World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
5181
World Ranking
12142
National Ranking
4938

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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