World's Best Scientists 2026 revealed!
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Mathematics
USA
2026

D-Index & Metrics

Computer Science

D-Index
68
Citations
31172
World Ranking
2031
National Ranking
1027

Mathematics

D-Index
75
Citations
33760
World Ranking
198
National Ranking
116

Research.com Recognitions

  • 2026 - Research.com Mathematics in United States Leader Award
  • 2025 - Research.com Mathematics in United States Leader Award
  • 2013 - SIAM/ACM Prize in Computational Science and Engineering For her pioneering research in methods for the computational solution of differential-algebraic equations, their incorporation into widely distributed software and scientific applications, and her significant accomplishments in pioneering computational science and engineering education.
  • 2011 - ACM Fellow For contributions to computational science.
  • 2009 - SIAM Fellow For contributions to numerical ordinary differential equations and differential-algebraic equations and computational science.
  • 2007 - Fellow of the American Society of Mechanical Engineers
  • 2005 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2004 - Member of the National Academy of Engineering For advances in the numerical solution of differential/algebraic equations and their incorporation into widely distributed software.

Overview

Linda R. Petzold is affiliated with the University of California, Santa Barbara in the United States. Their primary field of study is Computer Science, with a focus on several subfields including Artificial Intelligence, Cognitive Neuroscience, Cellular and Molecular Neuroscience, Molecular Biology, and Statistical and Nonlinear Physics.

The main topics covered in their research are:

  • Topic Modeling
  • Neural dynamics and brain function
  • Neuroscience and Neural Engineering
  • Model Reduction and Neural Networks
  • Neural Networks and Applications
  • Natural Language Processing Techniques
  • Machine Learning in Healthcare

Frequent venues for their publications include:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Chaos An Interdisciplinary Journal of Nonlinear Science
  • PLoS ONE
  • PLoS Computational Biology

Linda R. Petzold has contributed to notable papers such as:

  • "Functional neuronal circuitry and oscillatory dynamics in human brain organoids," 2022, Nature Communications
  • "Experimentally Validated Reconstruction and Analysis of a Genome-Scale Metabolic Model of an Anaerobic Neocallimastigomycota Fungus," 2021, mSystems
  • "Epigenetic biotypes of post-traumatic stress disorder in war-zone exposed veteran and active duty males," 2020, Molecular Psychiatry
  • "An empirical study on the robustness of the segment anything model (SAM)," 2024, Pattern Recognition
  • "Interpretable polynomial neural ordinary differential equations," 2023, Chaos An Interdisciplinary Journal of Nonlinear Science

Co-authorship has frequently involved collaborations with:

  • Paul K. Hansma
  • Xianjun Yang
  • Colby Fronk
  • Kenneth S. Kosik
  • Rachael A. Callcut

Their work has been recognized with several awards, including:

  • SIAM/ACM Prize in Computational Science and Engineering (2013), acknowledging research in computational methods for differential-algebraic equations and education advancements
  • ACM Fellow (2011) for contributions to computational science
  • SIAM Fellow (2009) for work in numerical ordinary differential equations, differential-algebraic equations, and computational science
  • Fellow of the American Society of Mechanical Engineers (2007)
  • Fellow of the American Association for the Advancement of Science (AAAS) (2005)
  • Member of the National Academy of Engineering (2004) for advances in numerical solutions of differential/algebraic equations and software incorporation

Best Publications

  • Numerical solution of initial-value problems in differential-algebraic equations

    Kathryn Eleda Brenan;S. L. Campbell;Linda Ruth Petzold

  • Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations

    Uri M. Ascher;Linda R. Petzold

  • Description of DASSL: a differential/algebraic system solver

    L.R. Petzold

  • Automatic Selection of Methods for Solving Stiff and Nonstiff Systems of Ordinary Differential Equations

    Linda Petzold

  • Efficient step size selection for the tau-leaping simulation method

    Yang Cao;Daniel T. Gillespie;Linda R. Petzold

  • Differential/Algebraic Equations are not ODE's

    Linda Petzold

  • ODE METHODS FOR THE SOLUTION OF DIFFERENTIAL/ALGEBRAIC SYSTEMS

    C. W. Gear;L. R. Petzold

  • The Slow-Scale Stochastic Simulation Algorithm

    Yang Cao;Daniel T. Gillespie;Linda R. Petzold

  • Using Krylov methods in the solution of large-scale differential-algebraic systems

    Peter N. Brown;Alan C. Hindmarsh;Linda R. Petzold

  • Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

    Mark Alber;Adrian Buganza Tepole;William R. Cannon;Suvranu De

  • A New Look at Proper Orthogonal Decomposition

    Muruhan Rathinam;Linda R. Petzold

  • Stiffness in stochastic chemically reacting systems: The implicit tau-leaping method

    Muruhan Rathinam;Linda R. Petzold;Yang Cao;Daniel T. Gillespie

  • Efficient formulation of the stochastic simulation algorithm for chemically reacting systems

    Yang Cao;Hong Li;Linda Petzold

  • Improved leap-size selection for accelerated stochastic simulation

    Daniel T. Gillespie;Linda R. Petzold

  • Adjoint Sensitivity Analysis for Differential-Algebraic Equations: The Adjoint DAE System and Its Numerical Solution

    Yang Cao;Shengtai Li;Linda Petzold;Radu Serban

  • Perspective: Stochastic algorithms for chemical kinetics

    Daniel T. Gillespie;Andreas Hellander;Linda R. Petzold

  • Numerical methods and software for sensitivity analysis of differential-algebraic systems

    Timothy Maly;Linda R. Petzold

  • Avoiding negative populations in explicit Poisson tau-leaping.

    Yang Cao;Daniel T. Gillespie;Linda R. Petzold

  • Multiscale modeling meets machine learning: What can we learn?

    Grace C Y Peng;Mark Alber;Adrian Buganza Tepole;William R Cannon

  • Adaptive explicit-implicit tau-leaping method with automatic tau selection

    Yang Cao;Daniel T. Gillespie;Linda R. Petzold

  • Stabilization of Constrained Mechanical Systems with DAEs and Invariant Manifolds

    Uri M. Ascher;Hongsheng Chin;Linda R. Petzold;Sebastian Reich

  • Differential-algebraic equations

    Stephen L. Campbell;Vu Hoang Linh;Linda R. Petzold

Frequent Co-Authors

Francis J. Doyle
Francis J. Doyle Brown University
Uri M. Ascher
Uri M. Ascher University of British Columbia
Stephen L. Campbell
Stephen L. Campbell North Carolina State University
Mitchell J. Cohen
Mitchell J. Cohen University of Colorado Denver
Richard C. Alkire
Richard C. Alkire University of Illinois at Urbana-Champaign
Kenneth S. Kosik
Kenneth S. Kosik University of California, Santa Barbara
Igor Mezic
Igor Mezic University of California, Santa Barbara
Erik D. Herzog
Erik D. Herzog Washington University in St. Louis
Tresa M. Pollock
Tresa M. Pollock University of California, Santa Barbara

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