David Aristoff's webpage

In Fall 2024 I am teaching Math 517 (real analysis).

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, and dynamics. 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.

Benchmarking for Bayesian inversion problems, with applications in materials science

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

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

Optimizing genetic algorithms, with applications to molecular dynamics

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

Extending Markov models, with applications to molecular dynamics