Jonathan Weare's research represents an effort to make use of tools from probability theory, the theory of partial differential equations, and numerical analysis to analyze interesting physical phenomena and to design, analyze, and apply new computational techniques to study challenging problems arising in the physical sciences and engineering. His current areas of interest include applications to weather prediction and problems in computational statistical mechanics.
Weare's specific areas of interest include importance sampling and Markov chain Monte Carlo methods, rare events and rare event simulation, nonlinear filtering and weather prediction, crystal surface morphology, computational statistical mechanics, stochastic approximation, multiscale methods and dimensional reduction, and numerical solution of stochastic and deterministic ordinary differential equations and partial differential equations.
Weare received a PhD in mathematics from the University of California, Berkeley, in 2007. He graduated with a BA in mathematics and economics (with honors) from the University of California, Berkeley, in 2001.