Stochastic numerical algorithms, multiscale modeling and highdimensional data analytics
(July 18 — 22, 2016)
This workshop is concerned with sampling challenges, modeling and simulation for datarich applications in high dimensions. It brings together mathematicians, statisticians and computational scientists to explore the interplay between computational applied mathematics and data science. On the agenda will be novel developments in the study of complex phenomena based on dataanalytic techniques, such as efficient calculation of ergodic (long term) averages and statistical inference under a wide range of geometric, physical and analytical constraints.
In applied mathematics and computational science, in particular in molecular modeling, image analysis and geosciences, among others, many objects of interest are highdimensional and stochastic, and a wide variety of techniques have been developed for sampling and approximating the quantities of interest. Similar issues arise in the area of data science and statistical modeling, where learning problems in the presence of highdimensional data require efficient computational algorithms for sampling and approximation.
The workshop will focus on recent advances in the design of rigorous discretedynamics based sampling approaches, algorithms development for largescale data analysis and stochastic dynamical systems, scalable and rigorous numerical methods for stochastic differential equations and sampling from highdimensional distributions, and exploitation of lowdimensional structures in highdimensional data and stochastic dynamical systems for model reduction and efficient MonteCarlo schemes. The meeting will foster the interchange and deployment of the latest methodologies for sampling and approximation.
Organizing Committee
 Mark Girolami
(Warwick University)  Susan Holmes
(Stanford University)  Benedict Leimkuhler
(University of Edinburgh)  Mauro Maggioni
(Duke University)

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