David Aristoff's webpage

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.

Recent research highlights

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
(2026) Proc. Natl. Acad. Sci. 123 (19), e2529246123 (and supplementary information here)

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
(2026) J. Chem. Phys. 164, 094110

Other selected works

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

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

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