David Aristoff
Associate Professor
Department of Mathematics
Colorado State University

In Spring 2026 I am teaching Math 435 and Math 161.

My research focuses on numerical methods for optimization, interpolation, and numerical integration, with applications in physics, biology, and elsewhere. I am especially interested in rare events, uncertainty quantification, dynamics, and machine learning.

Some of my recent works (supported by the NSF) are listed below. A complete list of my publications is here. See also my Github page here.

Optimizing genetic algorithms in molecular dynamics

  • W.H. Ryu, J.D. Russo, M. Johnson, J. Copperman, J. Thompson, D. LeBard, R.J. Webber, G. Simpson, David Aristoff, and D.M. Zuckerman.
    Reducing weighted ensemble variance with optimal trajectory management
    (2025) arXiv:2504.21663

  • D. Aristoff, J.Copperman, G. Simpson, R.J. Webber, and D.M. Zuckerman.
    Weighted ensemble: recent mathematical developments
    (2023) J. Chem. Phys. 158, 014108

  • D. Aristoff.
    An ergodic theorem for the weighted ensemble method
    (2022) J. Appl. Probab. 59(1), 152--166

  • R.J. Webber, D. Aristoff, and G. Simpson.
    A splitting method to reduce MCMC variance
    (2022) arXiv:2011.13899

    • Automatic feature-finding kernel machine, for computing committor functions in molecular dynamics

  • D. Aristoff, M. Johnson, G. Simpson, and R.J. Webber
    The Fast Committor Machine: Interpretable Prediction With Kernels
    (2024) J. Chem. Phys. 161, 084113

    • Extending Markov models, with applications to molecular dynamics

  • S. Kania, R.J. Webber, G. Simpson, D. Aristoff, and D.M. Zuckerman.
    RiteWeight: Randomized iterative trajectory reweighting for steady-state distributions without discretization error
    (2025) arxiv:2401.05597v2

  • D. Aristoff, M. Johnson, and D. Perez.
    Arbitrarily accurate, nonparametric coarse graining with Markov renewal processes and the Mori-Zwanzig formulation
    (2023) AIP Advances 13, 095131

    • Featurizing Koopman mode decomposition, with applications to cancer cell signaling

  • D. Aristoff, J. Copperman, N. Mankovich, and A. Davies.
    Featurizing Koopman mode decomposition for robust forecasting
    (2024) J. Chem. Phys. 161, 064103

    • Benchmarking for Bayesian inversion problems, with applications in materials science

  • D. Aristoff and W. Bangerth.
    A benchmark for the Bayesian inversion of coefficients in partial differential equations
    (2023) SIAM Review 65(4), 1074--1105