This semester I am teaching MATH 340. Course details can be found on Canvas.

My research is centered on the use of Monte Carlo methods for optimization, numerical integration, and the sampling of probability distributions. Monte Carlo approaches are among the most promising for complex problems that arise in physics, biology, machine learning, and elsewhere.

I am especially interested in the role of rare events in such systems.

Some of my research is on genetic algorithms for sampling rare events. These are Monte Carlo methods based on "survival of the fittest." I am also interested in uncertainty quantification and Bayesian inference. See some of my recent publications below.

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

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

J.D. Russo, D. Aristoff, J. Copperman, G. Simpson, and D.M. Zuckerman.
Unbiased estimation of equilibrium, rates, and committors from MSM analysis
(2022) arXiv:2105.13402

D. Aristoff and W. Bangerth.
A benchmark for the Bayesian inversion of coefficients in partial differential equations
(2022) arXiv:2102.07263

This research has been supported by NSF-DMS 2111277 and NSF-DMS 1818726. Some of my other work has been supported by NSF-DMS 1818726 and NIH 2R01GM115805-05.

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