illinois at Urbana-Champaign
Garth N. Wells
Garth N. Wells University of Cambridge

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